Biomass burning processes emit substantial amounts of compounds into the atmosphere, such as phenolic compounds and furans. These compounds have been recognized as potential precursors for secondary organic aerosols (SOA). Here, the kinetics of phenolic compounds and furans with hydroxyl radical (OH) were characterized in the gas phase and aqueous phase, and corresponding SOA formation mechanisms were determined. Phenolic compound with high water solubility can be involved in aqueous-phase reactions. Bimolecular aqueous-phase reaction rate constants of phenolic compounds with OH range from (3.8 ± 0.7) × 10⁹ to (1.1 ± 0.3) × 10¹⁰ M⁻¹ s⁻¹. Aqueous-phase OH oxidation of phenolic compounds can form high-molecular-weight compounds, which are important SOA components. Hydroxylation of benzene ring occurs through OH addition, which could increase SOA oxidation degree. Furans are mainly oxidized by gas-phase OH to form SOA, and oxidation processes are influenced by different factors. Specifically, we observed a positive relationship between the furan SOA yields and NO_x concentration, which is attributed to the production of low-volatility hydroxyl nitrates and dihydroxyl dinitrates. Increasing RH results in high OH concentration and enhances carbonyl-rich products condensation, thereby promoting furan SOA formation. In addition, changing SO₂ level in the range of 0–80 ppb cause furan SOA yield to increase by 3.8%, while the presence of ∼50 ppb NH₃ increased furan SOA yield by 3.3%. SO₂ can lead to three C5-organosulfate formation, which may be precursors for the atmospheric C5-organosulfate. NH₃ has a substantial promotion effect on the formation of light-absorbing nitrogen-containing products, which may be important brown carbon constituents. Our work provided mechanism insights about SOA formation from biomass burning-derived compounds, which is helpful for refining the representation of biomass burning-related SOA formation in models.

Photochemical air pollution is a pressing environmental problem in the world’s urban and industrialized regions, including Hong Kong. Ozone and fine particulate are major air pollutants. In 2018, the Hong Kong Research Grants Council funded a comprehensive research project on the sources, atmospheric processes, and control strategy of photochemical pollution. This talk will give an overview of the project’s scope and key findings in the past 5 years. The major scientific achievements made thus far include the discoveries of the important roles of halogen atoms in air quality of polluted regions, the new source/production pathway of organic particulate matter from urban and biomass-burning emissions, and the complex responses of secondary air pollutants to emission controls. Based on the findings, the project has recommended additional measures to mitigate winter haze in north China and photochemical smog in south China.

Biomass burning (BB) and biogenic emissions of saccharides were investigated in 3 rural sites at Lincang, in the southwest border of China. PM_2.5 samples were simultaneously collected on three mountaintop _sites in Lincang: Datian, Dashu, and Yakoutian, which are located ∼ 300 km west of Kunming (the capital of Yunnan Province in China) and ∼ 120 km east from the Burma border. Five saccharide alcohols (glycerol, erythritol, inositol, arabitol, and mannitol) and five primary saccharides (fructose, glucose, mannose, sucrose, and trehalose), together with three anhydrosugars (levoglucosan, mannosan, and galactosan), were quantified by an improved high-performance anion-exchange chromatograph coupled with a pulsed amperometric detector. The total saccharides accounted for 1.6 ± 0.6 % in PM2.5. The anhydrosugars accounted for 48.5 % of total saccharides, among which levoglucosan was the most dominant species. The high level of levoglucosan was both attributed to the local BB activities and biomass combustion smoke transported from the neighboring regions of Southeast Asia (Myanmar) and the northern Indian subcontinent. The mono- or di-saccharides and sugar alcohols accounted for 24.9 ± 8.3 % and 26.6 ± 9.9 % of the total saccharides, respectively, and both proved to be mostly emitted by direct biogenic volatilization from plant material or surface soils rather than byproducts of polysaccharide breakdown during BB processes. Five sources of saccharides were resolved by non-negative matrix factorization (NMF) analysis, including BB, soil microbiota, plant senescence, airborne pollen, and plant detritus with contributions of 34.0 %, 16.0 %, 21.0 %, 23.7 %, and 5.3 %, respectively. The results provide information on the magnitude of levoglucosan and contributions of BB, as well as the characteristic of biogenic saccharides, at the remote sites of southwest China, which can be further applied to regional source apportionment models and global climate models.

Global ammonia is increasingly recognized as a significant contributor to fine particulate matter with a diameter smaller than 2.5 microns (PM2.5) due to the lack of mitigation policies for this species. Apart from agriculture fields, feedlots, and dairy farms, the more frequent wildfires during the dry season are contributing to rising emissions. Airborne and satellite instruments, such as Hyperspectral Thermal Emission Spectrometer (HyTES) and Cross-track Infrared Sounder (CrIS), have both detected and observed the ammonia plumes and enhancement from wildfires. Quantitative retrievals of HyTES observations at Gulch Fire in Arizona on July 3rd, 2014, suggest that the enhancement level is about 5 ppb, which is lower than both the emission from power plants (~10 ppb) and feedlots (~50 ppb) but ineligible. A further study with CrIS observations showed an unusual high level of ammonia at the Los Angeles area during the Bobcat fire in September 2020. A similar abnormal peak during the same month over Imperial Valley, a remote region southeast of Los Angeles basin. This may suggest the remote impact of wildfire emissions. Further modeling studies will help us to understand the process and contribution of wildfires in addition to local anthropogenic sources.

Volatile organic compounds (VOCs) are important species in the atmosphere which affect air quality and climate. Fire burning, including wildfires, agricultural fires, prescribed burning, and residential wood combustion, is the largest combustion-related source of VOCs to the atmosphere ^1–3. In addition to CO, CO₂ and CH₄ hundreds or even thousands of VOCs are emitted from fire burning ⁴. Such emissions are likely to increase in the future, with the spatial extent, number, and severity of wildfires having increased markedly at the global scale in recent decades ^5,6, this is predicted to continue as the climate warms ^7,8. In general, VOCs from fire burning include non-methane hydrocarbons (NMHC, C₂-C₁₂), aromatics, oxygenated hydrocarbons, nitrogen containing compounds. In the atmosphere, the emitted VOCs could be oxidized by OH radical, NO₃ radical and O₃ to form photochemical oxidants (e.g. O₃) and fine particles, which can cause severe air pollutions and damage human health. In this presentation, we review the knowledge about VOCs emissions from fires and discuss their atmospheric impacts on air quality and climate using the recent data from laboratory and fields studies. Reference:(1) Reid, J. S.; et al., Atmospheric Chem. Phys. 2005, 5 (3), 799–825. (2) Chen, J.; et al., Sci. Total Environ. 2017, 579, 1000–1034. (3) Granier, C.; et al., Clim. Change 2011, 109 (1–2), 163–190. (4) Koppmann, R.; von Czapiewski, K.; Reid, J. S. Atmospheric Chem. Phys. preprint; 2005. (5) Jolly, W. M.; Cochrane, M. A.; Freeborn, P. H.; et al., Nat. Commun. 2015, 6 (1), 7537. (6) Harvey, B. J. Proc. Natl. Acad. Sci. 2016, 113 (42), 11649–11650. (7) Krikken, F.; Lehner, F.; Haustein, K.; et al., Nat. Hazards Earth Syst. Sci. 2021, 21 (7), 2169–2179. (8) Lohmander, P. Cent. Asian J. Environ. Sci. Technol. Innov. 2020, 1 (5).

Nitro-phenolic compounds (NPs) have attracted increasing attention because of their health risks and impacts on visibility, climate and atmospheric chemistry. Both primary emission and secondary formation of NPs have been reported in recent studies, despite increasing research, the knowledge of the abundances, sources, atmospheric fates, and impacts remains incomplete, especially the gaseous species. In the present work, we conducted continuous measurement of eighteen gaseous NPs with a chemical ionization mass spectrometer at a background site in South China. Abundant NPs were observed in the continental outflows, with a total concentration of up to 122.1 pptv. A tracer method estimation showed that besides the direct emission from biomass burning, the secondary formation from the transported precursors contributed significantly to the observed NPs, with mono-NPs exhibiting photochemical daytime peaks and nighttime enrichments of di-NPs and Cl-substitute NPs. The budget analysis with a photochemical box model indicates that besides the ŸOH oxidations of aromatics, the NO₃Ÿ oxidation also contributed significantly to the daytime mono-NPs, and was more dominant for the nocturnal formation of di-NPs. Photolysis was the main daytime sink of NPs, producing substantial HONO, and thus would largely influence atmospheric oxidation capacity in downwind and background regions. This study provides quantitative insights on the characteristics, formation and impacts of gaseous NPs in the continental outflow, and highlights the role of NO₃Ÿ chemistry in the nitro-aromatics production that may facilitate regional pollution.

Urban fires in densely populated areas can burn large amounts of manmade structures and synthetic materials, which potentially have different chemical characteristics to wildfire smoke. Realworld chemical composition data of urban fire plumes are vital for evaluating health impacts; however, they are rarely available. Online monitoring of trace elements in ambient PM_2.5 at five sites throughout Hong Kong in 2021 provided unprecedented opportunities to observe the highly transient urban fire plumes. We observed sharp spikes for bromine (up to 0.65 µg m^-3), chlorine (7.0 µg m^-3), and certain metals such as lead (0.97 µg m^-3), zinc (8.3 µg m^-3), and copper (0.32 µg m^-3) during four urban fires, which showed ~10–6,000 times higher concentrations than pre-fire periods. These elements behaved decreased concentrations with increased distance from the fire location due to transport and dilution, but their concentration ratios remained relatively constant. During fire-influenced hours, these elements exhibited 3–101 times enrichment relative to iron, an element that had no discernable increase in concentration. It was distinct to one monitored hill fire that was characterized of remarkably high enrichment of tracers for biomass burning, i.e., Rb (11.6) and K (6.5). Our results represent the first observation of the co-emissions of Br and certain metals from urban fires. Reactive bromine released from burning of the wide-spread brominated synthetic chemicals was suggested to facilitate the mobilization of toxic metals into the atmosphere. This mechanistic knowledge will help us understand the emissions and impacts of fires occurring in wildland urban interface zones. The work also calls attention to previously overlooked influence of urban fires-mobilized metals on human health and of bromine on atmospheric oxidizing capacity in megacities.

Methoxyphenols represent one of the most abundant biomarker tracers for atmospheric wood smoke pollution. The reactions of atmospheric oxidants (ozone, OH, and NO₂) with methoxyphenols can contribute to the formation of secondary organic aerosols (SOA). The ionic strength effect can affect the kinetics and products distribution within the aerosol deliquescent particles. However, the ionic strength effects on aqueous phase reactions of atmospheric relevance have been barely studied in the past. In this work, we have leveraged our knowledge on ionic strength (I) effects on the reactive uptakes of gas-phase oxidants ozone (O₃) and nitrogen dioxide (NO₂) on methoxyphenols by the well-known wetted wall flow tube technique. The obtained results highlight the importance of the ionic strength effect and suggest a much faster oxidation kinetics of methoxyphenols in aerosol deliquescent particles, compared to that in the dilute aqueous phase of cloud droplets; hence, the oxidative power of O₃ and NO₂ could be enhanced by one order of magnitude at elevated ionic strength, compared to a dilute aqueous phase. Considering the high concentrations of inorganic salts occurring in aerosol deliquescent particles during haze events, the oxidative power of different atmospheric oxidants might become considerably higher than that predicted for oxidation reactions in cloud droplets. The addition of SO₄^2- typical for the liquid core of aerosol deliquescent particles increase the formation of condensed aromatics but also organosulfates (-OSO₃H) arose upon ozone reactions with methoxyphemols. The addition of NO₃^- ions, substantially favors the formation of multi-core aromatic compounds through heterogeneous ozone processing of methoxyphenols. More importantly, the addition of NO₃^- ions favors the formation of nitrooxy (-ONO₂) or oxygenated nitrooxy group of organonitrates, which are potential components of poorly characterized light absorbing organic matter (“brown carbon”) which in turn can affect the climate and air quality.

The September rainfall over Northern China (NC) in 2021 was the heaviest since 1961 and had unprecedented socioeconomic impacts. Holding the hypothesis that the drivers of extreme climate events usually contain extreme factors, we firstly propose the Ranking Attribution Method (RAM) to find the possible air–sea multi-factors responsible for this rainfall event. Via the atmospheric bridges of zonal-vertical circulation and Rossby wave energy propagation, the remote factors of warm sea surface temperature anomalies (SSTA) over the tropical Atlantic, cold SSTA over the tropical Pacific, Southern Annular Mode-like pattern in the Southern Hemisphere and North Pacific Oscillation-like pattern in the Northern Hemisphere jointly strengthened the Maritime Continent (MC) convection and Indian monsoon (IM). Through meridional-vertical circulation, the intensified MC convection enhanced the subtropical high over southern China and induced ascending motion over NC. The local factor of extreme air acceleration in the east Asian upper-level jet entrance region further anchored the location of the southwest-northeast rain belt. The strengthened IM and subtropical high over southern China induced considerable moisture transport to the rain belt via two moisture channels. The combined effect of these extreme dynamic and moisture conditions formed this unprecedented rainfall event. This study suggests that the RAM can effectively reveal the factors that contributed to this extreme rainfall event, which could provide a new pathway for a better understanding of extreme climate events.

Sudden heavy rainfalls occur more and more frequently in Japan during the summer. They are concentrated in restricted areas (typically of 5x5 km²) and can cause severe damage to infrastructure, often with casualties. The extrapolation of precipitation measured by radars is the conventional method for carrying out their nowcast in real time, i.e., short-term prediction on small spatial scales. However, the limit of predictability of such storms with current operational nowcasts is less than 10 minutes. This is due to the short lifetime (<15 minutes) of the individual convective cells causing the precipitation, as well as the limitations of nowcast models to properly account for the 3D nonlinear evolution of the cells. This presentation is about AI based real-time nowcasts of heavy precipitation on meso-γ-scale (2-20 km). The X-band Multi-Parameter Phased-Array Weather Radar (MP-PAWR) operating in Saitama prefecture (Japan) provides 3D observations of individual convective cells with a time resolution of 30 sec. A recurrent neural network is trained to detect the cells aloft and extrapolate them up to 10 minutes ahead. The neural network uses conventional Long Short-Term Memory (LSTM) units enhanced with 3D spatial convolutions and a training method developed for Generative Adversarial Network (GAN). It exploits high spatio-temporal resolution of the measurement up to an altitude of 8 km and the dual polarization. The model is trained with observations of 2020 and successfully predicts the onsets of sudden storms that occurred in 2018 and 2019. Its high prediction skills are illustrated with examples of typical storms where the operational system of Japan Meteorological Agency (JMA) or a conventional approach fails. A real-time system has been prepared to continuously analyze the observations of summer 2023 and we will present the first results.

Because of the progress of global warming, heavy rainfall intensity and the total amount are increasing in Japan. The heavy rainfall in Japan is frequently observed especially during summertime and occurred by various causes. One of the causes of the heavy rainfall is stagnated pressure pattern around Japan which guides the moisture transport from southwest toward Japan. With the pressure pattern as a background field, extratropical cyclone, front activity, and tropical cyclone induce the heavy rainfall. Since the air-sea interaction has influence on the formation of the pressure pattern, ocean prediction has a possibility to increase the prediction skill for heavy rainfall, and may extend the prediction lead time. Using regional atmospheric and coupled models, the impact of ocean prediction on summer heavy rainfall in Japan was examined for four events. It was found that the ocean prediction could extend the prediction lead time, when the heavy rainfall was caused by atmospheric rivers. Also, it increased prediction skill in the case of front-derived heavy rainfall. Further investigations showed that ocean prediction can provide better air-sea coupling regarding the Pacific high locating southeast of Japan, leading to provide better moisture transport from southwest of Japan. Analysis on reanalysis data indicates that the air-sea coupling around Japan is atmospheric feedback dominant region. Thus, the ocean prediction is necessary to consider the feedback from the atmosphere. Otherwise, the atmospheric feedback is neglected, and sea surface temperature prevails the atmospheric feedback as seen in tropics, then it causes unrealistic air-sea coupling and deteriorates the prediction. It is concluded that the ocean prediction contributes to increase prediction skill and can extend the lead time of heavy rainfall prediction.

To study the effects of different boundary layer schemes on the simulation of landing attenuation stage of typhoon Meranti (1614), a series of high-resolution (1.33 km) numerical tests were carried out using seven boundary layer parameterization schemes in the mesoscale numerical model WRF v3.8, namely, YSU, MYJ, QNSE, ACM2, UW, GBM, and Boulac, in terms of movement track, intensity, structure, rainfall, and near-surface physical variables. The results indicate the following. First, boundary layer schemes significantly influenced the simulation of typhoon Meranti’s track, intensity, and rainfall during its landing attenuation stage. Second, in the overall simulation of track, intensity, and precipitation of the typhoon, Boulac and MYJ schemes showed optimal results, in which the Boulac scheme was superior in simulating the typhoon track and precipitation and the MYJ scheme was superior in simulating typhoon intensity. The YSU and GBM schemes had the second best simulation results, whereas QNSE, UW, and ACM2 schemes had worse simulation performance. Moreover, the boundary layer schemes significantly differed in calculating the latent heat flux and sensible heat flux of near-surface layer, thereby affecting the simulation of typhoon track, intensity, and rainfall, leading to significantly different simulation results. The QNSE scheme resulted in an abnormally high latent heat flux, the MYJ and Boulac schemes resulted in the most modest values, and other schemes resulted in slightly smaller values. On the other hand, the QNSE scheme had a slightly higher sensible heat flux, the MYJ scheme showed the most modest one, and other schemes resulted in significantly smaller values. Finally, the boundary layer schemes significantly differed in the simulated thermal and dynamical structure of boundary layer, and Boulac scheme had the obvious advantages, particularly for the structure of boundary layer in daytime.

The Maritime Continent (MC) regularly experiences powerful convective storms that produce intense rainfall. Often the intense rainfall leads to flooding and landslides and thus to widespread destruction. At short lead times (0-12 hours), numerical weather prediction (NWP) models show low skill in the MC for forecasting convective activity. Nowcasting aims to solve this issue by using alternative methods to NWP models to make more accurate and reliable predictions of convective activity from observations over this key timescale. Optical flow algorithms are one of the most effective nowcasting methods as they are able to accurately track clouds across observed image series and predict forward trajectories. Optical flow is generally applied to weather radar observations, however, the radar network over the MC is sparse and cannot penetrate the high mountainous regions. In this research, we present the results of applying the Lukas-Kanade optical flow algorithm to infrared satellite imagery to generate 1 – 6 hour lead time nowcasts of heavy-precipitation-producing storms over the MC. For evaluation of the nowcasts, we present a novel adaption of the fractional skill score to quantify how nowcast skill varies spatially and temporally across the MC domain. Overall, the results show that the Lukas-Kanade algorithm has good skill, outperforming a persistence forecast for all lead times and showing skill up to 6 hours on a 50 km spatial scale. Low skill is observed over the mountainous regions in the early afternoon due to the algorithm’s inability to predict convection initiation. Overnight, however, high skill is seen over the sea as the model is able to accurately predict the offshore propagation of storms. These results show that the Lukas-Kanade algorithm can effectively nowcast mature storms and, when analysed alongside the skill maps, has potential to be used in an operational nowcasting system for the MC.

The major precursor phenomena were derived for detecting torrential rain clouds by intuitively analyzing satellite images. There were 17 cases of torrential rains in 2020-2021 in South Korea. The precursor phenomena were derived for satellite images such as water vapor, RGB composite, and secondary outputs from the Geo-KOMPSAT. We summarized major 12 possible check lists of key precursors that could become torrential rain clouds.
1. Updraft zone in front of the boundary of the upper dry area of North pacific High
2. Updraft zone in front of the dry area due to the Low pressure trough
3. Compressed wet zone between the North-South dry zones
4. Warm advection accompanied by Low pressure in warm conveyor belt
5. Lower cumulus clouds from strong southwesterly flow
6. Cirrus cloud as divergent in the upper strong wind zone
7. Upper layer cold core
8. Low-pressure rotating clouds of upper, middle, and lower layers on the stationary front
9. Periodic upper-level wave inflow on the stationary front
10. The cooling rate of the developing convective cloud lasts less than -3℃/10minutes
11. Clouds thickness of 10 km or more
12. Heavy rainfall critical index of 30 mm/hr or higher
If six or more phenomena among the above 12 conditions are present, the clouds would be developed to cause heavy rainfall. In addition, more than 60 mm/hr of possible precipitation, a strong instability index, the speed of movement of the developed clouds, and terrain factors should be considered. It is necessary to prepare for intense rainfall in summer by considering a combination of topographical factors. Several dominant cases with satellite images and checklists were introduced. This study was carried out with the support of the Korea Meteorological Administration R&D program [Meteorological satellite forecast support and convergence service technology development] (KMA2020-00120).

The Meteorological Service Singapore (MSS) maintains and runs a convection-permitting tropical configuration of the Met Office Unified Model (UM) known as SINGV for weather forecasts and climate projections over the Western Maritime Continent. A new Regional Atmosphere and Land (RAL) science configuration that includes, among other changes, a multi-moment cloud microphysics scheme has been proposed for kilometre-scale UM simulations in the mid-latitudes as well as in the tropics. Short-range forecast experiments with SINGV show that, compared to the operational configuration, the new science configuration produces more coherent storm structures with broader stratiform regions and reduces the tendency to over-forecast (under-forecast) precipitation over land (sea). An objective evaluation of the precipitation forecasts reveals significant improvements in Fractions Skill Scores (FSS) across forecast lead times and rainfall thresholds. The new RAL science configuration is a significant step towards better precipitation forecasts in this region.

The Cloud AeroSol Interacting Microphysics (“CASIM”) is the new double moment cloud microphysics scheme that will be introduced into the Met Office operational regional model next year. Results will be shown for performance in the Tropics and the UK. We show comparisons against the currently operational single moment cloud microphysics and investigate the effects of different aerosol activation protocols for activating droplets from the environmental aerosols. In the Tropics, CASIM outperforms the single-moment microphysics as evident from improved comparison with radar derived precipitation rates. We demonstrate that these improvements derive, in part of improved representation of rain evaporation and sedimentation when two prognostic moments are used. CASIM is also used for regional climate projections, and we will show results from dynamical downscaling over China with a particular emphasis on temperature and precipitation extremes. 

Vanuatu is one of the world's most vulnerable countries to natural disasters. The main climate hazards for Vanuatu include tropical cyclones, heavy rainfall resulting in flooding, extended periods without rain causing drought, rising sea levels threatening coastal environments and property, as well as sea temperature increase and ocean acidification impacting highly valuable coastal ecosystems and resources (including coral reefs, seagrass and fisheries). Recognising urgency to assist Vanuatu with climate change adaptation, the Green Climate Fund provided support for "Climate Information Services for Resilient Development Planning in Vanuatu (Van-CIS-RDP)" project, which is named Van-KIRAP (Vanuatu Klaemet Infomesen blong Redy, Adapt mo Protekt) in local language. Van-KIRAP project helps to address the needs to strengthen Climate Information Services (CIS) in Vanuatu delivering climate science to support decision makers and communities in Vanuatu to prepare for and adapt to climate variability and change. World Meteorological Organization's Global Producing Centre for Long-Range Forecasts (WMO GPC LRFs) Melbourne hosted by the Australian Bureau of Meteorology disseminates sub-seasonal to seasonal (S2S) climate information from the Australian Community Climate Earth-System Simulator – Seasonal (ACCESS-S) on global, regional and national scales, including climate forecast products for Vanuatu. This presentation provides an overview of products for climate monitoring and ACCESS-S S2S products for climate prediction disseminated via WMO GPC LRFs, with emphasis on utilising them under Van-KIRAP project for production of Climate Outlook Bulletins for tourism, agriculture, and fisheries sectors.

Persistent heavy rainfall produced by western North Pacific (WNP) tropical cyclones (TCs) can lead to widespread flooding and landslides in Asian countries. On July 2021, unprecedent rainfall amount occurred when Typhoon Infa passed through the highly populated eastern China. While the associated synoptic features have been analyzed, the extreme characteristics and return periods of rainfall induced by In-fa remain unexplored. Analyses of rainfall data from a WNP TC database of the China Meteorological Administration (CMA) show that Typhoon In-fa not only produces record-breaking rainfall accumulations at individual surface stations, but generates unprecedent rainfall amounts for the whole area of eastern China. Quantitatively, 2, 4, 11, 24 and 55 stations are exposed to once in 200-, 100-, 50-, 20- and 10-year extreme TC rainfall accumulations, respectively, and total rainfall at 75 stations reaches a record high since 1980. Overall, the return period is up to 481 years for the total rainfall amount accumulated in eastern China during the 1980–2019 baseline. The extremely long rainfall duration is identified as key to the torrential rains in the Yangtze River Delta before In-fa changes its direction of movement from northwestward to northeastward, while the extreme rain rate plays a dominant role in the northern areas afterwards. Probabilities of occurrence of such an unprecedented TC rainfall event have increased in most (75%) of the eastern China during the period of 2000–2019 compared with those during 1980–1999. Our study highlights the likely increase in risk of extreme TC-induced rainfall accumulations which should be considered in disaster risk mitigation.

Previous studies focused on the intense TCs in the central-southeastern western North Pacific (WNP), whose variability is intimately linked to El Niño-Southern Oscillation and extratropical sea surface temperature anomaly (SSTA) in the Pacific. Compared with them, weak TCs (WTCs) are more numerous and form further northwestward. The great number of WTCs and thereby the landfall cases may also cause huge damage to countries in Southeast Asia. However, their modulators are far from fully understood. Our research emphasizes the delayed impact of the early spring North Atlantic tripole SSTA (NAT) on the WTC formation frequency through the “capacitor” effect of sea ice (SIC) and SST in the Barents Sea. Detailed analysis indicates that a positive NAT may modulate an anomalous high in the Barents Sea-North Europe and decrease the local low cloud cover. Thus, more downward solar radiation tends to heat the local SST and decrease the SIC. This warmer Barents Sea could maintain through the typhoon season and excite a significant southeastward wave train, with several centers in the Arctic, Central Asia, and East Asia. The abnormal easterly to the south of the anticyclone in East Asia facilitates the cyclonic anomaly in the South China Sea, the Philippines, and the subtropical WNP, which reinforces the monsoon trough and favors the WTC formation there. A physical-based empirical model is developed for the WTC frequency, and hindcast is performed from 1979 to 2018. It shows the early spring NAT effectively improves the prediction skill for the WTC frequency, which can be considered as a crucial source of predictability for WTCs.

Tropical cyclones (TCs) over the western North Pacific (WNP) mainly occur in boreal summer and autumn. Using three best track datasets for 1979-2021, this study finds a significant inverse relationship between the relative TC genesis frequency (TCGF) during summer and that during autumn. This strong relationship is characterized with TC activity transitions from active (inactive) season in summer to inactive (active) season in autumn. That is, positive (negative) anomaly of TCGF in summer is followed negative (positive) anomaly in autumn. Such a reversal of TCGF anomaly is generally supported by the changes in large-scale environmental fields over the WNP from summer to autumn. Based on diagnostic analyses of TC genesis potential index, it is suggested that the mid-level relative humidity and vertical motions play major roles for the phase-transition of TCGF anomalies, followed by the secondary roles of low-level vorticity and mid-level meridional shear of zonal wind. Moist static energy budget analyses demonstrate that the phase reversal of climatological moist enthalpy advection by anomalous winds contributes to the phase transitions of anomalous vertical motion and relative humidity over the WNP. Seasonal evolution in sea surface temperature over the Indian Ocean is hypothesized to be associated with the phase transition of TCGF anomaly and corresponding large-scale environmental factors over the WNP.

Seasonal prediction of tropical cyclone (TC) activity has been a hot research theme in the past decades. Usually, the tropical sea surface temperatures (SSTs) provide considerable predictability sources for the western North Pacific (WNP) TC activity. Here, we emphasized that the Chukchi-Beaufort (C-B) and Greenland (GL) sea ice variability is closely linked to the year-to-year variations of the early autumn WNP TC formation frequency (TCF). Observational and numerical evidence proved that the excessive C-B and GL sea ice sustains from August to the following early autumn and triggers the southeastward propagation of the Rossby wave trains originating from the Arctic across Western Eurasia (Okhotsk Sea) to the WNP. The resultant anomalous low pressure over WNP provides suitable environmental conditions for TC formation—the enhancement of the lower-level relative vorticity and water moisture, and the decrease of vertical wind shear. For the reduced sea ice, an opposite situation tends to emerge. The persistent combined sea ice signal makes it a physically meaningful precursor for TCF prediction. The cross-validated hindcast and independent forecast based on both the tropical SST and the Arctic sea ice precursors present that the TCF index is predicted with much higher correlation coefficients than those of the empirical models with only the tropical SST predictors. The results demonstrate that the Arctic sea ice truly promotes the seasonal prediction capability of the WNP TCF.

Understanding how global warming affects tropical cyclone (TC) intensity and precipitation for target regions is essential to preparing for associated damages but detailed processes remain uncertain. This study provides a first quantification of anthropogenic influences on TC characteristics affecting South Korea using convection-permitting model (CPM) simulations (3 km resolution). For the observed four recent TCs that strongly affected South Korea, CPM simulations were performed under current (ALL) and pre-industrial conditions (NAT). The observed sea surface temperature and lateral boundary conditions were used for ALL while changes attributable to human influences (estimated using CMIP6 multimodel simulations) were removed from observed boundary conditions for NAT runs. ALL experiments captured the observed TC intensity and precipitation reasonably. After removing human influences, TC intensity and precipitation were reduced in NAT experiments. Importantly, areas with extreme precipitation (i.e., having precipitation larger than 150 mm) were found to expand by 16~37% in ALL compared to NAT, which was induced by an enhanced upward motion near the TC core and an increase of background water vapor in line with warming. Further, the role of increased moisture was found to become important as TC moves to mid-latitudes. This study provides new insights into how greenhouse warming can intensify TC-induced extreme precipitation over East Asia.

Tropical cyclone (TC) track characteristics in a changing climate remain uncertain. Here, we use downscaled simulations from nine Coupled Model Intercomparison Project Phase 6 (CMIP6) models to investigate the genesis, tracks, and termination of >64,000 synthetic TCs traveling within the Southeast Asia region from the historical era (1881-1900 CE) to the modern era (1991-2000 CE) to the future (2081-2100 CE). Under both a moderate emissions scenario (SSP2-4.5) and a high-emissions scenario (SSP5-8.5), TCs are more likely to form closer to major land masses, terminate farther inland, and move most slowly while over mainland Southeast Asia or in the Bay of Bengal from the historical era to future. Furthermore, TCs become more likely to intensify most quickly in locations near mainland Southeast Asia in the future compared to the historical era, and the rate at which TC maximum winds increase is amplified in the future under both of our modeled scenarios. Additionally, results suggest a northern shift in TC track locations in the northwest Pacific basin, as well as an increased tendency for TC tracks to traverse mainland Southeast Asia in the future compared to the historical era.

Convectively coupled equatorial waves and Madden and Jullian Oscillations (MJO) are known to significantly impact cyclogenesis throughout the tropical ocean basins. Studies show that these waves can modulate cyclogenesis potential index, thus affecting the genesis frequency. Recent studies show that the frequency and intensity of cyclones forming over Arabian Sea and Bay of Bengal have undergone significant trends over the recent decades. In this study we explore the role of tropical intraseasonal oscillations and their long term variabiliity on the observed trends of cyclogenesis over Bay of Bengal and Arabian Sea for a period of 40 years from 1981 till 2020. Long term trends in the factors that determine genesis potential index are analyzed along with their relation to equatorial waves and MJO. The study shows that the trends in the wave modulated of the genesis parameters, namely, relative humidity at mid troposphere, vorticity, vertical wind shear and potential intensity, each play a different role in the observed trends in cyclogenesis for the two basins during pre and post monsoon seasons. The results give an insight into the importance of these subseasonal processes in modulating the long term changes in extreme events in addition to background climate change.

Severe turbulence encounters can result in serious injuries and millions of dollars of operational costs to airlines. In addition to the turbulence generated within the storm, thunderstorms can induce turbulence in the surrounding clear air, which can extend large distances from the storm boundary due to gravity waves and other circulations. Using relatively limited datasets, past studies have indicated that severe turbulence can extend beyond the current federal guidelines for avoidance (20 mi/~32 km), but many questions remain. For example, 1) What is the spatial distribution of convection induced turbulence relative to individual storms? 2) What dynamical factors determine the spatial distribution? Now, extensive archives of operational radar data and automated eddy dissipation rate (EDR) reports from commercial aircraft present an opportunity for more detailed analysis. To address the above questions, we objectively identify thunderstorms in the GridRad hourly radar archive over the contiguous USA and compare them to automated turbulence reports over a period of 9 years (2009-2017). This analysis, based on millions of turbulence data points, is used to diagnose the risk of turbulence relative to storms.

Clear-air turbulence (CAT) has a large impact on the aviation sector. Our current understanding of how CAT may increase with climate change in future is largely based on simulations from CMIP3 and CMIP5 global climate models (GCMs). However, these models have now been superseded by high-resolution CMIP6 GCMs, which have grid lengths at which median individual turbulence patches may start to be resolved. Using a CMIP6 multi-model approach, projected CAT changes over the North Atlantic have been quantified. Twenty-one CAT diagnostics are used, to represent the uncertainties in CAT production mechanisms. Each diagnostic responds differently in time, but the majority display an increase in CAT between 1950 and 2050. These results refer to Northern Hemisphere (NH) CAT changes, particularly over the North Atlantic basin. Although NH winter is historically the most turbulent season, there is strong multi-model agreement that autumn and summer will have the greatest overall percentage increase in CAT frequency. Future NH summers are projected to become as turbulent as 1950 winters. Using the global-mean seasonal near-surface temperature as a comparative metric, with every of global near-surface warming, NH autumn, winter, spring, and summer are projected to have an average of 14%, 9%, 9%, and 14% more moderate CAT, respectively.

The one-third power of the energy dissipation rate (EDR) is calculated based on the Thorpe method using high vertical-resolution radiosonde data (HVRRD) at 68 operational stations in the United States for six years (2012–2017) observed at 00 and 12 UTC. The spatiotemporal distributions of HVRRD-derived EDR (HVRRD-EDR) are compared with those of in-situ flight EDR (flight-EDR) observed along main flight routes of commercial airlines over z=20–45 kft and within ±1 h centered at 00 and 12 UTC. Both datasets show similar seasonal variations, with the largest total occurrences in June–August (JJA) and the smallest in December–February (DJF). Vertically, both ratios of light-or-greater (LOG) and moderate-or-greater (MOG) intensity turbulence events to the total turbulence events of HVRRD-EDR reveal two local peaks at z=20–23 kft and 41–44 kft, while those of flight-EDR at z=20–23 kft and 35–38 kft. The LOG and MOG ratios of HVRRD-EDR show clear seasonal variations with large values in JJA and small values in DJF, while those of flight-EDR show somewhat different variations: large values in DJF and small values in JJA at z=20–30 kft, large values in March–May and small values in September–November at z=30–45 kft. Consequently, correlation coefficients between HVRRD-EDR and flight-EDR are negative at z=20–30 kft and positive at z=30–45 kft. Horizontally, the MOG ratio of HVRRD-EDR is large primarily over the Rocky Mountains which is consistent with the flight-EDR. The discrepancies in the spatiotemporal distributions between the two datasets likely stem from, at least, two reasons: (i) turbulence observed from the two datasets cannot be the same events, and (ii) HVRRD-EDR associated exclusively with static instability cannot include shear instability under stable atmospheric condition that is related to the Kelvin-Helmholtz instability of which most of flight-EDR is involved.

Turbulence is one of the most significant physical processes in aviation meteorology. Rapid changes in a headwind in the vicinity of an aircraft can lead to aviation accidents. Although many efforts have been made to detect such turbulence in advance, difficulties have been encountered due to its small spatial (1 mm ~ 1 km) and temporal (1 s ~ 1 hr) scales, and unpredictable behavior. By utilizing Doppler lidar to observe turbulence with a high spatiotemporal resolution continuously, it is possible to overcome the limitations of in situ observation and detect turbulence in advance. Energy dissipation rate (EDR) is a valuable measure that can quantitatively represent the intensity of turbulence. Various techniques have been proposed to estimate EDR using Doppler lidar. In this study, we will compare the performance of three inertial range EDR estimation techniques (power spectrum, second-order structure function, variance). They are applied to Doppler lidar measurements operating in the vertically fixed-pointing mode. The accuracy of the estimated EDR was evaluated by the one from in-situ sonic anemometer. The results show that the data sampling rate greatly affected the EDR estimation using the power spectrum. The variance technique produces the most accurate estimation when compared to the value calculated from the sonic anemometer. This study will examine the benefits and weaknesses of each technique, taking into account the instrumental characteristics and scanning strategy of Doppler lidar.

ACKNOWLEDGEMENT
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A4A1032646). This work was funded by the Korea Meteorological Administration Research and Development Program "Developing Technology for High-Resolution Urban Weather Information Services” under Grant (KMA2018-00627).

The current aviation turbulence forecast systems worldwide, including in South Korea, are mostly based on deterministic forecasts that provide the turbulence potential fields predicted by calculating turbulence diagnostics using the single numerical weather prediction (NWP) model output. Given that both the turbulence diagnostics and NWP model have inherent uncertainties, the need for probabilistic forecasts that can take such uncertainties into account and provide confidence in the forecasting is increasing. In this study, we perform probabilistic turbulence forecasts using the two operational Global Data Assimilation and Prediction System (GDAPS) outputs based on the Unified Model (UM) and the Korean Integrated Model (KIM) of the Korea Meteorological Administration (KMA), and evaluate the performance of the two GDAPS-based probabilistic turbulence forecasts against available turbulence observation data for one year (2022.01–2022.12). Although the UM- and KIM-based GDAPS have a similar horizontal resolution (~10 km for UM; ~12km for KIM), the KIM-based GDAPS has a higher vertical resolution than the UM-based GDAPS (70 levels below 80 km for UM; 91 levels below 80 km for KIM). To perform a single NWP model-based probabilistic turbulence forecast, two probabilistic forecasting approaches are considered, which are a forecast time-lagged ensemble and a multi-diagnostic ensemble. The UM-based deterministic turbulence forecast with a higher horizontal resolution predicts wider and stronger turbulence regions than the KIM-based deterministic turbulence forecast, regardless of the higher vertical resolution of KIM-GDAPS, although the predicted turbulence regions are overall similar. Detailed comparison results of performance validation of two GDAPS-based probabilistic turbulence forecasts will be presented in the conference. Acknowledgment: This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-00410.

As global air traffic has steadily increased over the past few decades, the need for more accurate flight time compliance and safe and efficient air operations has arisen. To this end, the ICAO (International Civil Aviation Organization) established a global navigation plan and urged each country to implement it. Accordingly, Korea also launched the National Aviation Plan (NARAE) to realize a safe and efficient air traffic system. To successfully carry out the NARAE, it is necessary to provide detailed and three-dimensional aircraft weather information to support optimal operation for each flight stage in preparation for environmental changes in future air operations. In particular, at airport points, it is essential to provide probabilistic forecasts for decision-making of aircraft operations for factors such as visibility, ceiling, and wind shear, which significantly affect the takeoff and landing of aircraft. In this study, we configured the ensemble system to produce probabilistic forecasts of aircraft meteorological factors. An ensemble system is configured based on the Korea Meteorological Administration's KLAPS (Korea Local Analysis and Prediction System) and employs the stochastic ensemble methods; SPPT (stochastically perturbed physics tendencies) and SKEBS (stochastic kinetic-energy backscatter scheme). We conducted experiments on low visibility and wind shear cases at Incheon Airport. We confirmed the ensemble spread of each experiment by comparing the results of experiments with the different numbers of ensemble members. This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-00410. And the main calculations in this study were performed using supercomputing resources provided by the Korea Meteorological Administration (National Center for Meteorological Supercomputing).

The sea-effect generally refers to the process of air mass denaturation after the cold air flows above the warm sea surface. This type of fog is often observed in the western coastal zone of Bohai Sea and has an important impact on the local aviation. There is little research on the heavy fog in this area due to the influence of the "sea effect". This study investigates the mechanism of a typical sea-effect fog case in the morning of October 17, 2007 using WRF model over western coastal zone of Bohai Sea. Results indicated that: contrary to the most analyses of sea-effect snowstorm and precipitation studies that generally emphasize the role of oceanic heating, evaporation of Bohai Sea and accompanying advective vapor transport are shown to play more important roles in the formation of fog. The moisture budget reveals that the evaporation of the Bohai Sea at night is ten times greater than in the western coast. The water vapor transport guided by the back-flow in Northeast China contributes the most to the fog water. The vapor flux in this direction fluctuates and increases from 16:00, Oct 16 to 03:00, Oct 17 (LST), and vertical integral corresponding to the tendency of liquid water reaches a maximum at 03:00, Oct 17. In contrast, the temperature advection is weak. Sensitivity tests further show that the changes of the SST field only cause a shift in the location of the fog, while the humidity change triggered by the Bohai Sea effect plays a decisive role on fog formation in western coastal zone. The sensitivity test of evaporation shows that: with the decrease of evaporation in Bohai Sea, the advective vapor transport will be significantly weakened, and the simulated fog on the western coast of the Bohai Sea will disappear completely.

A modified algorithm which represents additional top-down turbulent mixing driven by cloud/fog-top radiative cooling was provided by Wilson and Fovell in 2018. The algorithm is appended to the Yonsei University (YSU) planetary boundary layer (PBL) parameterization scheme to improve turbulent mixing within the cloud/fog. Considering the modified YSU (YSU_mod) simulation may be affected by different combined parameterizations, three different combinations of parameterization schemes, including the schemes by formula provider recommended (WF), the schemes using by operational numerical prediction of North China Plain (NCP), and the land surface process parameterization in WF schemes being substituted by that in NCP (WF_1), are selected to evaluated fog cases over North China Plain using the Weather Research and Forecasting (WRF) model in order to promote the application of YSU_mod in NCP operational forecast system. The results show that the influence of the additional top-down turbulent mixing in fog is mainly shown as, (1) it significantly increases PBL temperature while the slight and uncertain effect on specific humidity, which lead to less fog layer liquid water and fog cover, and later formation and early dissipation of fog. (2) Under different combinations of parameterization schemes, the influence is that the warmer bottom of the PBL in WF schemes which promotes fog layer lifting, while the warmer upper of the PBL in NCP schemes which lowers fog layer. (3) The warmer fog layer don’t always correspond to the truth, the negative temperature deviation is modified successfully in WF schemes, while the temperature and humidity deviations are larger than observations in NCP schemes. In addition, an appropriate land surface parameterization greatly affects fog simulation which can be concluded from the significant fog simulation improvement in WF_1 comparing to the WF.

It has been observed that some tropical cyclones (TCs) rapidly intensify within 24-48 hours of landfall along the South China coast, which poses a great challenge in terms of intensity forecasts and subsequent disaster preparedness. Some case studies have shown that an increase in sea-surface temperature (SST) might be responsible for such rapid intensification. In this paper, we show that the SST increase is likely due to solar heating of the ocean over a shallow continental shelf, which can occur for a TC with a small cloud cover. Numerical simulations of TCs with small and extensive cloud cover demonstrate that this is indeed the case. Further studies based on a large number of cases illustrate further that a combination of a high TC translation speed and a small cloud cover has the highest chance of causing a TC to intensify rapidly just before landfall.

Current theories of tropical cyclone (TC) intensification give little direct indication of the role of the TC size in intensity changes, although there are observations showing a relationship. We develop a new model of TC central pressure tendency where the pressure change can be expressed as exponential with a time constant determined by the ratio of radius maximum wind (R_max) and the column inflow or outflow speed. An analysis of observations confirms the relationship which becomes more important for a larger pressure tendency and suggests an upper bound on pressure tendency for a given R_max. The dependence of the pressure tendency on size poses a challenging constraint on the accurate forecasting of TCs in numerical weather prediction and climate models.

Understanding the responses of landfalling tropical cyclones to a changing climate has been a topic of great interest and research. Among them, the recently reported slowdown of tropical cyclone landfall decay in a warming climate engenders controversy. Here, the global climatology of landfall decay, based on the tropical cyclone best-track data available, reveals that the reported trends are uncertain and not universal, but spatial, temporal, data, and methodology dependent such that any claim of a climate trend could be misleading at present. The effective area of moisture supply from the ocean, most likely determined by the landfalling track modes, is demonstrated to be an important factor for the decay. This study provides timely essential clarifications of the current contentious understanding.

Accurately prediction of tropical cyclone (TC) intensity is quite challenging due to multiple competing processes among the TC internal dynamics and the environment. Most previous studies have evaluated the environmental effects on TC intensity change based on TC best-track data which results from both internal dynamics and external influence. This study quantifies the environmental effects on TC intensity change using a simple dynamically based dynamical system (DBDS) model recently developed. In this simple model, the environmental effects are uniquely represented by a ventilation parameter B, which can be expressed as multiplicative of individual ventilation parameters of the corresponding environmental effects. Their individual ventilation parameters imply their relative importance to the bulk environmental ventilation effect and thus to the TC intensity change. Six environmental factors known to affect TC intensity change are evaluated in the DBDS model using machine learning approaches with the best-track data for TCs over the North Atlantic, central, eastern and western North Pacific and the statistical hurricane intensity prediction scheme (SHIPS) dataset during 1982–2021. Results show that the deep-layer vertical wind shear is the dominant ventilation factor to reduce the intrinsic TC intensification rate or to drive the TC weakening, with its ventilation parameter ranging between 0.5–0.8. Other environmental factors are generally secondary, with their respective ventilation parameters over 0.8. An interesting result is the strong dependence of the environmental effects on the stage of TC development. Finally, applications of the DBDS model to real TC intensity prediction are briefly discussed.

Unlike most tropical cyclones (TCs) that begin to weaken when approaching the land, there are some TCs that intensified near-landfall on the contrary. Those TCs bring a great challenge to typhoon forecasts and disaster prevention. Based on three best track datasets JMA, CMA and JTWC from 1980 to 2021, the intensity variation characteristics of tropical cyclones in the Western Pacific within 48 hours before landfall were studied, and 11 common typical near-landfall intensified typhoon cases in the three datasets were selected for composite analysis. The environmental wind vertical shear, relative eddy flux convergence and local radiation flux during landings were calculated using ERA5 reanalysis. The intensity changes of 24hr, 12hr and 6hr before landfall were documented. According to statistical analysis, tropical cyclones whose intensity changes of 24hr before landfall exceeded 20kt and did not weaken within 48hr before landfall were defined as near-landfall intensification cases. Statistics show that those TCs have northwestward prevailing tracks. The high activated period is from July to September, and the probability during nighttime is significantly higher than that of daytime. Composite analysis of selected typical near-landfall intensified TCs shows that the vertical wind shear, which has no significant decreasing before landfall. Further analysis find out that the decreasing of local vertical wind shear and the increase of relative eddy flux convergence can favor the development of inner-core convection. The asymmetric distribution of latent heat, sensible heat flux and long-wave radiation on the underlying surface around the typhoon before landfall is further analyzed. It is found that the asymmetric distribution of local non-adiabatic typhoon circulation is conducive to the development of offshore intensity. Under favorable dynamic and thermal processes. Statistical analysis of such typhoons reveals common characteristics and possible influential factors.

Using validated Weather Research and Forecasting (WRF) model simulation data, kinetic energy (KE) budgets were analyzed to study the dynamic processes associated with the radial movement of maximum wind in the lower troposphere during the rapid intensification (RI) of tropical cyclone (TC) Khanun (2017). In this study, the radial movement of the maximum symmetric rotational KE is computed to measure the movement of the maximum wind. RI is divided into four stages: before RI, the radial distance decreases inward rapidly (stage 1); during RI, the radial distance undergoes a slowly inward decrease (stage 2), barely unchanged (stage 3) and a slowly inward decrease again (stage 4). The comparison between the radii of maximum symmetric rotational KE and its maximum tendency reveals that the radius of maximum symmetric rotational KE generally is larger than that of maximum tendency of symmetric rotational KE in all stages except in stage 3 when maximum symmetric rotational KE and its maximum tendency are nearly collocated. The analysis of symmetric rotational KE budgets shows that flux convergence of KE always is an important term determines maximum tendency in all stages. The conversion from asymmetric rotational KE to symmetric rotational KE, and the conversions from environmental KE to symmetric rotational KE and from symmetric divergent KE to symmetric rotational KE contribute to the maximum tendency, respectively, in stage1 and stages 3 and 4. The important terms in determining maximum tendency are further partitioned and the results will be reported in the conference. Keywords: tropical cyclone, rapid intensification, kinetic energy budget, symmetric and asymmetric circulations, divergent and rotational circulations.

Higher tropical cyclone (TC) intensification rates are affected by smaller radius of maximum wind (RMW) while continuing convection within the RMW can cause RMW contraction. Therefore, understanding the factors affecting convection distribution and RMW is crucial for characterizing TC intensification rates. Previous studies have shown that convection distribution is affected by TC genesis type, but subjective classification of TC genesis doesn’t rely on data distribution. Hence, this study develops a new objective method to classify TC genesis type based on a K-means cluster analysis of critical environmental parameters available in ECMWF Reanalysis v5 (ERA5) data. For a proportionate comparison between intensification rate and RMW, the lifetime maximum intensification rate (LMIR) in each case is estimated in this study. The K-means cluster analysis shows four TC genesis types: (i) monsoon confluence (MC), (ii) easterly wave or north of monsoon trough (EW), (iii) monsoon shear (MS), and (iv) monsoon depression (MD). The positive vorticity area in MC and MS indicates the monsoon trough. MC forms at the east of the monsoon trough while MS forms inside the monsoon trough. EW has a small RMW and a small high positive vorticity area. In contrast, MD has a larger RMW and a larger high positive vorticity area. Each genesis type has different convection distribution compared to previous objective method. Owing to larger circulation and scattered convection, MD has a significantly larger RMW and lower LMIR than EW based on Conover's test. In contrast, EW cases have high specific humidity only around the center. Although MC and MS have medium RMW sizes between those of EW and MD, their LMIR is as high as that in EW due to the aggregated convection.

The occurrence of rapid intensification (RI) of a tropical cyclone (TC) involves multi-scale processes and complex interactions, posing a major challenge in operational forecasting. RI is also a well-trodden path for TCs reaching high storm intensity. The most widely-applied RI threshold is 30 kt intensity change in 24 hours, which is the 95^th percentile of the overwater intensity changes based on National Hurricane Center best-track database in Atlantic and eastern North Pacific basins during 1995-2012. The statistics of the 95^th percentile of intensity change can vary for TCs in other basins or during a different time period. To conduct a process-based study, this work attempts to identify a better RI definition that can most representatively identify the storm structure difference between RI and non-RI periods for TCs in the western North Pacific. Results based on 40-year (1979-2020) TC best-track data from JMA and JTWC are presented. This study explores the differences in storm structure and TC-environment conditions between RI and non-RI periods. It is shown that an RI threshold of 50 (40) kt intensity change in 36 (24) h based on JMA (JTWC) yields the most distinct warm-core structure difference between RI and non-RI periods.

Polycyclic aromatic hydrocarbons (PAHs), one class of most toxic compounds in atmosphere, showed much longer than predicted lifetimes in ambient air due to heterogeneous scavenging by ozone (O₃) according to previously studies. Here we show heterogeneous reactions with hydroxyl (OH) radical dominates far more over that with O₃ in the degradation of particle-bound PAHs based on chamber simulation on the evolution of plumes from biomass burning, the largest emitter of PAHs on a global scale. The evolution of crop straw burning fumes in a 30 m³ smog chamber revealed that for heterogeneous reactions of PAHs with OH, their second order degradation rate constants were estimated to be 4.4~8.6×10^-12 cm³ molecule^-1 s^-1 at the initial stage of photo-oxidation, and they dropped 4-5 times to to 1.1~1.7×10^-12 cm³ molecule^-1 s^-1 after 1~5×10¹¹ molecule cm^-3 s OH radical exposure, corresponding to a decrease of uptake coefficients of OH (γ_OH) from 2.2±0.9 to 0.5±0.1. The decrease of second order rate constants and γ_OH were mainly caused by the reduction in secondary chemistry and the burial effect of coatings by secondary organic/inorganic aerosols during the atmospheric oxidation. Moreover, the OH oxidation pathway (1.1~1.7×10^-6 s^-1) dominates over ozonolysis pathway (1.7~12.7×10^-7 s^-1) in heterogeneous degradation of PAHs. The lifetimes of particulate PAHs were estimated to be 93.5~219 h, approximately 120 times that obtained previously by exposing pure PAHs to oxidants, implying their stronger long-range transport and more persistent health risks.

A better understanding of spatial-temporal variations of open bio-mass burning in China is required to assess its impacts on the air quality and especially on the heavy haze pollution. The MODIS fire spots data and the calculated burned areas were used in this research, which shows the varying number of fire spots in China from 2013 to 2017, with the highest in 2014 and the lowest in 2016. Meanwhile, the fire spots were found mainly concentrated in three key periods (March-April, June and October-November) and two zones with inter-annual variations of burned areas. In addition, the contribution of major vegetation types burning was studied, the cropland occupied the largest proportion of burned area of more than 70 % in any period time, followed by forest. Finally, from the perspective of climate and human activities, the causes of inter-annual variations were discussed. By comparing the average temperature and precipitation in the two zones from 2013 to 2017, it was found that the burned forest area is positively correlated with the average temperature of the zones and negatively correlated with the average precipitation. Meanwhile, the relationship between the El Niño events and the bio-mass burning was discussed. Finally, by using the datasets of MODIS fire spot, land cover, vegetation cover, bio-mass loading and emission factors, a bio-mass emission model is developed, which is then embedded as an on-line module to an air quality model (WRF-CUACE) to quantitatively assess the impacts of bio-mass burning on surface PM_2.5 concentration in China.

Formaldehyde (HCHO) plays a vital role in atmospheric chemistry and O3 formation. Open biomass burning (OBB) is considered to be an important source of HCHO; however, its quantitative contribution to ambient HCHO remains poorly understood due to the lack of reliable high-resolution emission inventories. In this study, a satellite-based method coupled with local emission factors was developed to estimate the hourly primary emissions of HCHO and volatile organic compound (VOC) precursors from OBB in Guangdong (GD) Province of southern China. Furthermore, the contribution of OBB to ambient HCHO was quantified using the Community Multi-scale Air Quality model. The results suggested that in average OBB emissions contributed 5293 tons of primary HCHO per year, accounting for ~14% of the total anthropogenic HCHO emissions in GD. The ambient HCHO concentration ranged from 0.3 ppbv to 8.7 ppbv during normal days, and from 8 ppbv to 45 ppbv in downwind area during OBB impacted days. The monthly contribution of OBB to local HCHO levels reached up to 50% at locations with frequent fires and over 70% during a forest fire event. Ambient HCHO was heavily affected by primary OBB emissions near the source region and by the oxidation of OBB-emitted VOCs in the downwind area. Secondary HCHO formation from OBB emissions was enhanced during photochemical pollution episodes, especially under conditions of high O3 and low NOx. OBB-emitted ethene was identified as the most important VOC precursor of HCHO and contributed to the formation of ~50% of the secondary HCHO. The HCHO formation potential of cropland fires was 26% higher than that of forest fires. Our results suggest that OBB can elevate ambient HCHO levels significantly. Thus, strict control policies on OBB should be implemented, especially for open-burning agricultural residues in upwind areas on serious photochemical pollution days.

This study provides the first comprehensive inventory of all known pyrocumulonimbus (pyroCb) events observed worldwide (546 confirmed events) over the nine-year period 2013-2021. PyroCbs are a dangerous and severe type of fire weather, which present many hazards to firefighting efforts and communities along the wildland-urban interface. These unique storms also serve as a vertical transport pathway (large chimney) facilitating rapid injection of smoke into the upper troposphere and lower stratosphere (UTLS). This inventory provides insight into basic questions on inter-annual, seasonal, sub-daily, and regional variability of pyroCb, along with potential controlling factors. Development of this inventory has included detailed analysis of the distribution and variability of pyroCb smoke injection altitudes, quantitative estimation of the aerosol mass associated with each stratospheric plume, and examination of the impact of pyroCb activity on stratospheric aerosol loading worldwide. This pyroCb inventory provides the means to address a wide range of significant open questions about the nature, behavior, and impact of this phenomenon. Answers to these questions are critical for advancing pyroCb prediction capabilities to mitigate aviation hazards and aid firefighting efforts. This new multi-year inventory dataset also sets a foundation for an official Earth System Data Record that can be maintained into the future and extended back in time to identify longer-term trends in pyroCb activity and ensuing impacts on the climate system.

Pyrocumulonimbus (pyroCb) are fire-induced and smoke-infused thunderstorms that serve as the primary pathway for smoke to reach the upper troposphere and lower stratosphere (UTLS).The magnitude of smoke plumes observed in the UTLS has increased significantly in recent years, rivaling or exceeding the impact from all volcanic eruptions observed over the last decade, with the potential for significant climate feedbacks on seasonal and hemispheric scales. The Black Summer fire season of 2019-2020 in southeastern Australia contributed to an unprecedented pyroCb ‘super outbreak’ that took place over 51 non-consecutive hours. More than half of the 38 observed pyroCb updraft pulses injected smoke particles directly into the stratosphere, producing two of the three largest smoke plumes observed at such altitudes to date. Over the course of three months, these plumes encircled a large swath of the Southern Hemisphere while continuing to rise, in a manner consistent with existing nuclear winter theory. Fewer than three years earlier, a large pyroCb outbreak in Canada produced a persistent smoke plume that encircled a portion of the Northern Hemisphere. We summarize what the community has learned from these extreme events and identify science questions that remain unanswered. A recently-developed pyroCb inventory for 2013-2021 facilitates the first analysis of regional, seasonal, monthly, and inter-annual variability worldwide. Unique in-situ and remotely-sensed measurements of pyroCb activity observed during the 2019 FIREX-AQ field experiment identify the fire characteristics, cloud microphysical properties, and smoke plume chemistry associated with this extreme fire-weather phenomenon.

Wildfire events are increasing globally which may be partly associated with climate change, resulting in significant adverse impacts on local, regional air quality and global climate. In September 2020, a wildfire event occurred in Souesmes (Loir-et-Cher, Sologne, France), and its plume spread out over 200 km on the following day as observed by the MODIS satellite. Based on measurements at a suburban site (~50 km northwest of the fire location) in Orléans, young wildfire plumes were characterized. Significant increases in gaseous pollutants (CO, CH₄, N₂O, VOCs, etc.) and particles (including black carbon) were found within the wildfire plumes. Emission factors, defined as EF (X) = ∆X/∆CO (where X represents the target species), of various trace gases and black carbon within the young wildfire plumes were determined accordingly and compared with previous studies. Changes in the ambient ions (such as ammonium, sulfate, nitrate, chloride, and nitrite in the particle- and gas- phase) and aerosol properties (e.g., aerosol water content, aerosol pH) were also quantified and discussed. Moreover, we estimated the total carbon and other gas (e.g., CO) emissions that were compared with fire emission inventories. We found that the Global Fire Assimilation System (GFAS) may underestimate emissions (e.g., CO) of this small wildfire while other inventories (GFED, FINN) showed significant overestimation. Considering that few wildfires are recorded in this region, related atmospheric implications are presented and discussed. More details can be found in Xue, Chaoyang, et al. "A study on wildfire impacts on greenhouse gas emissions and regional air quality in South of Orléans, France." Journal of Environmental Sciences (2022).

Our understanding of the vertical coupling between the lower atmosphere and the upper atmosphere has significantly advanced in recent years, spurred by the large amount of new space-borne and ground-based observations and the extension of atmospheric models into the mesosphere and thermosphere. Observational and model studies have revealed that the mesosphere-lower thermosphere (MLT) region is the nexus where the forcings by gravity waves (GWs), atmospheric tides and planetary waves (PWs) contribute to driving the mean meridional circulation (MMC). Sandwiched between the two summer-to-winter overturning circulations in the mesosphere and the upper thermosphere, the climatological lower thermosphere mean meridional circulation is a narrow gyre that is characterized by upwelling in the middle winter latitudes, equatorward flow near 120 km, and downwelling in the middle and high summer latitudes. Based on the hourly output from the 2000–2014 simulations of the National Center for Atmospheric Research’s vertically extended version of the Whole Atmosphere Community Climate Model (WACCM-X) in specified dynamics configuration, we examine the roles of PWs, GWs and atmospheric tides in driving the mean meridional circulation in the lower thermosphere and its response to the sudden stratospheric warming phenomenon with an elevated stratopause in the northern hemisphere. Following the onset of the sudden stratospheric warmings, this gyre reverses its climatological direction, resulting in a “chimney-like” feature of un-interrupted polar descent from the altitude of 150 km down to the upper mesosphere. This reversal is driven by the westward-propagating planetary waves, which exert a brief but significant westward forcing between 70 and 125 km, exceeding gravity wave and tidal forcings. We present evidence of this circulation in observational data and the observational needs to improve our understanding of its variability.

An international joint research project, entitled Interhemispheric Coupling Study by Observations and Modelling (ICSOM), is ongoing. In the late 2000s, an interesting form of interhemispheric coupling (IHC) was discovered: when warming occurs in the winter polar stratosphere, the upper mesosphere in the summer hemisphere also becomes warmer with a time lag of days. This IHC phenomenon is considered to be a coupling through processes in the middle atmosphere (i.e., stratosphere, mesosphere, and lower thermosphere). Several plausible mechanisms have been proposed so far, but they are still controversial. This is mainly because of the difficulty in observing and simulating gravity waves (GWs) at small scales, despite the important role they are known to play in middle atmosphere dynamics. In this project, by networking sparsely but globally distributed radars, mesospheric GWs have been simultaneously observed in seven boreal winters since 2015/16. We have succeeded in capturing five stratospheric sudden warming events and two polar vortex intensification events. This project also includes the development of a new data assimilation system to generate long-term reanalysis data for the whole middle atmosphere, and simulations by a state-of-art GW-permitting general circulation model using reanalysis data as initial values. By analyzing data from these observations, data assimilation, and model simulation, comprehensive studies to investigate the mechanism of IHC are planned. This paper provides an overview of ICSOM, but even initial results suggest that not only gravity waves but also large-scale waves are important for the mechanism of the IHC.

Radar observations of the troposphere and lower stratosphere made in northern Germany with two VHF MST radars separated by about 30 km in quite different topography over a period of one week are presented. One of the radars, the SOUSY MST radar, was located in the Harz Mountains; the other radar, the Mobile SOUSY MST radar, was operated in a limited form and provides an interesting and rare example of a small beam swinging Doppler radar operating in the lower VHF band (53.5 MHz). Wind results are consistent between the radars, but there are some differences in the measured upward fluxes of horizontal momentum. Wave activity associated with the passage the jet stream over the location of the larger radar in the Harz Mountains is quite evident in the measured winds at the two locations. Stratospheric and tropospheric measurements of aspect sensitivity, mean winds and wave fluxes measured at the two sites from this campaign are presented and discussed.

Bromine nitrate (BrONO₂) is a major reservoir of the active bromine radicals that contribute to the formation of ozone hole. Energetic particle precipitation (EPP) generated NO_x enhancement is known to lead to ozone loss in the mesosphere and upper stratosphere. In the lower stratosphere, however, this NO_x enhancement is believed to restrain ozone depletion by deactive chlorine and bromine species to the reservoir forms (HCl/HBr and ClONO₂/BrONO₂). In this study we report BrONO2 response to EPP for the first time, using the latest BrONO₂ measurement data from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat. We report a positive and significant correlation between BrONO2 and EPP Ap index above 30km in Arctic. In addition, the Whole Atmosphere Community Climate Model with D-region ion chemistry (WACCM-D) was used to characterize the EPP contribution to BrONO₂ formation.

The mesosphere and lower thermosphere (MLT) region is strongly modulated by gravity wave (GW) perturbations, and the momentum flux of GW is essential in quantitatively understanding and modeling atmospheric structure at all altitudes. In this study, we present the seasonal variations, long-trend of mesospheric density and GW momentum flux observed by the meteor radars at Davis Station (68.6°S, 77.9°E), in Antarctica, Svalbard (78.3°N, 16°E), Tromsø (69.6°N, 19.2°E) in the Arctic. In general, u’w’ decreases and v’w’ increases as altitude increasing, and westward maximum of u’w’ and northward maximum of v’w’ in spring and autumn at upper altitude. the momentum flux u’w’ and v’w’ is anti-correlated with the background winds (u/v). The seasonal variations in the Davis Station meteor radar relative densities in the southern polar mesopause are mainly dominated by an annual oscillation (AO). The mesopause relative densities observed by the Tromsø meteor radars at high latitudes in the Northern Hemisphere show mainly an AO and a relatively weak semiannual oscillation (SAO).

Thermospheric nitric oxide (NO) is a minor species that not only cools the thermosphere through 5.3 µm radiation but also changes significantly during geomagnetic storms. NO column densities derived from TIMED/GUVI data reveal a number of features: (1) NO enhancement is very sensitive to energy input in high latitudes; (2) NO column density and O/N2 column density ratio are anti-correlated on a global scale but show a noticeable shift between the boundaries of NO enhancement and O/N2 depletion regions during geomagnetic storms; (3) Flare time NO enhancement. The features (1) and (2) are likely caused by storm-time meridian circulation that bring high latitude NO enhancement and O/N2 depletion to mid and low latitudes. The feature (3) is caused by increased production of NO. TIMED/GUVI observations of NO serve as a way to monitor the storm-time dynamics and state of the thermosphere for space weather research and operation.

A new dual-frequency atmospheric radar system is built and installed in Langfang Observatory, north China. It utilizes novel two-frequency system design that allows interleaved operation of 53.8 MHz for stratosphere and troposphere wind observation, and 35.0 MHz for meteor observation, which optimizes performance for both ST wind retrieval and meteor trail detection. In dedicated meteor mode, the daily meteor count rate reaches over 40,000 and allow wind estimation of finer time resolutions, such as 15-min and 30-min interval, better than the 1-hour typical of most meteor radars. The uncertainty of the ST wind measurements is better than 2 m/s when estimating the line of best fit with radiosonde winds. Preliminary observation results of typical winter gravity waves (GWs) momentum fluxes in the mesosphere, lower stratosphere and troposphere are also presented.

A record-breaking precipitation event caused 398 deaths and 20.06 billion RMB economic losses in Henan Province of China in July 2021. A maximum 24-h (1-h) precipitation of 624 mm (201.9mm) was observed at the Zhengzhou weather station. However, all global operational forecast models failed to predict the intensity and location of maximum precipitation for the event. This high social impact event has drawn much attention from the research community. This presentation provides a high-level review of the event and its research from the perspectives of observations, analysis, dynamics, predictability, and the connection with climate warming and urbanization. Global reanalysis revealed obvious abnormality in large-scale circulation patterns that resulted in abundant moisture supplies in the region of interest. Recent studies of this event also revealed, via high-resolution model simulation and data assimilation, that three mesoscale systems (a mesoscale low pressure system, a barrier jet and downslope gravity current) contributed to the local intensification of the rainstorm. Further, observational analysis suggested that an abrupt increase of graupel through microphysical processes contributed to the record-breaking precipitation. Although these findings aided in our understanding of the extreme rainfall event, preliminary analysis indicated that the practical predictability of the extreme rainfall for this event was rather low. The contrary influences of climate warming and urbanization on precipitation extremes as revealed by two studies could add further challenges to the predictability. We concluded by emphasizing that data sharing and collaboration between meteorological and hydrological researchers would be crucial in the future research on high-impact weather events.

Climate risks from compound extremes are much greater than the sum of their sub-components. In early summer 2022, record-breaking rainstorm and heatwave were respectively observed in the south and north of eastern China and they formed an unprecedented compound extreme (hereafter referred to as SRNH). However, most of seasonal predictions failed to capture the SRNH and the behind mechanisms are still unclear. Here we show that the wavier mid-latitude westerlies and the ridge of the western Pacific subtropical high stabilized at 20 °N were the main causes in the atmosphere and the ultra-strong 2022 SRNH event could be well reproduced. The February-March sea surface temperature anomalies in the North Atlantic concurrently promoted the 2022 SRNH extreme, which was successfully verified by numerical experiment. Main physical mechanism is the warmer sea surface excited a Rossby wave train to enhance the contrast between hot North China and wet South China.

An extremely heavy rainfall event lasting from 17 to 22 July 2021 occurred in Henan Province of China, with accumulated precipitation of more than 1000 mm over a 6-day period that exceeded its mean annual precipitation. The present study examines the roles of persistent low-level jets (LLJs) in maintaining the precipitation using surface station observations and reanalysis datasets. The LLJs triggered strong ascending motions and carried moisture mainly from the outflow of Typhoon In-fa (2021). The varying directions of the LLJs well corresponded to the meridional shifts of the rainfall. The precipitation rate reached a maximum during 20−21 July as the LLJs strengthened and expanded vertically into double LLJs, including synoptic-weather-system-related LLJs (SLLJs) at 850–700 hPa and boundary-layer jets (BLJs) at ~950 hPa. The coupling of the SLLJ and BLJ provided strong mid- and low-level convergence on 20 July, whereas the SLLJ produced mid-level divergence at its entrance that coupled with low-level convergence at the terminus of the BLJ on 21 July. The formation mechanisms of the two types of LLJs are further examined. The SLLJs and the low-pressure vortex (or inverted trough) varied synchronously as a whole and were affected by the southwestward movement of the WPSH in the rainiest period. The persistent large total pressure gradient force at low levels also maintained the strength of low-level geostrophic winds, thus sustaining the BLJs on the synoptic scale. The results based on a Du-Rotunno 1D model show that the Blackadar and Holton mechanisms jointly governed the BLJ dynamics on the diurnal scale.

This study investigates the influences of urban land cover on the extreme rainfall event over the Zhengzhou city in central China on 20 July 2021 using the Weather Research and Forecasting model at a convection-permitting scale [1-km resolution in the innermost domain (d3)]. Two ensembles of simulation (CTRL, NURB), each consisting of 11 members with a multi-layer urban canopy model and various combinations of physics schemes, were conducted using different land cover scenarios: (i) the real urban land cover, (ii) all cities in d3 being replaced with natural land cover. The results suggest that CTRL reasonably reproduces the spatiotemporal evolution of rainstorms and the 24-h rainfall accumulation over the key region, although the maximum hourly rainfall is underestimated and displaced to the west or southwest by most members. The ensemble mean 24-h rainfall accumulation over the key region of heavy rainfall is reduced by 13%, and the maximum hourly rainfall simulated by each member is reduced by 15–70 mm in CTRL relative to NURB. The reduction in the simulated rainfall by urbanization is closely associated with numerous cities/towns to the south, southeast, and east of Zhengzhou. Their heating effects jointly lead to formation of anomalous upward motions in and above the planetary boundary layer (PBL), which exaggerates the PBL drying effect due to reduced evapotranspiration and also enhances the wind stilling effect due to increased surface friction in urban areas. As a result, the lateral inflows of moisture and high-θe (equivalent potential temperature) air from south and east to Zhengzhou are reduced.

Moisture transport, associated with moisture sources and synoptic-scale weather conditions, is a key dynamic process of precipitation events. Using the K-means clustering method and the FLEXPART Lagrangian particle dispersion model, this paper investigates moisture contributions from different source regions to extreme precipitation in the first rainy season (hereafter FRS) over South China. In average, land regions contribute more to the FRS extreme precipitation over South China than the ocean regions. The main source regions are Southeast Asia (22.7%), South China (17.2%), the South China Sea (14.3%), and the Bay of Bengal (8.3%). Extreme precipitation events are classified into three types by the K-means clustering based on 850 hPa geopotential height, which are all characterized by an anomalous low-pressure system over South China with varying intensity and locations. The distribution of geopotential height anomaly for Type I (30.3%) is characterized the low trough extending from Japan to South China, while Type II (42.5%) and Type III (27.2%) are characterized by “west negative–east positive” and “north positive–south negative” patterns over East Asia with anomalous cyclone over South China, respectively. The much larger contribution of land sources than ocean regions are mainly concentrated in Type I and Type III, of which the contribution from each source region is similar. Ocean sources play a more important role in Type II and are mainly from the Indian Ocean (16.2%) associated with the onset of South China Sea summer monsoon.

During the pre-summer rainy season in southern China, MCS-associated heavy rainfall frequently occurs in the warm sector hundreds of kilometers to the south of a front or without any front, which is one of the major contributors for coastal flooding events during this period. The intensity and frequency of strong convective storms are expected to rise with warming climate, posing a greater threat of extreme rainfall in the future. This study aims at revealing the future changes of coastal warm-sector MCS in southern China on its related precipitation features and essential mesoscale process associated based on quasi-idealized WRF simulations and pseudo global warming (PGW) approach. Typical warm-sector heavy rainfall events are selected to produce composite environments that force the quasi-idealized simulation in current climate (CTRL). After that, the climate sensitivity experiment (PGW) is conducted with the same configurations except that it is forced by reanalysis data plus a climate thermodynamic perturbation derived from a 32-model CMIP5 ensemble climate change signal for SSP5-8.5 scenario. Comparisons between PGW and CTRL reveal that 12-h accumulated area-averaged rainfall increases 51% by the end of 21st century, with the maximum rising from 333.1 mm to 703.4 mm. As for convection population, the frequency of convection weaker (stronger) than 20dBZ increases (decreases). The rainfall distribution and rainfall maximum are characterized by a north shift, which is largely due to stronger southerly LLJ in warming climate. Moreover, the MCS is initiated ~1h earlier in PGW, with more unstable inflow and stronger back building process leading to larger rainfall accumulation. Notably, although dynamic climate change signal is not included in the current experiment, the LLJ is significantly enhanced by increasing latent heating. The LLJ, latent heating, and deep moist convection may therefore link as a positive feedback in warming climate and eventually lead to extreme rainfall.

Extreme precipitation events cause damage to the natural environment and human society, but their jointly evolution behaviors in both time and space dimensions have not been extensively studied. By introducing a spatiotemporally contiguous events tracking (SCET) method, here we examine the climatologies and trends of the 3D (latitude×longitude×time) characteristics of spatiotemporally contiguous extreme precipitation events across the globe under the past and future climate warming during 1980 and 2020. The results show that the SCET can well demonstrate the jointly dynamic evolution patterns of contiguous precipitation events in time and space dimensions. Spatially, large contiguous precipitation events tend to be concentrated southeastern Asia, Europe and central Africa. Temporally, the frequency of contiguous precipitation events shows an overall increasing trend, but their affected area, intensity, duration and movement showed different characteristics. The future changes of spatiotemporally contiguous precipitation are further investigated based on a set of CMIP6 model simulations. In the coming decades (e.g., 2020–2100), the affected area, intensity and moving speed of contiguous precipitation events will increase. These trends are especially more prominent under high greenhouse gasses emission scenarios (e.g., SSP585). It is expected that extreme precipitation events that are more intense, affect larger areas, and move faster will cause even more harm to human society. Our results provide new insight to understand the spatiotemporal evolutions and future changes of precipitation extremes.

This study explores the coupled variability of temperature and precipitation in eastern China during summer using multivariate EOF analysis. Two leading modes are identified and analyzed, with the first mode showing a robust increasing trend in temperature and a robust drying trend in precipitation in the region north of the Yangtze River; the second mode suggests a systematic decadal variability in temperature and precipitation. The underlying mechanisms for these leading modes are revealed through correlation and composite analysis. A negative Pacific-Japan teleconnection pattern in the lower troposphere and a stationary Rossby wave train across Eurasia in the upper troposphere are contributing factors to the trend mode. The trend component is closely tied to global warming, and also to increased sea surface temperature anomalies over the western Atlantic Ocean, which amplify the wave train teleconnection across Eurasia. The decadal variability is found to be associated with internal decadal change in interannual variability in the atmosphere, specifically, the decadal alternation of active oscillations above the North Atlantic and Pacific, which are likely related to the North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) modes, respectively. Further analysis illustrates that the NAO has a stronger impact during its active periods, while the PDO tends to have an opposite influence to the NAO when the NAO is inactive.

The Global Satellite Mapping for Precipitation (GSMaP) produces high-resolution and high-frequent global rainfall map based on multi-satellite passive microwave radiometer observations with information from the Geostationary InfraRed (IR) instruments (Kubota et al. 2020). Outputproduct of GSMaP algorithm is 0.1-degree grid for horizontal resolution and 1-hour for temporal resolution. Images and data of the GSMaP are available at JAXA GSMaP website (http://sharaku.eorc.jaxa.jp/GSMaP/). The GSMaP has been developed by the Japan Aerospace Exploration Agency (JAXA). The JAXA has participated in the World Meteorological Organization (WMO) has been initiated the Space-based Weather and Climate Extremes Monitoring (SWCEM) (Kuleshov et al. 2020) and provided 22-yr GSMaP data. Currently, the gauge-adjusted near-real-time version 6 (GNRT6) of the GSMaP (Tashima et al. 2020) has been distributed to the SWCEM.In December 2021, the GSMaP algorithm was updated to version 8 by implementing various improvements to better estimate precipitation (Kubota et al. 2022). We plan the reprocessing of the GSMaP version 8 in a period during the past 24 years ”since Jan. 1998” using JAXA super computer system (JSS3). Past GSMaP products did not cover the first 2 years of TRMM era (1998-Mar. 2000) due to lack of NOAA CPC-4km Global IR dataset. There is a possibility that GridSat-B1 data (Knapp et al. 2011) enables to fill the lack of period. In order to extend the period of the GSMaP before 2000, the validity of GridSat-B1 data is investigated in this study.

Satellite rainfall estimates provide valuable spatial information of precipitation, although biases in the estimate need to be corrected, especially for regions near the equator with large spatial and temporal variability in precipitation. It was found that the large variability in the bias can significantly reduce the benefit of quantile-mapping methods for the calibration of the satellite rainfall estimates with gauge measurements. Further investigations found that the bias is possibly related to the type of weather system and this bias can be sub-divided into different categories accordingly. In this presentation, the results of the calibration of weekly and sub-weekly rainfall from the SWCEM-EAWP (Space-based Weather and Climate Extremes Monitoring - East Asia West Pacific) products (CMROPH-CRT and GSMaP_GNRTv6) using rainfall measurements from subset of gauges over Singapore will be presented. The categorization of the rainfall to the respective bias group is first trained using machine learning methods and followed by quantile-mapping for calibration. The improvement in the performance in the machine-learning aided quantile mapping is then evaluated with the full set of rain gauges in Singapore, and compared to the satellite rainfall estimates before calibration and with just quantile mapping calibration applied.

Quite a few real-time forecasts of the annual frequency of tropical cyclones (TCs) have been made in recent years, using statistical and dynamical approaches. However, such forecasts are not very useful for disaster preparedness. Since 2015, we have been making seasonal forecasts of the number of TCs making landfall in different regions along the East Asia coast using a regional climate model with boundary conditions from the Climate Forecast System (CFS). In this paper, how such forecasts are made will be presented together with verification statistics for the last six years. A possible extension of this approach to forecast the intensity of these landfalling TCs will also be discussed.

Marine Heatwaves (MHWs) have dire impacts on aquatic ecosystems and the communities that rely on them for livelihood, recreation, and cultural identity. In Vanuatu, a MHW event in 2016 led to record high sea surface temperatures with widespread reports of fish mortality. Given the severity of impacts, monitoring and predicting MHW events is critical in allowing communities and key sectors to effectively manage (and where possible) mitigate the impacts of MHW events. In this study, we use in situ data, satellite data and dynamical forecasts to derive MHW climatologies and investigate key MHW events. Results for the common period of overlap between all examined datasets (1981-2018) will be presented.

Droughts are a cyclical feature of Australia's climate and have compounding effects on agricultural productivity and wellbeing. Understanding future conditions in context of antecedent observations is critical to providing informed early warning of drought. In this study we pair probabilistic seasonal forecasts with monitoring datasets to provide early warning of drought. Hindcasts from the Bureau of Meteorology's sub seasonal to seasonal forecast model, ACCESS-S2, are paired with MSWEP satellite blended precipitation data and AWRA-L water balance modelled soil moisture and evapotranspiration. Principal Component Analysis (PCA) is used to derive objective weightings to combine precipitation, soil moisture and evapotranspiration percentiles in a multivariate manner similar to the U.S. Drought Monitor. The final DEWS maps overlay forecasting information with PCA-weighted antecedent conditions. We produce 1-, 3- and 6-month maps and analyse drought concern over the common period of overlap between our datasets (1981-2018) and conduct case studies for the 1982-1983 Ash Wednesday tinder drought and the 1997-2001 Millennium drought. We validate PCA-weighted maps with satellite vegetation data and find performance is strongest over the Murray Darling Basin region (R = 0.63, p= 0.009) and poorest over Central interior Australia (insignificant correlations). We also validated PCA-weighted maps using agricultural commodity data from ABARES. Significant negative correlations at 95%, 99% and 99.9% confidence intervals were found between %-Area in drought category and crop cultivation area; export volume/value; crop yield; and rural debt. Our findings indicate that early warning of drought can be categorised by concern – wherein dry antecedent conditions and dry forecasted conditions are of highest concern. Our proof-of-concept drought early warning system contributes to the growing body of proactive drought research. In a drought vulnerable future, operationalising and communicating drought early warnings will be critical to reducing the harmful impacts of drought on economies, environments, and people.

There is a near consensus among scientists that the average global temperature will increase in the next several decades. Many international organizations are taking steps to reduce this anticipated phenomenon by as much as possible. However, there are many forces, social and economic interests that make this goal almost impossible to achieve. Significant climate changes will occur and the world must be prepared. The talk will discuss the top ten reasons why the efforts taken by certain governments and motivated people are not going to be enough. The USA supreme court decision from June 2022; Increased world population; Rapid increase in GDP mainly in developing countries; Increased global demand for electric power; The coal oil and Gas Industry; Increased demand for air and ocean transportation and the tourism Industry; The increased demand for food, mainly but not only from live stock; Increased demand for raw materials mainly steel and cement; The Garment industry and Production of solar panels and storage devices and 

The zonal sea surface temperature (SST) gradient over tropical Pacific is important for global climate and known to be a pacemaker of global warming. During recent decades, observations show a “La Niña-like” strengthening of the zonal SST gradient, whereas most climate models produce an “El Niño-like” weakening of this gradient. There is also large model uncertainty in the future projection of this gradient change under global warming. Quantifying the sources and understanding the mechanisms for the uncertainty is needed to reconcile the discrepancy between observations and models and reduce model uncertainty of future projections. Here we use a total of 342 simulations, including six large ensembles, 36 CMIP5 and 36 CMIP6 models, to quantify the relative contribution of internal variability and model spread to the uncertainty in this gradient change on multidecadal timescales and future projections. On multidecadal timescales, the uncertainty of zonal SST gradient change over the tropical Pacific is dominated by internal variability. Although external forcing is increasing under global warming, the ratio of internal uncertainty is decreasing but still >80% at the end of the 21^st century. The Pacific Decadal Oscillation (PDO) is revealed as the key internal mode influencing the uncertainty of the multidecadal changes. The PDO-related SST pattern exhibits a pronounced zonal gradient over the tropical Pacific. For future projection, the uncertainty of zonal SST gradient change over the tropical Pacific is mainly from the model spread in response to external forcing. Based on future change between 2070-2099 and 1961-1990, model spread accounts for ~70% of the total uncertainty. The SST change in the western and eastern tropical Pacific dominates the total uncertainty of the zonal SST gradient for CMIP5 and CMIP6 models, respectively. That is mainly due to the stronger low cloud positive feedback over the southeastern Pacific among CMIP6 models.

Satellite altimetry is a matured technology that provides accurate measurements of ocean geophysical information of sea surface heights (SSHs), significant wave heights (SWHs), and wind speed. It was designed specifically for observing ocean’s geo-physicality and dynamics. Furthermore, these technologies also have the potential to be developed to study the ocean and atmosphere, simultaneously. It is well known that satellite altimetry is also equipped with sensors to measure atmospheric components to provide corrections for the range measurement. On-board microwave radiometer measures atmospheric water vapour to provide wet path delay correction for the main altimeter range measurement. This perspective develops a new potential for satellite altimetry to also be exploited to observe the atmosphere. It has been proven that the interaction of ocean and atmosphere have a significant role on influencing climate phenomena and their variations through its complex process. Several studies also found that the uncommon reactions between anomalies in atmosphere and ocean hydrological cycle occurred due to rapid climate changes resulting in hydro-meteorological catastrophe. These situations make a thorough study about the interaction between ocean and atmospheric component are necessary. Thus, this study is performed to leverage satellite altimetry sea level anomaly and water vapour measurement to analyse the complex interactions between the ocean and atmosphere to determine the nature and characteristics of these interactions. Using both ocean and atmosphere measurement from satellite altimetry not only present a new potential of the satellite itself, but also a way to overcome the drawback of previous ocean-atmosphere studies, which has temporal and spatial disparities as each parameter were observed using different platforms at different time and locations. Moreover, by multi-missions observations, spatial and temporal resolution are enhanced, making it reliable enough to monitor long periodic phenomena, such as El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Madden-Julian Oscillation (MJO), and monsoons.

Predicting tropical cyclone intensity changes, especially rapid intensification (RI), has been a more challenging topic than tropical cyclone tracking because of its multi-scale physical process with significant contributions from convective-scale phenomena. Before intensification, tropical cyclones experience precession process, in which tilted vortices rotate counterclockwise around the center of circulation, and develop an axisymmetric structure. The forecast uncertainty in precession process limits the predictability of early-stage intensification of TCs. In this study, we have explored the contribution of moist convective activity to the variability of TC precession process using a simple toy-model of the vortex precession process, together with fully dynamic ensemble-based sensitivity experiments using convection-permitting Weather Research and Forecasting model (WRF-ARW) simulations. The results indicate the potential existence of a threshold in vortex strength and structure that governs whether it completes precession and initiates RI. Given the strong nonlinearity of the onset process of RI, the advancement of our understanding of the uncertainty sources will provide an insight about the observation network that may effectively constrain the TC forecasting.

As an artificial intelligence method, machine learning (ML) has been widely used in prediction models of high-dimensional datasets. This study proposes an ML method, the Gradient Boosted Regression Tree (GBRT), to predict the intensity changes of tropical cyclones (TCs) in the Western North Pacific at 12-, 24-, 36-, 48-, 60-, and 72-h (hr) forecasting lead time and the model is optimized by the Bayesian Optimization algorithm. The model predictands are the TCs intensity changes at different forecasting lead times, obtained from the best track data of the Shanghai Typhoon Institute (STI) and the Joint Typhoon Warning Center (JTWC) from 2000 to 2019. The model predictors are the synoptic variables, climatological and persistent variables derived from the reanalysis data obtained from the National Centers for Environmental Prediction (NCEP), and the sea surface temperature (SST) data obtained from the National Oceanic and Atmospheric Administration (NOAA). The results show that the GBRT model can capture the TCs intensity changes well for the succeeding 12-h, 24-h, 36-h, and 72-h. Compared with the traditional multiple linear regression (MLR) model, the GBRT model has better performance in predicting TCs intensity changes. Compared with the MLR model, R² of the GBRT model for TCs intensity forecast increases by an average of 8.47% and 4.45% for STI data and JTWC data. MAE (RMSE) drops by 26.24% (25.14%) and 10.51% (4.68%) for the two datasets, respectively. The potential future intensity change (POT), the intensity changes during the previous 12 h (Dvmax), Initial storm maximum wind speed (Vmax), SST, and the Sea-Land ratio are the most significant predictors for the GBRT model in predicting TCs intensity change over the Western North Pacific.

The aim of this study is to enhance our understanding about the intensification phase of tropical cyclones that occurred in the southwest Indian Ocean. Tropical cyclone (TC) Batsirai (2022) which unexpectedly and rapidly intensified into a Saffir-Simpson Category 4 cyclone (lowest sea level pressure of 934 hPa) after undergoing an eyewall replacement cycle was selected in this study. During the passage of the intensified tropical cyclone (1−4 February 2022), Mascarenes Islands (i.e. Rodrigue, Mauritius, Réunion) were heavily impacted though the effects were relatively minor; the major devastating hazards were reported in Madagascar (121 deaths). The French cloud-resolving mesoscale model, Meso-NH, successfully reproduced the lifecycle of TC Batsirai. In this study, the intensification phase of TC Batsirai has been focused to understand the physical and thermodynamical processes responsible for the intensity change of the tropical cyclone. In addition, the impact of the orography of Mascarenes Islands on localized heavy rainfall and strong winds has been studied. The detailed analysis using the numerical simulation results with fine horizontal grid spacings (250 m) has been done. The key results will be presented at the conference.

In this study, kinetic and potential energy budgets of Typhoon Mujigae (2015) are compared in slow (SI) and rapid (RI) intensification periods. The high-resolution numerical simulation data of Mujigae are validated with observations and are used for comparison analysis. The analysis of kinetic energy budgets reveals that the conversion between asymmetric rotational kinetic energy and symmetric rotational kinetic energy play a key role in determining the differences in the tendencies of symmetric and asymmetric rotational kinetic energy between RI and SI in the lower troposphere. Asymmetric rotational kinetic energy is converted to symmetric kinetic energy in RI, whereas symmetric rotational kinetic energy is converted to asymmetric kinetic energy in SI. This can explain why the symmetric rotational kinetic energy tendency is larger in RI than in SI, but asymmetric rotational kinetic energy tendency is smaller in RI than in SI. In both RI and SI, both conversions from symmetric divergent and environmental kinetic energy to symmetric rotational kinetic energy contributed to the increase in symmetric rotational kinetic energy. The analysis of symmetric divergent kinetic energy budget shows that conversion from symmetric divergent kinetic energy to symmetric rotational kinetic energy is nearly balanced out by the work done by symmetric divergent winds against pressure gradient, which is further related to the conversion from potential energy mainly due to the latent heat release. The detailed results will be presented in the conference.

Tropical cyclones (TCs) moving poleward to mid-latitudes in the western North Pacific basin are complex to forecast their track since their tracks are largely influenced by the upper-level synoptic fields in subtropical and mid-latitude regions (i.e., western North Pacific subtropical high, mid-latitude trough, and jet stream). Thus, forecast performances are especially poor when TCs approach the East Asian region. It is known that soil moisture (SM) modulates the total available energy into sensible heat fluxes (SHF) and latent heat fluxes (LHF) at the land surface. It can affect the atmospheric temperature, clouds, and even precipitation through land-atmosphere interaction. Thus, those land surface processes are necessary for accurate weather forecasts or climate predictions. Therefore, we prescribed two types of SM data for the initial condition of the Weather Research and Forecasting (WRF) model to investigate the sensitivity of the SM initialization on the TC forecasts. The Global Land Data Assimilation System (GLDAS) Version 2.1 and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) were selected in this study. The result showed that ERA5 SM data had more wet biases than GLDAS data over the whole simulations except for some areas located south of Mongolia. Also, TC intensity forecast performances were similar in both runs, while TC track forecast performances were improved in the experiments with GLDAS data. The differences in track forecasts between the two runs were more considerable during the landfall period. In addition, in the experiments with ERA5 data, simulated TCs tended to move westward compared to the experiments with GLDAS data due to the strengthened interaction between simulated TCs and the mid-latitude trough. This study showed that it is important to provide realistic SM data to improve the TC track forecast performances during the TC landfall period.

Predicting Tropical Cyclogenesis has long been a challenging research task. Machine learning methods recently have been shown to withhold a high prediction skill for forecasting tropical cyclogenesis. In this study, we first use Kalman filters applied on the Gaussian-smoothed 850-hPa vorticity field to label tropical disturbances. Dynamic and thermodynamic variables, such as wind shear and sea surface temperature, are extracted following the disturbance center in a Lagrangian sense. Three machine learning models with varying complexity: a random forest, a support vector machine and a neural network, are trained to classify developing and non-developing tropical disturbances at a forecast lead time of 24 hours. All of the three models have displayed consistently good performance, with the neural network having the highest f1-scores of 0.79. SHAPley values analysis shows that the mid-level vorticity at 500 hPa is the most essential feature in deciding if a tropical disturbance is developing or non-developing for all the models. Wind shear and tilting are found to possess a certain level of importance as well. Our results suggest that rich mid-level cyclonic vorticity in a mesoscale convective vortex is instrumental in the subsequent low-level spin-up of the disturbance and is an essential sign for the prediction of Tropical Cyclogenesis.

The prediction of tropical cyclones (TCs) has progressively improved in the past few decades. How long can we further extend skillful TC prediction? The answer to this question relies on in-depth studies about TC intrinsic predictability. In this study, the HWRF-based convection-resolving ensemble forecasts were adopted with perturbed initial conditions to study the error growth and intrinsic predictability of TCs. The new aspect of our study is the focus on the sensitivity of TC track and intensity predictability on region-dependent initial errors. The main conclusions include: (1) the most sensitive region for TC track forecasts is case-dependent, for TC intensity forecasts, however, consistently locates within the TC vortex. (2) The 0-2 wave number structures of the TC inherent vortex flow can be predicted for more than 4 days, which is primarily determined by the predictability of the synoptic-scale environment. In contrast, the remaining components (wave numbers >2) are only predictable for a few hours. These finer scales, despite much less variance than larger scales, saturate rapidly and intensively limit the predictability of TC intensity which is generally defined by the maximum surface wind at a certain point in time and space rather than the azimuthal mean wind.

With the nonhydrostatic axisymmetric cloud-resolving model CM1, two sets of ensemble runs are conducted to study the influence of initial vortex depth on the early development of an idealized tropical cyclone (TC). It is found that with the shallow initial vortex, the intensification rate (IR) is smaller and the TC takes a longer time to reach a state of slantwise moist neutrality. These behaviors are primarily ascribed to the less active convection in the shallow case. In contrast to the deep initial vortex, the shallow initial vortex is characterized by a more stable stratification in the lower troposphere and a smaller gradient of angular momentum (M) in the mid-to-upper troposphere. The former tends to hinder the occurrence of deep convection in the TC while the latter makes it difficult to intensify the mid-toupper level vortex and to establish a deep vortex. Thus, deep convection bursts intermittently in the early developing stage of the TC originating from the shallow vortex. During the periods of inactive convection, the TC weakens by the decline of M source associated with reduced horizontal advection, which is overwhelmed by sinks of M from moderately decreased vertical advection and nearly-constant dissipation term. Therefore, the IR of the TC is smaller in the early developing stage and the development of TC from the shallow vortex usually needs a longer time to build the steady deep secondary circulation favorable for more rapid intensification.

This study examines the long-lived concentric eyewall structure of Typhoon Lekima (2019) from an axisymmetric perspective. Possible maintenance mechanisms for the concentric eyewalls are investigated using a high-resolution WRF simulation (nested down to 1-km horizontal grid size). The secondary-circulation responses to the latent heating in the inner eyewall, moat and outer eyewall are diagnosed by solving the Sawyer-Eliassen equation individually to examine the corresponding contribution to the moat downdraft. By calculating the dynamic efficiency factor (DEF), the conversion of latent heating to kinetic energy is evaluated in the moisture-restricted inner eyewall. The Sawyer-Eliassen diagnoses show that the moat downdraft was contributed mainly by latent heating in the inner and outer eyewall, with a secondary contribution from latent cooling in the moat after concentric eyewall formation. DEF diagnoses show that the conversion of latent heating to kinetic energy in the inner eyewall was more efficient than in the outer eyewall. Although tangential wind within the boundary layer was weakened by friction, the compensative tangential wind in the inner eyewall was larger than in the outer eyewall. The compensative tangential wind indirectly accumulated moisture from the sea surface in the moat, aiding the moisture supply to the inner eyewall and enhancing the amount of kinetic energy converted from latent heating. Although the inner eyewall of Typhoon Lekima eventually weakens due to the moisture cut off from the outer eyewall, the inner eyewall can still be maintained for tens of hours by the high DEF from latent heating.

Idealized model simulations have shown that cloud radiative effect (CRE) can accelerate early development of tropical cyclones (TCs). Deep convective region near TC inner cores receives more energy than surrounding environment, and this spatial radiative heating difference can further drive a secondary circulation, transporting angular momentum and water vapor into TC center, thus favoring the TC intensification. However, the quantitative contribution of radiation to TC genesis in observation has not been well documented and need further investigation. In this study, tropical cloud clusters are identified according to their brightness temperature, then classified into developing and non-developing systems based on IBTrACS data. Satellite observations from CloudSat are used to composite vertical structure of radiative heating and atmospheric cloud radiative effect (ACRE), and to verify the differences between these two groups. Preliminary results show that, for developing systems, the upper troposphere absorbs more shortwave but lose more longwave from clouds. While at mid-level, more longwave energy is trapped but less shortwave energy can be transmitted through deep clouds to these layers. Overall, shortwave ACRE drives anomalous upper-level upward motion, and longwave ACRE promotes mid-level upward motion. Both of these effects enhance the secondary circulation between the convective region and their environment. As for non-developing systems, the general characteristics are similar to those in developing systems, except for smaller deep cloud extent and less radiative heating in surrounding area, implying that the deep convection structure, the cloud canopy extent, the associated cloud radiative effect, and the responded vertical motion could be related to the cyclogenesis process. More quantitative analyses, such as Sawyer-Eliassen equation, are still undergoing to further evaluate how secondary circulation and TC genesis process would respond to the radiative heating difference between developing and non-developing systems.

Super Typhoon Hinnamnor (2022) was rare and unique in the record over the western North Pacific as throughout its lifespan it featured all of the major frontier issues in typhoon research currently. Specifically, in different stages of its lifespan, it had a sudden change of track, underwent rapid intensification, interacted and merged with another vortex, expanded in size, underwent rapid weakening and had a strong cold wake, eyewall replacement, and extratropical transition. Therefore, a timely identification and review of these typical features of Hinnamnor (2022), as reported in this article, will help update and enrich the case sets for each of these scientific issues and provide a background for more in-depth mechanistic studies of typhoon track, intensity, and structural changes in the future. We believe that Hinnamnor (2022) can serve as an excellent benchmark to quickly evaluate the overall performance of different typhoon numerical models in predicting its track, intensity, and structural changes.

One of the most prominent diurnal cycle features in tropical cyclones (TCs) is the radially outward propagation of a cooling signal in the upper-level clouds, the so-called diurnal pulse (DP). Previous studies suggested that some of the DPs occur in the deeper convective layer and therefore may impact the TC structure and intensity. This study investigates how the internal structures and intensity will change associated with DPs in global TCs using 18 years of multi-source satellite observations. Over 3000 DP events are identified based on objective method using satellite infrared data. Satellite microwave observations and spaceborne radar measurements are used to examine the changes of the TC convective structures related to DP events. Results show that the precipitation, microwave ice scattering, radar-echo top height, and lightning in the TC inner core are all markedly enhanced on DP days compared to non-DP days. Spaceborne radar observations further indicate that convection becomes deeper in the upshear quadrants with the presence of DPs and convective depths remain similar in the downshear quadrants. These changes in the TC internal structures are consistent to changes in rapidly intensifying TCs reported in the literature. In fact, this study shows that rapidly intensifying TCs have a higher frequency of the long-duration DP event and significantly longer DP duration than steady-state and gradually intensifying TCs. In short, DP implies a coherent pattern of the convective structure in the TC’s evolution and provides important implications for TC intensity change.

The prediction of tropical cyclone (TC) intensity remains a major scientific challenge. Recent studies indicate that cloud-radiation feedback (CRF) plays a positive role in the intensification of TCs during their genesis. However, little attention has been given to how CRF affects TC intensity after genesis. This study shows that CRF may prevents TCs from attaining higher maximum intensities. The ascending motion induced by the anomalous radiative heating of TC promotes more latent heating on the outer side of the upper eyewall, resulting in a more tilted eyewall. A more tilted eyewall leads to a larger inner-core size and less inward flux of absolute vertical vorticity within the inner core, thus preventing the TC from reaching higher intensity. This work highlights that CRF may affect TC intensity by modulating the structure of the inner-core convection, and further advances our understanding of the interaction between radiation effect and TC dynamics

Toward the understanding of rapid intensification (RI) of tropical cyclones (TCs) in the western North Pacific, the TC's deep convective cloud (DCC), precipitation, and cloud properties in terms of cloud effective radius, optical thickness, and top height from satellite observations are investigated. Mean and radial distributions of the variables at different intensity stages and intensification categories are examined. The relationship indicates that the DCC percentage and temperature, especially their radial distributions, could be used to identify an impending RI regardless of TC intensity. Meanwhile, the mean and radial distribution of precipitation may discriminate RI from non-RI in tropical depression (TD) and tropical storm (TS). The radial distribution of the cloud properties in rapidly intensifying TD and TS also suggest that most of the clouds near the center of the storm has deepened already while those that are far from the center are generally in developing or dissipating stage. Moreover, rapidly intensifying TCs, regardless of their intensities, manifest common DCC, precipitation, and cloud properties characteristics near the TC center. It is to be noted that the different mean and radial distribution characteristics of the variables between initial and continuing stages of RI are inferred to be artifacts of their intensities and RI rates (or radius of maximum wind sizes) rather than whether the TCs are at the onset or 24 hr of RI.

The Western North Pacific basin has large uncertainty in future tropical cyclone (TC) frequency projection across climate models, but the underlying reason has been unclear. We show that uncertainty in TC frequency projection is highly correlated with uncertainty in the response of clouds to sea surface warming. Based on the previously developed seed propensity index and gross moist stability theory, we hypothesize that the pattern of cloud response influences TC frequency through its radiative effect on the atmospheric column energy budget. Cloud radiative heating over the Western North Pacific drives anomalous vertical ascent in the large-scale circulation, generating more precursory vortices and seeding more TCs. This hypothesis is supported by numerical experiments with perturbed radiative heating rates and sea surface temperature using global atmospheric models developed at the Geophysical Fluid Dynamics Laboratory. We show that uncertainty in Western North Pacific TC response is reduced when controlling for the radiative heating rate. We further show that cloud and TC response in the Western North Pacific has global impact on the top-of-atmosphere energy budget, using simulations with localized sea surface temperature perturbations. The results suggest that Western North Pacific TC frequency tends to decrease in a scenario with more positive cloud feedback; while the frequency tends to increase in a scenario with more negative cloud feedback.

The Airborne and Satellite Investigation of Asian Air Quality is a multi-perspective field study (aircraft, ground, satellite) planned to take place in early 2024. It is jointly sponsored by NIER (Korea) and NASA (United States) with flights over South Korea as a definite part of the plan and flights over a second partner country still under negotiation at the time of this abstract. The project will make an important contribution to science-based validation of the Geostationary Environment Monitoring Spectrometer (GEMS) which serves to provide an important catalyst for increased dialogue and cooperation among Asian countries to address air quality. In combination with satellite and ground observations, data will support analyses for assessment of emissions, model evaluation, process-level understanding of secondary pollutants (i.e., fine particles and ozone), and satellite validation and interpretation. Aircraft observations can provide invaluable context to the satellite and ground-based perspectives that are used more routinely to inform air quality models used for both forecasting and attribution. Important information from aircraft includes measuring detailed composition for source fingerprinting, vertical profiling of composition for satellite validation and model assessment, observing chemical and dynamical processes affecting secondary pollution (i.e., fine particles and ozone), relating specific VOC mixtures to satellite HCHO, providing fine scale pollution mapping with remote sensors, etc. Such information is critical for understanding the local factors influencing air quality for a specific location, quantifying emission sources, and assessing potential mitigation strategies for decision makers. This presentation will provide an important update on final decisions for deployment locations and planned observing strategies.

Hourly observations of air quality (AQ) over Asia have been available by the Geostationary Environment Monitoring Spectrometer (GEMS) for the first time from a geostationary Earth orbit (GEO) since its launch in February 2020. After 8-month in-orbit tests, GEMS has observed column amounts of atmospheric pollutants (O₃, NO₂, SO₂, HCHO, CHOCHO, and aerosols) to capture their diurnal variations with the UV–visible spectrometer at 0.6 nm spectral resolution and sophisticated retrieval algorithms. Details of the GEMS mission are presented, including calibrations, results, validations, and case studies including volcanic eruption, dusts, and urban pollution. L2 algorithms have been updated for version 2 and the products were released on November 30, 2022. In version 2, there are noticeable improvements in trace gases from updated AMF and the separation of stratospheric/tropospheric components. Ongoing calibration/validation activities including the 2022 GMAP/SIJAQ campaign and international CAL/VAL team works are critical to diagnose and improve the overall data quality. The GEMS retrievals indicate good agreements from the validation campaign, but still require further improvement in L1 processing. We start testing improvements for L1 processing including BTDF correction. Faster sampling rates at higher spatial resolution increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than has been possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) this year and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in late 2024, respectively, as recognized by the Committee on Earth Observation Satellites (CEOS).

Although polar-orbit satellite NO₂ Vertical Column Density (VCD) observations have been widely used for inverse modeling of the NO_x emission inventory, the relative low temporal resolution of polar-orbit satellite data restricts the improvement of the temporal resolution and accuracy of posterior emission inventory. Geostationary Environment Monitoring Spectrometer (GEMS) will provide NO₂ VCD observations with unprecedented spatial and temporal resolution, and it is critical to investigate their impacts on NO_x emission estimates. In this study, Observing System Simulation Experiments (OSSEs) are performed to quantify the improvements of using GEMS high spatiotemporal NO₂ VCD observations to constrain anthropogenic NO_x emission over China in relation to the widely used polar-orbit TROPOspheric Monitoring Instrument (TROPOMI) measurements. Our OSSEs use NO₂ data from WRF-Chem simulations as the “true” atmosphere and sample it with a configuration designed to represent TROPOMI and GEMS. These TROPOMI and GEMS NO₂ “observations” are assimilated through GEOS-Chem adjoint model with the perturbed prior anthropogenic NO_x emissions to obtain posterior monthly and daily emission inventories the corresponding diurnal NO_x emission profiles. The posterior results based on GEMS “observations” are in better agreement with the “true” anthropogenic NO_x emissions used in the WRF-Chem simulations than TROPOMI “observations”.

A novel method developed at the University of Iowa for retrieving aerosol optical depth at high resolution over coastal areas has been implemented using data from the NASA MODIS instrument. This algorithm leverages the spatial autocorrelation of atmospheric properties and the temporal autocorrelation of near-coastal areas to obtain high-resolution retrieved AOD even in areas with significant water-leaving radiance. This code uses MODIS top-of-atmosphere reflectance data and numerous ancillary data sources, and produces retrievals of 550nm aerosol optical depth at the native pixel resolution of MODIS (1km at nadir). This retrieval is applied specifically in coastal areas that are rejected for processing by other MODIS-based algorithms including the Dark Target retrieval. The retrieval code has been ported for near-real-time production by the Naval Research Laboratory. The data have been post-processed to provide the cleanest possible set of observations for data assimilation and incorporate estimated uncertainties required for use in data assimilation. This paper briefly summarizes the novel algorithm, and the skill evaluations that establish its usefulness for characterizing coastal aerosol. The steps taken for near-real-time production are described, including code optimization and linking of near-real-time ancillary data. Post-processing is described, including discussion of the tradeoffs between data coverage and data quality mediated by filtering and aggregation decisions. Estimation of per-observation uncertainties for data assimilation is described. Finally, results from including these observations in the initialization of aerosol forecasts using the Navy Aerosol Analysis and Prediction System are shown, including discussion of the regional, seasonal, and diurnal impacts of this new dataset. The potential for operational use of this product, and more broadly the potential of multi-sensor and hybrid products using both remote sensing and modeling inputs is discussed. 

This study developed an aerosol data assimilation system with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model and Ensemble Kalman Filter (EnKF) approach to improve PM2.5 forecast skill over East Asia. Unlike the variational assimilation method, the advantages of EnKF are flow-dependent background error covariance which is important in a fast-developing air quality system. In spite of considering flow-dependent BEC, the baseline run analysis exhibits poor performance, primarily due to small ensemble spread. This study conducted new two effective methods for increasing ensemble spread: one considering the uncertainty of model physics and the other considering the uncertainty in the prognostic variables. Both methods improved the quality of surface PM analysis substantially, compared with baseline run. And the DA_all experiment which incorporates both uncertainty in model physics and prognostic variables, demonstrates the best performance. The physical perturbation and multiplicative perturbation have a non-linear relationship. The forecast skill is also improved over South Korea and Jing-Jin-Ji area. With the substantial increase of BEC, the revised EnKF system has significantly improved the PM2.5 forecast skills.

Fengyun-3(FY-3) is the second generation of polar orbit series meteorological satellite built by China, with the capability of global observation. The Medium-Resolution Spectral Imager (MERSI) is one of the main loads of the FY-3 series satellites, belonging to the visible infrared band scanning imager. MERSI and MODIS belong to the same type of sensors. Most of the channel designs are similar and have the ability of aerosol retrieval. Based on the principle of MODIS dark target (DT) algorithm, we build a globally applicable land aerosol retrieval algorithm for the new generation of MERSI-II carried by the newly launched FY-3D to test its quantitative capability. Compared with the DT algorithm of MODIS, the algorithm has been improved mainly in two aspects: MERSI's own surface estimation model and inland-water mask method for haze extraction. We have carried out aerosol retrieval tests on global data in 2019 and 2020, and the validation results using the ground-based observation data of AERONET have reached a high overall accuracy close to MODIS aerosol product. We also used the DT algorithm to conduct aerosol retrieval for MERSI over ocean. The global retrieval and validation test in 2019 shows that the validation results have reached an acceptable accuracy. Recently, we plan to develop a new aerosol retrieval algorithm for the bright surface for MERSI, extending the DT algorithm to the bright target. The algorithm has been preliminarily tested in Northwest China as a research area. The results and validation of two years show that this algorithm can realize the retrieval of aerosol optical thickness over the bright surface by MERSI, and the validation accuracy of the retrieval results for the research area is slightly better than MODIS DB and MAIAC aerosol products. 

Atmospheric boundary layer structure plays a critical role in controlling the occurrence and evolution of extreme air pollution episodes. Our knowledge on the boundary layer structure of Nitrogen dioxides (NO2) is still limited. Satellite remote sensing technique measures vertical column density of NO₂ with an extensive spatial and temporal coverage. Recent developments in geostationary satellites (e.g., GEMS) offer new opportunities to observe air pollution with a high resolution of 1 hour. In this study, we applied the GEMS measurements together with various ground measurements to evaluate the variations of the scale height of NO2, which is an important indicator of the vertical structure of NO2. The monthly and diurnal variations of the scale height of NO2 were explored. In particular, the association between the scale height of NO2 and various meteorological factors (e.g., temperature, wind, humidity, air pressure) were evaluated. Our results show that the variations of the vertical structure of NO2 were highly related to the meteorological conditions. For instance, with the subsidence effect of tropical cyclone, the scale height of NO2 were extremely low because most NO2 were accumulated near the ground. We then used random forest regression to estimate the scale height of NO2 based on the meteorological values. Good results with a R2 value of >0.9 were obtained. These analyses enhance our understanding of the vertical structure of NO2 and the relationships between column and surface NO2.

This study investigates the impact of assimilating densely distributed GNSS zenith total delay (ZTD) and surface station data on very short-term heavy rainfall prediction associated with afternoon thunderstorms. A series of sensitivity experiments are applied to multiple cases characterized by intense rainfall rates within 2 hours in the Taipei Basin to identify key assimilation strategies for initializing afternoon thunderstorms. Data assimilation experiments are conducted with a convective-scale WRF-LETKF system, which assimilates the ZTD data and surface station data every 30 min. The results suggest that ZTD assimilation provides effective low-level moisture adjustments. The model generates strong convections due to the large amount of moisture flux over northern Taiwan, and thus heavy precipitation takes place in a short time. When station data is additionally assimilated, the location of the strongest convection is better predicted. Adopting variable- and scale-dependent covariance localization is crucial for optimizing the impact of ZTD assimilation. While the large-scale moisture correction from ZTD assimilation provides the essential thermodynamic precondition of convection development, the small-scale wind correction from surface station assimilation gives the ability to capture the wind direction over the complex terrain, which is decisive for the heavy rainfall location. Increasing the assimilation frequency gives the strongest near-surface convergence in the Taipei Basin. The well-coupled dynamic and thermodynamic conditions trigger extreme convection development in Taipei Basin and lead to a rainfall intensity that best agrees with the observations.

Water vapor (WV) is generally recognized as an essential climate variable and one of the most active components in the atmosphere. Being a typical type of greenhouse gas, the content and migration of WV are greatly associated with the intensity, time and extent of various types of severe weather events, e.g., rainstorm, typhoon and drought. Therefore, it is significant to further refine the existing methods to conduct continuous, timely and accurate monitoring of atmospheric humidity fields. Over the past few decades, on account of the benefits including high spatiotemporal resolution, long-term stability and all-weather capability, the emerging ground-based Global Navigation Satellite Systems (GNSS) atmospheric sounding technique has been widely used to sense WV content in the atmosphere. The GNSS-derived products of zenith total delay and precipitable water vapor have also advanced their usages in improving the accuracy of atmospheric WV fields simulated by numerical weather prediction models. In this paper, a comprehensive investigation on the optimal spatial resolution for assimilating GNSS-derived tropospheric products in the Weather Research and Forecasting (WRF) model to improve atmospheric humidity field is conducted in the context of Australia. By using the strategy of data thinning, the optimal spatial resolution for data assimilation was proved to be 46.4 km. By using the reanalysis data over the study period as the reference, it was found that, with the variational assimilation of GNSS-derived products, both the accuracy of initial field and that of final predictions were greatly improved. Therefore, the research findings not only further corroborate the effectiveness of using ground-based GNSS tropospheric products to improve model performance, but could provide some clues/insights in the development of more robust forecasting models.

The impact of assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) near infrared (NIR) water vapor data on the weather forecasting performance of mesoscale Numerical Weather Prediction (NWP) model, i.e., the Weather Research and Forecasting (WRF) model, is investigated in this study. Three WRF schemes including a no data assimilation scheme and two data assimilation schemes are performed over the South China area for February 2020 and July 2020, respectively. The no data assimilation scheme dose not assimilate any data and is treated as the background run. For the first data assimilation scheme, Precipitable Water Vapor (PWV) data from the MODIS onboard Terra satellite are assimilated into the WRF model. For the second data assimilation scheme, a Back Propagation Neural Network (BPNN) model with assistance of Global Navigation Satellite System (GNSS) PWV is adopted to calibrate the MODIS PWV data. Then, the MODIS calibrated PWV are assimilated into the WRF model. The performance of three WRF schemes are comprehensively assessed by actual observations from the GNSS, radiosonde, and meteorological stations. The results show that: (1) Within the first 12 h after data assimilation, assimilation of MODIS calibrated PWV gains an average PWV forecasting accuracy improvement of 7.6% for February period and 3.3% for July period, while the corresponding improvements of assimilation of MODIS raw (uncalibrated) PWV are 3.6% and 4.1% for February period and July period, respectively. (2) Both assimilation of MODIS raw PWV and calibrated PWV improve the humidity and temperature profile forecasting accuracy, particularly for July period. (3) For July period, after assimilating MODIS raw PWV and calibrated PWV, the accumulated rainfall forecasting success rate for the first 12 h after data assimilation increases from 62.7% to 63.1% and 62.9%, respectively.

Global positioning system (GPS) radio occultation (RO) data has been playing an important role in the new Korea Meteorological Administration (KMA) operational weather prediction system. The Korea Institute of Atmospheric Prediction Systems (KIAPS) delivered the global atmosphere NWP model - named the Korean Integrated Model (KIM) - to the KMA. GPS-RO provides a promising data set with a combination of global coverage, high vertical resolution, and all-weather capability. However, the accuracy of the observation operator in the lower atmosphere (troposphere) - particularly in the subtropical moist marine area - limits the effectiveness of assimilating RO data in these regions. We are currently using the standard Abel transform, which gives the minimum refractivity solution of the continuum consistent with a given RO bending angle (BA) profile, but this is known to give rise to large negative biases in the lower troposphere. As for this issue, we guessed two strategies could relieve the enormous gap of the refractivity (or bending angle) between the observation and the background field over the moist region. The first is to modify the observation operator directly to get more reliable simulation results based on a better understanding of the GNSS-RO features according to the atmospheric structure. And the other is enhancing the quality control (QC) process allows it could more sophisticatedly eliminate the observations that seem highly affected by the moisture.
In this study, we tried to strengthen the impacts of the GNSS-RO, especially over the lower atmosphere, by enhancing the QC process using the humidity information of the background field. And the new QC process was tested in conjunction with applying dynamical observation errors varying with the observation point. Preliminary results from the latest KIAPS DA cycle experiments with these GNSS-RO data processing modifications will be presented.

Precipitable water vapor (PWV), as the medium of land surface-atmosphere energy exchange, plays a crucial role in Earth radiative balance and global climate. However, the applicability of mainstream satellite PWV products is limited by the tradeoff between spatial and temporal resolution and spatiotemporal discontinuity caused by cloud contamination. In this study, we fused the reanalysis-based ERA5 and satellite-derived MODIS PWV to generate accurate and seamless PWV of high spatio-temporal resolution (0.01°, daily) across the continental Europe from 2015 to 2018 based on the generative adversarial network (GAN) model. Firstly, the optimal clear-sky ERA5- MOD05 image pairs are selected based on the image similarity as inputs to raise the fusion performance. Secondly, a flexible multiscale feature extraction architecture is used to capture spatio-temporal variations which primely addresses the non-stationarity of PWV. Finally, the attention-based GAN architecture is used to recover the high-resolution and seamless PWV based on the extracted multiscale features. The results indicate that the fused PWV has great consistency with the GPS retrievals (r = 0.88–0.95 and Bias = -0.89–3.13 mm), showing an improvement in the accuracy and continuity compared to the original MODIS PWV. The fused accurate and spatiotemporal continuous PWV has great potential for meteorological and hydrological analyses.

High-quality satellite remotely sensed water vapor observations present a vital role in Earth’s weather and climate. The Ocean and Land Color Instrument (OLCI) sensor, on-board Sentinel-3A and Sentinel-3B satellite platforms, can observe atmospheric water vapor data records at near-infrared (NIR) measurements. Yet the measurement performance of satellite-sensed OLCI water vapor estimates is generally inferior to that of ground-based and reanalysis-based water vapor observations. In our research, we develop a novel Back Propagation Neural Network (BPNN) based calibration approach to adjust the observation performance of official Sentinel-3 OLCI NIR water vapor products, which considers multiple influence factors that are correlated with the performance of satellite-based NIR water vapor measurements. The in-situ water vapor data records, measured from Global Positioning System (GPS) stations, are used as the desired resulting water vapor estimates. The model is validated based on ground-based water vapor data from GPS and radiosonde instruments. The results show that the BPNN-based adjustment approach can significantly improve the measurement quality of official Sentinel-3 OLCI NIR water vapor data records. Our model can reduce the root-mean-square error (RMSE) of official OLCI NIR water vapor by 24.52% from 3.10 mm to 2.34 mm for Sentinel-3A and 25.00% from 3.44 mm to 2.58 mm when compared to GPS-measured reference water vapor. When compared to radiosonde-observed reference water vapor, the RMSE reduces 21.01% from 4.57 to 3.61 mm and 20.81% from 5.19 to 4.11 mm for Sentinel-3A and Sentinel-3B, respectively. The BPNN-based calibration model exhibits a higher accuracy enhancement on OLCI/Sentinel-3 NIR water vapor observations, compared with other existing previous methods in previous studies. This could be because we have considered multiple dependence parameters in association with the measurement performance of OLCI-retrieved NIR water vapor, while the previous research did not do so.

Tropical cyclones are low pressure systems created by a local heating of the sea surface, which triggers an intense convective activity involving large amounts of water vapor. A deadly tropical cyclone was formed in the early April 2021 over the Savu Sea and was given the name Seroja. This study aims to understand the characteristics of Seroja based on the precipitable water vapor (PWV) as derived from the Global Navigation Satellite Systems (GNSS) data. Observations at 22 permanent GNSS stations, as well as in situ meteorological sensors, in the Nusa Tenggara region from 1 March 2021 until 30 April 2021 were used for this purpose. Surface pressure values dropped significantly by more than 20 hPa, while relative humidity increased and temperature was reduced. Furthermore, during the formation of Seroja, PWV values gradually increased, reaching its peak around the time of when the cyclone was at its closest. After the cyclone had passed, PWV values decreased rapidly, dropping from around 70 mm to below 20 mm at CKUP. PWV pre-Seroja tends to be larger than post-Seroja, whereas post-Seroja PWV was more variable.

This study aims to clarify the spatiotemporal variation, chemical fingerprint, transport routes, and source apportionment of marine fine particles (PM_2.5) at a remote island in the South China Sea (SCS). Field sampling and receptor modeling of PM_2.5 were conducted from September, 2020 to July, 2023. Twenty-four hour marine PM_2.5 was collected at the Dongsha Islands for continuous seven days in each season. Chemical composition of PM_2.5 was analyzed for water-soluble ions (WSIs), metallic content, carbonaceous content, anhydrosugars, and organic acids. Moreover, the potential sources of PM_2.5 and their contribution were further resolved by backward trajectory simulation and chemical mass balance (CMB) receptor model. Three-year field sampling results indicated that high concentrations of PM_2.5 were commonly observed in winter and spring. In terms of chemical composition of PM_2.5, secondary inorganic aerosols (SIAs; SO₄^2-, NO₃^-, and NH₄⁺) accounted for 53.0-64.1% of WSIs which dominated PM_2.5. Crustal elements (Ca, K, Mg, Fe, and Al) dominated metallic content of PM_2.5, while trace elements (V, Ni, and Cu) were originated from anthropogenic sources. The concentrations of organic carbon (OC) in PM_2.5 were generally higher than those of elemental carbon (EC). The highest concentrations of levoglucosan were 20.94 ng/m³ at the Dongsha Islands in winter, while galactosan and mannosan were non-detectable (MDL). Major sources of PM_2.5 resolved by CMB receptor modeling were sea salts (16.6-24.7%), fugitive dust (15.8-23.0%), industrial boilers (6.6-12.0%), secondary sulfate (7-10.9%), mobile sources (4.7-9.6%), waste incinerators (2.4-5.2%), and biomass burning (2.8-5.3%). Keywords: marine fine particles, chemical fingerprints, spatiotemporal variation, transport routes, source apportionment.

Multiphase chemistry of chlorine is coupled into a 3D regional air quality model (CMAQv5.0.1) to investigate the impacts on the atmospheric oxidation capacity, ozone (O3), as well as fine particulate matter (PM2.5) and its major components over the Yangtze River Delta (YRD) region. By including the chlorine chemistry, the model performances in simulating hydrochloric acid (HCl), particulate chloride (PCl), and hydroxyl (OH) and hydroperoxyl (HO2) radicals are significantly improved. O3 is enhanced in the high chlorine emission regions by up to 4% and depleted in the rest of the region. PM2.5 is enhanced by 2~6%, mostly due to the increases in PCl, ammonium, organic aerosols, and sulfate. Nitrate exhibits inhomogeneous variations, by up to 8% increase in Shanghai and 2~5% decrease in most of the domain. Radicals show different responses to the chlorine chemistry during the daytime and nighttime. Both OH and HO2 are increased throughout the day, while nitrate radicals (NO3) and organic peroxy radicals (RO2) show an opposite trend during the daytime and nighttime. Higher HCl and PCl emissions can further enhance the atmospheric oxidation capacity, O3, and PM2.5 so that the anthropogenic chlorine emission inventory must be carefully evaluated and constrained.

Tropospheric ozone is an important greenhouse gas and is central to atmospheric oxidation capacity. We examine observed tropospheric ozone trends, their attributions, and radiative impacts from 1995–2017 using aircraft observations from the In-Service Aircraft for a Global Observing System database (IAGOS), ozonesondes, and a multi-decadal GEOS-Chem chemical model simulation. IAGOS observations above 11 regions in the Northern Hemisphere and 19 of 27 global ozonesonde sites have measured increases in tropospheric ozone (950-250hPa) by 2.7 ± 1.7 and 1.9 ± 1.7 ppbv decade^-1 on average, respectively, with particularly large increases in the lower troposphere (950-800 hPa) above East Asia, Persian Gulf, India, northern South America, Gulf of Guinea, and Malaysia/Indonesia by 2.8 to 10.6 ppbv decade^-1. The GEOS-Chem simulation, driven by reanalysis meteorological fields and the most up-to-date year-specific anthropogenic emission inventory, reproduces the overall pattern of observed tropospheric ozone trends with an increasing trend of 0.4 Tg year^-1 of the tropospheric ozone burden in 1995–2017. Sensitivity simulations show that changes in global anthropogenic emission patterns, including the equatorward redistribution of surface emissions and the rapid increases in aircraft emissions, are the dominant factors contributing to tropospheric ozone trends by 0.5 Tg year^-1. In particular, we highlight the disproportionately large contribution of aircraft which emitting NO_x in the mid- and upper troposphere where ozone production efficiency is high. Regionally, the Chinese nationwide ozone monitoring network has observed rapid increases in warm-season surface ozone in cities during 2013−2019 by about 2.4 ppbv per year, among the fastest urban ozone trends in the recent decade reported in the Tropospheric Ozone Assessment Report (TOAR). Our model simulations reveal that the presence of soil NOx emissions in the North China Plain, mainly driven by agricultural fertilizer applications, present as an underappreciated challenge for ozone mitigation there.

Surface ozone (O₃) is a major air pollutant and draw increasing attention in the Pearl River Delta (PRD), China. Here, we characterize the spatial-temporal variability of ozone based on a dataset obtained from 57 national monitoring sites during 2013-2019. Our results show that: (1) the seasonal difference of ozone distribution in the inland and coastal areas was significant, which was largely affected by the wind pattern reversals related to the East Asian monsoon, and local ozone production and destruction; (2) the daily maximum 8hr average (MDA8 O₃) showed an overall upward trend by 1.11 ppbv/year. While the trends in the nine cities varied differently by ranging from -0.12 to 2.51 ppbv/year. The hot spots of ozone were spreading to southwestern areas from the central areas since 2016. And ozone is becoming a year-round air pollution problem with the pollution season extending to winter and spring in PRD region. (3) At the central and southwestern PRD cities, the percentage of exceedance days from the continuous type (defined as ≥ 3 days) was increasing. Furthermore, the ozone concentration of continuous type was much higher than that of scattered exceedance type (< 3 days). In addition, although the occurrence of continuous type starts to decline since 2017, the total number of exceedance days during the continuous type is increasing. These results indicate that it is more difficult to eliminate the continuous exceedance than the scatter pollution days and highlight the great challenge in mitigation of O₃ pollution in these cities.

A Source- and Age-Resolved Algorithm (SARA) was developed in the CMAQ model and applied to study the sources and ages of primary fine particulate matter (PPM) and secondary inorganic aerosols (SNA) in China during January 2013. Residential and industry are the major contributors to PPM and sulfate, especially for fresh particles. The contributions of power increase with age, accounting for 25% and 51% of aged sulfate and nitrate (> 48 h), respectively. Local emissions and intra-regional transport with younger age contribute to over 70% of PPM and SNA in Beijing, while long-range transport from northern China with older age becomes more significant in Shanghai. On pollution days, the ages of PPM and SNA consistently increase, suggesting enhanced contributions from regional transport. This study highlights that the age-specific particle source and region information produced by our SARA algorithm can help design cost-effective emission control strategies to reduce extreme haze pollution.

Fine plastic particles (particle diameter < 2.5 μm, hereafter FPP) have made considerable contributions to human health, aerosol pollution, climate effects, and many aspects of the ecosystem. However, the direct characteristics and high-time resolution (hourly) quantitative detection of atmospheric FPP were limited. We investigated the composition and probable sources of FPP in PM_2.5 in this study. Dual-hour-resolution was obtained for the quantitative study of FPP in PM_2.5 using the Versatile Aerosol Concentration Enrichment System (VACES) and Thermal Desorption/Pyrolysis-Gas Chromatography-Mass Spectrometry (TD/Py-GC-MS). The FPP in PM_2.5 accounted for an expected value of 5.57 μg/m³ (from 0 to 24.73 μg/m³) while a ratio of FPP to PM_2.5 was 13.2 % (from 4.1 to 42.6 %) in most PM_2.5 samples during the campaign. FPP are not significantly correlated with PM_2.5 yet accounted for in PM_2.5 concentrations, and PAEs are not part of atmospheric particulate matter yet are highly correlated with PM_2.5. Additionally, a comprehensive chemical analysis of the FPP in PM_2.5 has revealed the abundant presence of plastic particles, as opposed to other particles such as soot and tar balls. Heavy pollution of FPP was closely related to local human activities. This work implied that FPP in the air are a growing threat and atmospheric stocks of FPP were strongly related to human activity.

Increasing air pollution levels and associated detrimental health and environmental impacts is a rising concern for citizens of India. National policies and action plans have been sporadically implemented by the government along with various mitigation pathways suggested for further control of air pollution. To effectively combat air pollution, a detailed emission inventory is vital taking into account every sector. The Indian food service industry is one sector that is neglected. The food industry in India is a growing sector, and the unexpected rise due to the high demand in the service delivery market indicates the expected increase in cooking oil fumes. The cooking oil fumes produced as a result of high temperature processes are a major health hazards to human. However, there are limited studies addressing the issue and lack of data related to the sector for construction of a national emission inventory for the food service industry. To develop an inventory for India, firstly the structure of food/restaurant industries in the capital region of the country, megacity Delhi was studied by conducting a survey campaign. Based on the data collected, the emissions from the sector were calculated for Delhi. This study helps understand the pollutant emissions from the food service industry for megacity Delhi which is the hub for the largest number of restaurants in the country. This information can further be extrapolated across all states of India to help construct national emission inventory in future studies.

Underpinned by large-scale air-sea coupled dynamics in the equatorial Pacific, the El Niño-Southern Oscillation (ENSO) makes it the strongest natural interannual variability on Earth with far-reaching socio-economical influences across the globe. Compared to its extensively studied changes in warming scenarios, ENSO responses to anthropogenic forcing removal or reduction, however, remain less explored so far. Based on the 1% per year CO₂ changing for ramp-up and ramp-down experimental scenarios and climate models that capture key ENSO dynamics, here we show two major ENSO sea surface temperature (SST) statistics of variance and skewness exhibit prominent hysteresis responses in a CO₂ changing pathway. In contrast to largely uncertain responses in the ramp-up period, eastern Pacific SST variance and central Pacific SST skewness during the ramp-down period display exaggerated increases and decreases changes, respectively. Such ENSO hysteresis owing to corresponding SST feedback changes is closely associated with a continuously strengthening and eastward-propagating of the El Niño action center until the middle of the ramp-down period. The El Niño action center changes are further linked with similar hysteresis background changes manifested in the intertropical convergence zone, which increase the eastern Pacific atmosphere sensitivity to local SST anomalies during the El Niño initial stage. We also suggest that the presence of El Niño hysteresis would possibly exacerbate its amplified consequential global impacts in a warming world.

 In this study, a comprehensive analysis is conducted to explore the response of global precipitation extremes to CO₂ in terms of hysteresis and reversibility effect and associated population exposure. In this regard, climate outputs under two idealized CO₂ scenarios such as ramp-up (RU; about +1% annually until quadrupling of present level) and ramp-down (RD; around -1% annually set back to present level) from Community Earth System Model version 1.2, and the projected population data from the five shared Socioeconomic Pathways (SSPs) are used. Precipitation extremes are evaluated using the number of heavy precipitation days, maximum consecutive 5-day precipitation, and the precipitation of very wet days indices. Results show that the magnitude of extreme precipitation change and associated population exposure is higher in the CO₂ reduction period (RD) than in RU. All the indices show substantial irreversible and hysteresis effects, ~69% of the global land is expected to experience irreversible changes in precipitation extreme. Further, the hotspots of irreversibility (the region with irreversible change and a large hysteresis) will emerge in >20% of the global area. Spatially, strong hysteresis and irreversibility are particularly concentrated over global land monsoon regions. The leading exposure is estimated under SSP3 combined with both RU and RD periods. Under the SSP3-RD combination, the highest population exposure is estimated at ~67.1% (globally averaged), and ~72% (averaged over hotspots) higher than that of the present day. The exposed population is prominent in South Africa and Asia. Notably, the population change effect is the principal factor in global exposure change, while it is the climate change effect over the hotspots of irreversibility. These findings provide new insight into policymaking that only CO₂ mitigation effort is not enough to cope with precipitation extremes, rather advanced adaptation planning is a must to have more socio-economic benefits.

For investigation in the change of global temperature after the cessation of CO₂ emissions, Zero Emissions Commitment (ZEC) scenario has been proposed. In our four ensemble members of CESM2 ZEC experiment, CO₂ emissions are linearly increased and symmetrically decreased till zero, then this net zero condition is maintained over longer than 100 years. During this net zero emission period, authors found that the ensemble spread of global mean surface temperature (GMST) increases drastically and the ensemble mean also increases again, without increasing CO₂ emissions. The ensemble spread, especially over the northern or subpolar Atlantic Ocean, is associated with differences in the reconstruction of Atlantic Meridional Overturning Circulation (AMOC) within ensembles. This could be explained by AMOC–Salt–Advection positive feedback that a stronger surface salinity derives a weaker stratification of the northern Atlantic Ocean and a stronger AMOC. This raises the stronger meridional warm and salt advection in faster warming ensemble members than slower warming members during the net zero emission period. On the other hand, the positive trend of surface temperature over Southern Ocean (SO) persists during the entire ZEC scenario, thus this plays an important role in the increasing ensemble mean GMST during the net zero period. In contrast, it is noteworthy that the ensemble spread is not as large as the northern Atlantic Ocean or Arctic regions. Authors further found that this positive trend over SO is associated with changes of surface heat budget by the intensified downwelling radiation. From these results, present study argues that the roles of both Northern and Southern hemisphere in ZEC scenario is distinguished in the ensemble spread and mean of GMST, respectively.

When projecting future monsoon changes by carbon dioxide (CO₂) pathway, most studies have analyzed precipitation responses without considering monsoon area (MA) variations. Further, how MA responds to CO₂ removal remains uncertain. This study evaluates MA variations and impacts in idealized CO₂ ramp-up (toward CO₂ quadrupling), ramp-down, and stabilized simulations using the Community Earth System Model version 1. Global MA negatively overshoots (i.e., recovery with decreasing tendency beyond the original MA) during the ramp-down period due to reduced or rapidly recovered MA in several regional monsoons, including Northern and Southern Africa, South and East Asia, and South America, showing hysteresis when comparing ramp-up and -down periods despite similar global warming levels. These non-linear regional MA variations come from distinct regional summer and winter precipitation variations, which are found to be associated with Intertropical Convergence Zone movements and El Niño-like response. Further, regional monsoon precipitation characteristics also vary through ramp-up and ramp-down periods consistently with overall hysteresis. Changes in total monsoon precipitation resemble the distinct responses of MA. Our results suggest that regions characterized by a monsoonal climate may experience reduced seasonal rainfall variations under net-negative CO₂ emissions.

Understanding the regional hydrological response to varying CO₂ concentration is critical for cost-benefit analysis of mitigation and adaptation polices in the near future. To characterize summer monsoon rainfall change in East Asia due to a change in the CO₂ pathway, we used the Community Earth System Model (CESM) with 28 ensemble members in which the CO₂ concentration increases at a rate of 1% per year until its quadrupling peak, i.e., 1,468 ppm (ramp-up period), followed by a decrease of 1% per year until the present-day climate conditions, i.e., 367 ppm (ramp-down period). Although the CO₂ concentration change is symmetric in time, the rainfall response is not symmetric. The amount of summer rainfall anomaly in East Asia is increased 42% during a ramp-down period than that during a ramp-up period when the two periods of the same CO₂ concentration are compared. This asymmetrical rainfall response is mainly due to an enhanced El Niño-like warming pattern as well as its associated increase in the sea surface temperature in the western North Pacific during a ramp-down period. These sea surface temperature patterns enhance the atmospheric teleconnections to East Asia and the local meridional circulations around East Asia, resulting in more rainfall over East Asia during the ramp-down period. This result implies that the removal of CO₂ does not guarantee the return of regional rainfall to the previous climate state with the same CO₂ concentration.

A poleward shift of the Hadley cell (HC) edge in a warming climate has been widely documented. However, its possible change to CO₂ removal has not been explored. By conducting large ensemble experiments where CO₂concentrations are systematically increased and then decreased to the present-day level, we show that the poleward-shifted HC edges in a warming climate do not return to the present-day state when CO₂ concentrations are reduced to the present-day climate. While the Southern-Hemisphere HC edge remains poleward of its present-day state, the Northern-Hemisphere HC edge returns and remains farther equatorward. Such hemispherically asymmetric HC-edge changes, which contribute to drought-prone subtropical regions, are closely associated with the changes in the vertical wind shear. We attribute the vertical wind shear change to the hysteresis in the oceanic response to a changing CO₂ pathway.

The reversibility of South Asian summer monsoon (SASM) precipitation under the CO₂ removal scenario is critical for climate mitigation and adaptation. In the idealized CO₂ ramp-up (from 284.7 to 1138.8 ppm) and symmetric ramp-down experiments, SASM precipitation is largely reversible while exhibiting strong asymmetry: it may overshoot the unperturbed level when CO₂ recovers. Such asymmetric response is mainly due to the enhanced El Niño-like and Indian Ocean dipole-like warming during the ramp-down period. The uneven sea surface warming weakens Walker circulation, with anomalous sinking over the SASM region; meanwhile, the warming also affects the rainfall over the Maritime Continent and tropical western Indian Ocean. The suppressed rainfall over the Maritime Continent triggers the equatorial Rossby wave, which weakens the ascent over the SASM region. The increased rainfall over the tropical western Indian Ocean excites the equatorial Kelvin wave, which reduces moisture transport. Additionally, tropic-wide warming reduces the land-sea thermal contrast and weakens monsoonal circulation. Consequently, the combined effects of the weakened ascent and moisture transport lead to the overshooting of SASM rainfall. Our results suggest that symmetric CO₂ removal, although unlikely in the foreseeable future, may result in a risk of local drought over the SASM region.

In the summer (July and August) of 2022, unprecedented heat wave occurred along the Yangtze River Valley (YRV) over East Asia while unprecedented flood occurred over western South Asia (WSA), which are located on the eastern and western sides of Tibetan Plateau (TP). Here, by analyzing the interannual variability based on observational and reanalysis data, we show evidences that the anomalous zonal flow over subtropical Tibetan Plateau (TP) explains a major fraction the extreme events occurred in 2022. As isentropic surfaces incline eastward (westward) with altitude on the eastern (western) side of the warm center over TP in summer, anomalous easterly (westerly) flow in upper troposphere generates anomalous descent (ascent) on the eastern side of TP and anomalous ascent (descent) on the western side of TP via isentropic gliding. The anomalous easterly flow is extremely strong to reverse the climatological westerly flow over subtropical TP in 1994, 2006, 2013 and 2022. The easterly flow in 2022 is the strongest since 1979, and it generates unprecedented descent (ascent) anomaly on the eastern (western) side of TP, leading to extreme heat wave over YRV and extreme flood over WSA in 2022. The anomalously strong easterly flow over subtropical TP in 2022 is dominated by atmospheric internal variability related to mid-latitude wave train, while the cold sea surface temperature anomaly over the tropical Indian Ocean increases the probability of a reversed zonal flow over TP by reducing the meridional gradient of tropospheric temperature.

Characteristics of extreme precipitation over Yellow River Loop Valley (YRLV) and links to European blocking are investigated in this study. Spatial and temporal analysis of extreme precipitation shows that it contributes more than 30% of the total summer precipitation in the YRLV and is characterized by a strong and short period of local rainfall. Most of the extreme rains in the YRLV occur in July and August. Two typical circulation patterns were identified using a k-means clustering method. The extreme precipitation results from the combined actions of intensified high pressure over northeast China (NECH) and the westward extension of the western Pacific subtropical high (WPSH). The intensified southerly flow of the amplified NECH strengthens the water vapor transport induced by the westward extension of the WPSH from the northwest Pacific or Bay of Bengal into the YRLV. The NECH is amplified by the wave energy propagating from European blocking via the Silk Road pattern (SRP). This is the subseasonal cause of extreme precipitation over the YRLV. The composited July and August mean 500 hPa geopotential anomaly pattern for extreme precipitation years shows a high-pressure anomaly over the European continent and a negative phase of the SRP. The former provides a background for the occurrence of European blocking, and the latter explains the preexistence of the NECH and provides a linkage between the activity of European blocking and the subseasonal evolution of the NECH. Thus, the interannual variation in the extreme precipitation over the YRLV is mainly reflected by the phase of the SRP and the stationary waves over Europe.

Southern China spring rainfall (SCSR) is significant for agricultural sowing and soil moisture accumulation before the rainy summer. A better prediction of the rainfall improves our ability to risk response to natural disasters. It is found that the SCSR can be promoted by multi-year El Niño events through the high-latitude pathway (HP) and low-latitude pathway (LP). The long-lasting El Niño warming heats the tropical troposphere persistently until the decaying spring, which strengthens the Arctic polar vortex and the mid-latitude blockings. This HP is in favor of more southward transport of Rossby wave energy and cold air, resulting in strong ascending motions over southern China in spring. The multi-year El Niño also induces an enhanced western North Pacific anticyclone and a secondary circulation transporting moisture to southern China through the LP. The HP is more important in the early spring, while the LP dominates the heavy SCSR in the late spring.

Investigation into the interannual variation of the Middle East jet stream (MEJS) and its influence on the Asian monsoon indicates that the eastward extension of MEJS is closely related with a wetter and colder winter in southern China and a later onset of the subsequent Asian summer monsoon, compared with normal conditions. When the MEJS extends eastward, a significant barotropic anomalous anticyclone is located over the Arabian Sea (AS), associated with the southeastward propagating wave train from Europe. Intense divergence in the southwest of the AS anomalous anticyclone favors more convection over the western tropical Indian Ocean, which excites an anomalous upper-level anticyclone to the north as a Rossby wave response, further intensifying the AS anticyclonic anomaly. This positive feedback loop maintains the AS anomalous anticyclone and results in the eastward extension of the MEJS. Accordingly, intense northeasterly anomalies over the Mediterranean Sea and the subtropical westerly anomalies bring abundant cold air from the middle-higher latitudes to subtropical regions, resulting in a widespread cooling in subtropical Eurasia including southern China. Barotropic anomalous westerlies occur around the Tibetan Plateau in the south and deepen the India-Burma trough, favoring more water vapor transport from the Bay of Bengal to southern China. This wetter and colder conditions in subtropical Eurasia can persist from winter to spring, leading to the much later onset of the Asian summer monsoon. Therefore, the winter MEJS variability can be considered as an important indicator for the Asian monsoon.

The towns of Cherrapunji and Mawsynram, located on the southern slopes of the Meghalaya Plateau in the northeastern part of the Indian subcontinent, are known to have the heaviest rainfall in the world. The Indian Meteorological Department announced that 972 mm of rain was recorded in the 24 hours ending 03 UTC on 17 June 2022. This was the third rain in the last 122 years. Severe flash floods occurred downstream in Sylhet, Bangladesh. Such case studies of heavy rain events must be useful in understanding the mechanism of heavy rain and in considering countermeasures against flash floods. An automatic weather station installed at Cherrapunji and an optical disdrometer installed at Sylhet recorded rain for the heavy rain event. The rainfall for the three days from the 14th to the 16th was 2412 mm, and it was 43% of the monthly rainfall in June 2022. Precipitation showed a distinct diurnal variation and increases from midnight to early morning. On the other hand, in Sylhet, which is located in the plains 40 km southeast of Cherrapunji, the precipitation variability was completely different and the diurnal variation of precipitation was unclear. The precipitation system was characterized by the images of Bangladesh Air Force (BAF) C-band radar at Jessore, Bangladesh, and the Global Satellite Mapping of Precipitation (GSMaP) product. The precipitation system was a topographical system localized in southern Meghalaya. The precipitation system on the 15th was not sufficiently detected by the BAF radar, suggesting that the echo top height was relatively low. While the GSMaP showed some agreement with the BAF radar, on the 14th it was not possible to properly detect the onset of the nocturnal rain around 12UTC. This was because the data from the satellite-borne microwave radiometer observations used to create the GSMaP were not available within 3 hours.

Heatwaves often cause immense stress on human society and the natural environment. While heatwaves can be classified into daytime, nighttime, and compound daytime-nighttime types, the specific processes associated with different heatwave types remain poorly understood. In this paper, we identify different mechanisms operating in compound (i.e., extreme heat during both day and night) and independent daytime and nighttime heatwaves in southern China. Compound heatwaves generally exhibit stronger temperature increases than either daytime or nighttime types. Daytime heatwaves are accompanied by increased downward shortwave radiation under a clear sky with reduced cloud cover and moisture. Nighttime heatwaves are characterized by more cloudy and moist conditions, and increased downward longwave radiation at the surface at night. A combination of these conditions for daytime and nighttime heatwaves prevail during compound heatwaves. All heatwaves are associated with strengthening and eastward extension of the South Asian high (SAH) in the upper troposphere, and strengthening and westward extension of the western North Pacific subtropical high (WNPSH) in the lower and middle troposphere. Further examinations suggest that compound heatwaves are accompanied by the strongest intensification of SAH and WNPSH. Compared with daytime heatwaves, nighttime events are associated with a stronger amplitude of SAH and WNPSH, and both highs tend to extend more southward when nighttime heatwaves occur. This southward extension induces an anomalous lower-level anticyclone that drives a southwesterly wind anomaly over southeastern China. This circulation feature transports warmer and more humid air towards southern China. The enhanced concentration of water vapor leads to increased absorption of outgoing longwave radiation, and increased re-emission of longwave radiation to the surface, thus resulting in surface warming at night.

Extremely heavy rainfall occurred over both Northwest India and North China in September 2021. The precipitation anomalies were 4.1 and 6.2 times interannual standard deviation over the two regions, respectively, and broke the record since the observational data were available, i.e., 1901 for India and 1951 for China. In this month, the Asian upper-tropospheric westerly jet was greatly displaced poleward over West Asia, and correspondingly, an anomalous cyclone appeared over India. The anomalous cyclone transported abundant water vapor into Northwest India, leading to the heavy rainfall there. In addition, the Silk Road pattern, a teleconnection pattern of upper-level meridional wind over the Eurasian continent and fueled by the heavy rainfall in Northwest India, contributed to the heavy rainfall in North China. Our study emphasizes the roles of atmospheric teleconnection patterns in concurrent rainfall extremes in the two regions far away from each other, and the occurrence of rainfall extremes during the post- or pre-monsoon period in the northern margins of monsoon regions.

Emissions from biomass burnings, cause large impacts on our society and environment and cause local to global scale issues. The properties of smoke particles from these emissions rapidly evolve at seconds to minutes scales. Being able to accurately measure the smoke absorption properties is essential when quantifying the aerosol impacts on radiation budgets and mesoscale dynamics and help improving the forecast as well as facilitate risk management. In-situ measurements can provide accurate measurements of aerosol properties but have limited sample size. From satellites, which have much wider temporal and spatial coverage, the fast-changing absorbing properties of smoke plumes are very challenging to retrieve. Utilizing the very fine temporal resolution observations from geostationary satellites and the powerful critical reflectance (CR) methods, we now have a pathway to retrieve wavelength dependent absorption at 10 minutes intervals. CR is a surface reflectance at which increasing or decreasing aerosol loading does not change the TOA reflectance. This reflectance value is highly associated with the absorption of the aerosol layer and not related to lower boundary conditions, which makes it suitable to apply over heterogeneous land surfaces. Using Advanced Base Imager onboard GOES-17, we demonstrated the power of CR technique by retrieving aerosol single scattering albedo on two fire events, following the track of freshly emitted smoke plumes, and compared with nearby ground-based observations. The uncertainty of the CR method is also tested using sensitivity studies. This method will be applied to all suitable fire events and the temporal and spatial variation of retrieved SSA for each case will be analyzed and compared to other aerosol absorption products. 

In the context of climate change, fire danger conditions are expected to increase in many regions of the world due to the projected changes in climate as the occurrences of biomass burning are events highly dependent on the meteorological driver. The threat from biomass haze in Malaysia and Indonesia will not only be about frequency but also intensity. There is a possibility that biomass burning will become more severe in the future. However, while the study of the near-real-time predictions of burning hotspots in Malaysia and Indonesia has been developed widely, the study of its projection has not been assessed. This study aims to project burning hotspots that are represented by FWI (Fire Weather Index), a fire danger system that relies upon the daily weather readings during the previous day, including temperature, relative humidity, wind speed, and rain. The projection is performed based on the future climate condition (2041 – 2070) under RCP4.5 and RCP8.5 scenarios using the downscaled simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX-SEA). The result of the projection of trends of the fire hotspots by applying the FWI shows an overall significant increase in fire activities under the RCP4.5 and RCP8.5 scenarios in the future. The average FWI reached 9.6 and 11.1 under RCP4.5 and RCP8.5 scenarios, respectively. The average percentage increase of FWI in the future under the RCP8.5 scenario is 43.9%, higher than under the RCP4.5 scenario, which is 39.7%. In short, the result of this study provides an understanding of how fire events condition in the future as the efforts to control the forest and land fire disasters which affect the biomass burning haze.

Research has shown that short term events, such as forest-fires, agricultural-burning, dust-storms, fireworks, etc., significantly contribute to air-pollution, and impact the climate, air-quality, visibility, human-health, etc. Festivals that are marked by firework displays and wood-log fires are among these transient events that severely alter the local air-quality. In India, two major festivals, Diwali and Holi, are celebrated throughout the country, and are dominated by fireworks and wood-log/ biomass burning, respectively. As per the religious belief, Holi is preceded by Holikadahan, when wood-log/ biomass is burned in the last evening. The burning results in the emission of huge amounts of gaseous and particulate pollutants. Innumerable point sources Holikadahan in the vicinity make Holikadahan a huge polluter on a regional basis.The study, spanning six years (2017-2022), was conducted using data collected from 12 cities, extending from west to east in the Indo-Gangetic plains (IGP) to examine the impact of Holikadahan by comparing the period of background levels, pre-Holikadahan, and Holikadahan of PM2.5, PM10 and aerosol optical depth (AOD). As a general observation, the highest spike in pollutant concentrations, including PM2.5, PM10 were observed during the Holikadahan and immediately after the Holikadahan, followed by a decreasing trend, back to background levels, typically after 24-hours.In the western IGP, the maximum impact of Holikadahan on air-quality was observed in 2021, showing 71%, 63% and 82% increase in PM2.5, Pm10, and AOD values, respectively, between pre-Holika and Holika. Similar increases in central IGP, observed in 2021, were 80%, 25% and 55%, respectively. In the eastern IGP, however, the maximum impact was observed in 2020, with 74% and 75% increase in PM2.5 and PM10 concentration, respectively. This increase in primary data is expected to change the localized air-quality index (AQI), aerosol radiative forcing efficiency, and atmospheric heating-rate.

For realizing a weather-controlled society, we need to discuss the way to maximize the effect of manipulations to the atmosphere. For that purpose, this project aims at developing methods that quantify weather controllability and mitigatable flood damage based on ensemble weather forecasts. To quantify weather controllability, this project investigates meteorological landscapes that separate disaster and non-disaster regimes which may be controllable with small manipulations. We also estimate economic damages under non-controlled/controlled scenarios, in order to quantify avoidable damage by weather control. We have started illustrating directed graphs as the first step in understanding the meteorological landscape. Typhoon Prapiroon in 2018 was used for the case study. Singular value decomposition (SVD) is employed for Japan Meteorological Agency’s operational meso-scale ensemble prediction data to extract principle components of atmospheric states, followed by a clustering using density-based spatial clustering of applications with noise known as DBSCAN. The illustrated graph succeeded in detecting separated two clusters that correspond to faster and slower movements of predicted Parapiroon. The developed algorithm is currently applied to other disastrous events as well as further investigations on non-linear data compression methods beyond SVD. This presentation includes the most recent achievements up to the time of the conference.

Ensemble sensitivity has been proved to be a very useful sensitivity measure in practice. In this study, we show the relevance of ensemble sensitivity in another important problem. Instead of estimating how each state element will change a forecast response, we now examine which analysis perturbation among all possible ones with the same magnitudes will yield the largest change in the forecast response. We have proved that changes of the forecast response are maximized along the direction of the vector consisting of ensemble sensitivities which forms the most sensitive perturbation. This fact is found to be applicable in the field of weather controllability. In the simplest problem of weather controllability, we concern the optimal perturbation that minimizes a damage cost over a critical area. This optimization problem can be solved with variational data assimilation techniques by including the damage cost into the cost function. However, the problem becomes more complicated if we consider two important factors: (1) the power of optimal perturbation is limited by our technology so that it is impossible to realize the optimal perturbation in reality; and more importantly (2) we need to control the damage not just over an area but over several areas and it is possible that the damage only redirected from an area to another area under modification. We will demonstrate how the new understanding on ensemble sensitivities can qualitatively give potential solutions for such complicated issues.

Forecast sensitivity has been formulated to identify fast-growing perturbations in ensemble forecast. The ensemble singular vector (ESV), which doesn't require the tangent-linear and adjoint models, is derived in this study to obtain the critical initial condition that affects the ensemble forecast of high-impact events. While past researches mainly use this method in large-scale systems, this research examines the feasibility of ESV for long-lived heavy rainfall associated with synoptic-mesoscale fronts prediction. All of three cases show large rainfall forecast uncertainty in targeted area at validation time. The ESV is calculated using the metric of rainfall accumulation in the target area to evaluate the final forecast. To validate the evolution of ESV, the leading ESV is compared with the post forecast sensitivity defined by the difference between the members with high- and low-performance rainfall forecast in each case. In addition, the leading ESV is used to perturb the initial condition of the ensemble forecast to investigate the impact of ESV on rainfall forecasts. The preliminary result with a Mei-Yu front associated heavy rainfall event shows that the rainband can be effectively adjusted with the perturbed forecasts. Furthermore, , we confirm that the final ESV agrees with the evolution of the ESV-based initial perturbations under the non-linear model dynamic to a great extent. Finally, the effectiveness and challenges of applying ESV will be shown and discussed in this presentation. 

The national project in Japan, the Moonshot Research and Development Program sets ambitious goals, and Goal 8 is “Realization of a society safe from the threat of extreme winds and rains by controlling and modifying the weather by 2050”. It is expected to develop weather control technology that is technically, ethically, legally and socially feasible. Between 1962 and 1983, research in hurricane modification centered on an ambitious experimental program, Project STORMFURY in the USA. In Japan, research on weather modification became active under the influence of Project STORMFURY. In this study, we investigated the weather modification research in 1960s and 1970s in Japan and the USA by looking through all the references and interviewing people. The main purpose of the project STORMFURY was to conduct cloud-seeding experiments within hurricanes. Unfortunately, during the Project, the project team didn not take good opportunities and was only able to conduct cloud-seeding experiments three cases. To increase experimental opportunities, the USA government proposed to Japan and other countries over the northwestern Pacific Ocean. The possibility of experimenting in the Pacific was considered, but it did not materialize. In Japan, weather modification research had been active for 7 years, but rapidly declined in 1971. In this presentation, we will report the reasons why the experimental relocation did not materialize and why the weather modification research declined in Japan. The USA had discussed the experimental relocation with countries concerned, but the results were not satisfactory and the deadline for reporting to the Navy was not met. In Japan, declining of weather modification research can be attributed to the 1971 Kawasaki accident, 15 people died in landslide experiments on artificially slopes. The members leading weather modification research at the time were involved in that accident. This research was supported by JST Moonshot R&D Grant Number JPMJMS2282.

A project ”Moonshot Goal 8” supported by Japan Science and Technology Agency was established to study the possible weakening of typhoon intensity due to artificial interventions. One such measure is to increase the sea surface drag by using obstacles such as large ships. The maximum potential intensity theory suggests that the equilibrium intensity decreases as the surface drag coefficient increases if the surface enthalpy exchange is unaffected, but numerical studies to test it are limited. Previous fine-resolution simulations (e.g., with a sub-kilometer grid) tend to agree with the theoretical indication, but the number of cases is limited. Studies with coarse-resolution models exhibit mixed results. Also, no studies have been conducted to elucidate the effect of surface drag coefficient change in a limited oceanic region. Therefore, we aim to conduct a comprehensive study on how tropical cyclones would react to surface drag change over limited regions that can be set in various ways. As a first step, we conducted preliminary numerical simulations of Typhoon FAXAI in 2019 by changing the drag coefficient (C_D) over the whole simulation area. Here we ran the Scalable Computing for Advanced Library and Environment (SCALE) at a coarse resolution of 5 km. The resultant minimum central pressure was nearly insensitive to C_D, but the maximum winds were weakened by about 60 % of the control run (CTL) when C_D was set to 1.5 to 3.5 times that in CTL. Also, the radii of average winds of 15ms^-1 were decreased by about 20 %. We will conduct further studies until the meeting. This research was supported by JST Moonshot R&D Grant Number JPMJMS2282.

A project of Moonshot Goal 8 supported by Japan Science and Technology Agency is to study the possibility to reduce typhoon intensity by artificial interventions. Ocean thermal energy conversion (OTEC) which is planned to be established in the ocean near Okinawa islands is considered one of artificial interventions. The OTEC technology collects cold deep sea water at a depth of 800m and warm sea water at a depth of 20m, and discharges this mixed water to a depth of 20m. Namely an OTEC decreases the sea surface temperature (SST). When a typhoon passes over the ocean where the SSTs are decreased by OTEC, it is considered that the typhoon intensity is lower than without an OTEC. The purpose of this study is to conduct a numerical simulation of real typhoons using an atmospheric model with a SST distribution simulated by ocean model including the effects of OTECs (OTEC experiment), and to evaluate the impact of a SST forcing in the OTEC (sensitivity experiment). In the case of 2020, Typhoon HAISHEN passed through the OTEC influence zone, and the central pressures of HAISHEN were 930hPa by the Best Track data created by the Regional Specialized Meteorological Center Tokyo-Typhoon Center, 924.4hPa by our experiment without OTECs effect, and 924.6hPa by the OTEC experiment with the cooled SST (from -0.05 to -0.02℃) spread from each point of OTECs to the north for 50 to 70 km (OTEC influence zone). In the sensitivity experiment, the central pressures of Haishen increased by 0.57hPa and 1.00hPa at SST forcing with -0.5℃ and -1.0 in the OTEC influence zone, respectively. The sensitivity experiment of the OTEC or SST forcing that effectively affects the other cases of typhoons will be investigated. This research was supported by JST Moonshot R&D Grant Number JPMJMS2282.

Understanding the sea surface wind structure during tropical cyclones (TCs) is the key for study of ocean response and parameterization of air-sea surface in numerical simulation. However, field observations are scarce. In 2019, three wave gliders were deployed in the South China Sea and the adjacent Western Pacific region, which acquired sea surface wind structure of eight TCs. Analysis of the field data suggests that the wave glider-observed surface winds are consistent with most analysis/reanalysis data (i.e., ERA5, CCMP and NCEP-GDAS) and SMAP. Both wave glider observations and analysis/reanalysis data indicate that TC wind fields induce an obvious increase in speed toward the sea surface together with a sharp change in direction, showing an asymmetric wind structure which is sensitive to TC translation speed and intensity. Larger mean values of wind speed and inflow angle are located on the right side along TC tracks. The inflow angle shows a highly dynamic dependence on the radial distance from the TC center, the TC intensity, as well as the TC-relative azimuth. Comparisons between field observations and theoretical models indicate that the most widely used, ideal TC wind profile models can largely represent the observed sea surface wind structure, but generally underestimate the wind speed due to lack of consideration of background wind. Moreover, simple ideal models (e.g., the modified Rankine vortex model) may outperform complex models when accurate information of TCs is limited. Wave glider observations have potential for better understanding of air-sea exchanges and for improvements of the corresponding parameterization schemes.

Eyewall Replacement Cycle (ERC) is often seen in TCs. ERC occurs when secondary eyewall forms outside the inner eyewall, and the inner eyewall disappears. ERC significantly impacts TC intensity, so revealing the mechanism is an important issue both scientifically and socially. Several mechanisms of secondary eyewall formation have been proposed. According to Huang et al. (2012), tangential wind enhancement associated with lower-level inflow causes secondary eyewall formation. The relationship between the mesoscale descending inflow (MDI) formed by diabatic cooling of stratiform precipitation areas and secondary eyewall formation has also been pointed out (Didlake et al. 2018). However, the detailed processes by which dry air inflow and diabatic cooling affect secondary eyewall formation through MDI are not yet well understood. Therefore, in this study, the role of dry air inflow from the middle and upper troposphere and diabatic cooling in secondary eyewall formation is investigated using numerical experiments. Idealized numerical experiments were conducted using the plane version of the nonhydrostatic icosahedral atmospheric model, NICAM. Control experiments confirmed the existence of dry air inflow in the middle and upper troposphere and the formation of downdrafts due to diabatic cooling. It was also confirmed that the mechanism of secondary eyewall formation by agradient force was working, as pointed out in previous studies. In a sensitivity experiment, we conducted an experiment to increase water vapor in the middle and upper troposphere outside the TCs. The results showed that the secondary eyewall formation was hindered and slowed down as the water vapor increased. We further conducted a realistic case study for Typhoon Haishen in 2020 and confirmed the above mechanism works for the ERC of Haishen.

Doppler weather radars are a powerful tool for investigating the inner-core structure of tropical cyclones (TCs). The Doppler velocity from a single radar has no information on the wind component normal to the radar beam. Therefore, closure assumptions are needed to estimate the circulations of the TCs from single-Doppler radar observations. The Generalized Velocity Track Display (GVTD) technique used to estimate the TC circulations adopts the closure assumption of no asymmetric radial winds in the TC vortex.
The present study proposes a new closure assumption introducing asymmetric radial winds to improve the axisymmetric-circulations estimation in TCs with asymmetric structure in the GVTD retrieval formula. Our new method can consider the asymmetric radial winds by using streamfunction based on the Helmholtz decomposition theorem of horizontal winds. As with GVTD, the new method retrieves TC circulations based on the Fourier decomposition of winds in the azimuthal direction and the least-square fit of the Doppler velocity from the single radar observation. The new method and GVTD are applied to analytical vortices and a real typhoon. For the analytical vortices with asymmetric winds in wavenumber-2 vortex Rossby waves, the axisymmetric tangential wind of V_T0 retrieved by the new method (GVTD) has a relative error of less than 2% (10%) near the radius of maximum wind speed. For the real typhoon, the GVTD-estimated V_T0 has periodical fluctuations with an amplitude of about 5 m/s near the elliptical eyewall. The period of the fluctuations is approximately synchronized with the counterclockwise rotating period of the elliptical shape of the eyewall, suggesting pseudo-signals due to the closure assumption in GVTD. The periodical fluctuations are largely reduced in the estimated V_T0 from the new method. We find that the new method can reduce the pseudo-signals of the GVTD-retrieved axisymmetric circulation in cases of asymmetric vortices.

Accurate prediction of rainfall distribution associated with tropical cyclone (TC) is very important for risk management but remains challenging due to complicated multi-scale interactions. Previous studies mostly focus on the precipitation associated with a single TC but little attention has been paid to the features of precipitation in binary tropical cyclone system. In this study, based on the satellite observations, a novel approach with rotated coordinate system is proposed to examine the binary TC interactions over the western North Pacific and the associated asymmetry of rainfall distributions. The results show that the asymmetric component of TC rainfalls strengthened with the decreasing of the separation distance between the two TCs. The results show that the asymmetric component of TC rainfalls enhances with the decreasing of the separation distance between the two TCs. A critical separation distance at about 2050 km for binary TCs could naturally emerge based on the relationship between the asymmetric rainfall component and the separation distance. This can be an observational reference for estimating the strength of binary-TC interaction and identifying binary TCs from two coexisting TCs. When two TCs become nearby, the asymmetric component of rainfall shows an increasing trend with rainfall significantly suppressed in Quadrant IV of the TC located to the west when orienting the two TCs in the west-east direction. The suppression becomes remarkable once the separation distance between the two TCs is within about 2,050 km. It is found that the convective activity in one TC is related to the deep-layer vertical wind shear (VWS) from its companion. Rainfall is enhanced downshear-left in a TC, consistent with a single TC embedded in an environmental VWS as found in previous studies. Our study suggests that the evolution of rainfall distribution under the binary-TC conditions could be potentially predictable at vortex scale.

Various aspects of tropical cyclones (TCs) fluctuate with the diurnal cycle. In this paper, diurnal variations in TC intensification and the growth of the radius of gale-force winds (34 kt; R34) over the Ocean for in global TCs were investigated using best-track data. Statistically significant diurnal variations were found for global TCs, with a maximum intensification over the period 03–09 local solar time (LST). No diurnal signals are detected for TC decay. Statistically significant diurnal variations are found for global TCs which experienced rapid intensification (RI, ≥30 knots within 24 h), with a maximum 6-h growth rate occurring at 03–09 LST. The highest rate of intensification and R34 growth rates at 03–09 LST for TCs were associated with the greatest coverage of very deep convective clouds with infrared brightness temperatures < 208 K at 03–06 LST. R34 growth is favored at the same time as TC intensification. This study suggests that nocturnal radiative cooling affects the intensity and outer region sizes.

Size of tropical cyclone (TC) is often asymmetric in nature. Yet, there is a lack of clean and intuitive definition/expression to specify the asymmetry of TC size. Here, we introduce a novel index, TC size asymmetry index (SAI), which indexes both the degree and pattern of the asymmetry systematically. In particular, the symbolic form of SAI is vividly designated for identifying the latter. The SAI proposes 1 quasi-symmetric pattern and 28 asymmetric patterns in total. The 41-yr (1979–2019) global climatology of SAI shows that the distribution of the degree of TC size asymmetry is trimodal. Elementarily, the degree and pattern of TC size are found to be TC intensity, TC movement, time, and space dependent. The introduction of SAI does not only give an insight into the subject of TC size asymmetry but also lays important foundation for future applications and research. Furthermore, besides meteorology, it could inspire other fields to index the geometric asymmetries of other kinds.

Tropical Cyclone Debbie (2017) made landfall near Airlie Beach on 28 March 2017 causing 14 fatalities and an estimated US$2.67B economic loss and was ranked as the most dangerous cyclone to hit Australia since TC Tracy in 1974. In addition to the extreme flooding as TC Debbie moved onshore and down the east coast of Australia, it intensified rapidly just offshore from Category 2 to Category 4 in approximately 18 hours and finally made landfall as a Category 4 TC, causing widespread and disastrous damage. A high-resolution WRF simulation (1-km horizontal, and 10-min temporal resolution) is used to analyze the inner-core structure and evolution during the offshore rapid intensification period in the current conditions and potential future change. In current condition, Debbie’s a rapid intensification (RI) stage is characterized by three rounds of eyewall breakdown into mesovortices and re-development events. Each round of breakdown and re-establishment brings high potential vorticity and equivalent potential temperature air back into the eyewall, re-invigorating eyewall convection activity and driving intensification. The potential future changes in the inner-core structure and eyewall evolution will also be discussed using WRF with the Coupled Model Intercomparison Project Phase 6 (CMIP6) perturbed conditions to better assess the possible TC intensity change under different climate change scenarios. 

Using the tropical cyclone best track data from Shanghai Typhoon Institute of CMA of 1949-2020 and the reanalysis data of the European Centre for Medium-Range Weather Forecasts of 1991-2020, and the joint EOF analysis of u and v components of the wind field on the 200 and 850 hPa, the characteristics of the larger-scale circulation at onset of the rapid-intensification typhoon are summarized, and the evolutions of environmental dynamic and thermal conditions during the ±12 h period at the onset of typhoon rapid intensification are further analyzed. The results indicate that the lower level of the main circulation of EOF decomposition is the convergence pattern of monsoon trough at the onset of the typhoon rapid intensification, and the circulation is conducive to the low-level water vapor transport of typhoon. The upper-level circulation has obvious typhoon outflow channels, and the characteristic can be used as a typical circulation for the rapid intensification forecast. The thermal conditions such as sea surface temperature, water vapor and convective instability, as well as dynamic conditions such as environmental vertical wind shear and the strength of upper-level outflow all can generally reach the fitness range of conditions that are favorable for typhoon intensification. However, the values of above environmental factors have not changed significantly or suddenly during the transition from the slow intensification process to rapid intensification process. Even some extreme cases show that the change tend of environmental factors towards to the unfavorable conditions. These research results provide a reference for the prediction of rapid intensification and further study of typhoon in the future. As for the unfavorable conditions in RI some cases, whether there are other favorable factors to offset the negative effects of these conditions and what are the corresponding physical processes, these issues need to be further studied in the future.

The AERONET program is a federation of ground-based sun-photometer networks and operates in partnership with various national and international agencies, universities, institutions, research groups, and individual scientists and stakeholders. It provides worldwide observations of spectral aerosol optical depth, inversion products (microphysical and radiative properties), and precipitable water across diverse aerosol regimes. In addition, other network components enable measurements of normalized water-leaving radiance, solar flux, and marine aerosols. For the past three decades, AERONET has been providing long-term, continuous, and readily accessible public-domain datasets. The network has been growing steadily, with currently about 600 active sites. The program also supports research and application field campaigns around the world. The program continues to refine instrumentation, measurement techniques, and algorithms through research and development. AERONET has recently started making nighttime aerosol measurements using direct moon observations. The program's biggest strength is its imposition of standardized of instruments, calibration, quality control, processing, and data distribution. This presentation will provide an overview of the program with examples of available datasets, current research, and applications. We will also highlight important upcoming satellite missions, field campaigns, and new research results from the AERONET team at NASA Goddard. The presentation will highlight ongoing data analysis focused on the Asia-Pacific region by the GSFC team.

The GEMS validation campaign was conducted in the Seoul Metropolitan Area (SMA) from October to November, 2021. Twenty-four research teams participated in four platforms: ground remote sensing, chemical sonde, ground and airborne in situ chemical analysis, and chemical transport modeling. In particular, 5 Pandora and 5 MAX-DOAS were deployed within SMA, and 3 Car-DOAS were operated throughout SMA. Preliminary results showed that diurnal variations in the NO2 and HCHO columns in ground remote sensing matched well with those in GEMS. Car-DOAS yielded pollution maps in detail, which were used for subgrid variability analysis. The various shapes in the vertical profile of NO2 retrieved from MAX-DOAS and directly measured from the airborne NO2 monitor demonstrate the importance of A prior in GEMS VCD calculation.

The intensive field observations were conducted in central to southwestern Taiwan to clarify the physical and chemical processes and emission sources that lead to the serious PM_2.5 problem. The deployment of the atmospheric observations included the wind profiler, radiosonde, unmanned aerial vehicle (UAV), lidar, and surface datasets. These observations provide the wind fields, temperature, humidity, and aerosol information with high temporal and vertical resolutions in the planetary boundary layer (PBL). In addition, the PBL ensemble data assimilation system was developed based on the Weather Research and Forecasting Local Ensemble Transform Kalman Filter (WRF-LETKF) framework coupled with the Community Multiscale Air Quality (CMAQ) model. We applied WRF and CMAQ models, and observations, to characterize the PBL vertical structures, evolutions, and air pollutants transport and dispersion processes. The preliminary analysis of the observation and model results demonstrate that the development of the PBL depth was limited due to the enhanced atmospheric stability in the PBL and the strong subsidence process from the layer above. The coastal sites revealed a shallow PBL depth; yet, the inland sites revealed a well-mixing PBL structure during the day. The lidar and radiosonde revealed distinct layer structures of the PM2.5 concentrations. The model can reproduce the observed features and assist in discussing the air pollution transport processes.

A recently developed GRASP/Component approach (GRASP: Generalized Retrieval of Atmosphere and Surface Properties) was applied to AERONET (Aeronet Robotic Network) sun photometer measurements in this study. Unlike traditional aerosol component retrieval, this approach allows the inference of some information about aerosol composition directly from measured radiance, rather than indirectly through the inversion of optical parameters, and has been integrated into the GRASP algorithm. The newly developed GRASP/Component approach was applied to 13 AERONET sites for different aerosol types under the assumption of aerosol internal mixing rules to analyze the characteristics of aerosol components and their distribution patterns. The results indicate that the retrievals can characterize well the spatial and temporal variability of the component concentration for different aerosol types. A reasonable agreement between GRASP BC retrievals and MERRA-2 BC products is found for all different aerosol types. In addition, the relationships between aerosol component content and aerosol optical parameters such as aerosol optical depth (AOD), fine-mode fraction (FMF), absorption Ångström exponent (AAE), scattering Ångström exponent (SAE), and single scattering albedo (SSA) are also analyzed for indirect verifying the reliability of the component retrieval. It was demonstrated the GRASP/Component optical retrievals are in good agreement with AERONET standard products [e.g., correlation coefficient (R) of 0.93 – 1.0 for AOD, fine-mode AOD (AODF), coarse-mode AOD (AODC) and Ångström exponent (AE); R = ~ 0.8 for absorption AOD (AAOD) and SSA; RMSE (root mean square error) < 0.03 for AOD, AODF, AODC, AAOD and SSA]. Thus, it is demonstrated the GRASP/Component approach can provide aerosol optical products with comparable accuracy as the AERONET standard products from the ground-based sun photometer measurements as well as some additional important inside on aerosol composition.

Changes in urbanization, industry, and biomass burning in South, Southeast, and East Asia, have led to rapidly changing emissions of gasses and aerosols. These changes are due to missing and misrepresented sources, extreme events, and time-varying sources. Due to few ground stations outside urban areas in China, Korea, Japan, Singapore, and Thailand, this work uses a new, model-free approach in combination with daily OMI measurements of NO₂ and UV, to identify and quantify emissions where measurements are available. An emphasis is made in regions that have undergone rapid or significant change. Using observations from the same platform takes advantage of consistent spatial and temporal coverage while taking advantage of the fact that NO₂ and BC are co-emitted from the same processes and under the same thermodynamic conditions. The BC mass, size, and number loadings are all computed using AERONET SSA and a core/shell-constrained MIE model. Through the application of the mass-conserving model-free approach constrained by first-order approximations of wet and dry deposition, chemical aging, and dynamic transport, the computed number, size, and mass emissions profiles are computed. Differences between existing a priori emissions from FINN and EDGAR are compared with the top-down emissions database and the ranges of allowable physical, chemical, and dynamical terms. All products and analyses are done grid-by-grid in both space and time, with an emphasis on extreme events previously identified from OMI NO₂ columns in 2016.The results demonstrate a significant underestimation in rural areas in Myanmar, Northern Thailand, Laos, and Northeast India, as well as in suburban areas around cities in Southeast Asia. However, some highly developed areas have overestimated emissions, likely due to more strict environmental enforcement, including Shanghai, Singapore, and Taibei. The uncertainty and day-to-day variability are examined in further detail, supporting that the inversion results are statistically significant in the regions mentioned.

Simultaneous observations of carbon dioxide (CO₂), the major greenhouse gas (GHG), and nitrogen dioxide (NO₂), a tracer for fossil fuel combustion, help to identify and quantify anthropogenic CO₂ sources. Ship-, aircraft-, and ground-based observations together with satellite observations of GHG and other trace gases aim to better understand changes in their atmospheric concentrations. To achieve this goal, the global coverage of in situ observations by public and private networks is expanding, and new satellite missions are scheduled like that of GOSAT-GW. By using an approach to integrate ship data of the Ship-of-Opportunity program and aircraft data of the Comprehensive Observation Network for Trace gases by Airliner, we set up a framework to evaluate satellite observations of the column-averaged dry-air mole fractions of, for example, CO₂ (XCO₂) over the ocean. Although the applicability to CH₄ is currently limited due to the sparseness of in-situ data, we explore the potential of this approach for the future when the observation networks are expanded. Complementary to this approach to constrain anthropogenic CO₂ emissions and to validate CO₂ and NO₂ data of the GOSAT-GW mission, we are aiming to make continuous cargo ship-based observations of XCO₂, XCH₄, and XCO using a semi-automatic Fourier transform infrared (FTIR) spectrometer, combined with a UV spectrometer to measure the vertical column densities of NO₂ (VCD_NO2). The setup, developed by the Heidelberg University, consisted initially only of the mobile semi-automatic FTIR, and was tested for the first time on a cargo ship in the summer 2022. Currently, the Heidelberg University is integrating the UV spectrometer. Scheduled for end of this year, the novel setup will be deployed on a cargo ship operating along major anthropogenic emission sources of the Japanese East Coast. We will present the concepts, challenges, and perspectives for validating future satellite missions and monitoring anthropogenic emissions.

Nitrogen dioxide (NO₂) is a major air pollutant. Tropospheric NO₂ vertical column densities (VCDs) retrieved from sun-synchronous satellite instruments have provided abundant NO₂ data for environmental studies, but such data are limited by insufficient temporal sampling (e.g., once a day). The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 will retrieve NO­₂ at an unprecedented high temporal resolution. Here we present a research product for tropospheric NO₂ VCDs, referred to as POMINO-GEMS. We develop a hybrid retrieval technique combining GEMS and TROPOMI observations as well as GEOS-Chem simulations to generate hourly tropospheric NO₂ slant column densities (SCDs). We then derive tropospheric NO₂ air mass factors (AMFs) with explicit corrections for the anisotropy of surface reflectance and aerosol optical effects, through pixel-by-pixel radiative transfer calculations. Prerequisite cloud parameters are retrieved with O₂-O₂-based cloud algorithm by using ancillary parameters consistent with those used in NO₂ AMF calculations. Initial retrieval of POMINO-GEMS tropospheric NO₂ VCDs for June–August 2021 exhibit strong hotspot signals over megacities and distinctive diurnal variations over polluted and clean areas. POMINO-GEMS NO₂ VCDs agree well with our POMINO-TROPOMI v1.2.2 product (R = 0.96, and NMB = 6.9%). Comparison with MAX-DOAS VCD data at five sites shows a small bias of POMINO-GEMS (NMB = –12.2%); however, the correlation for diurnal variation varies from -0.57 to 0.86, suggesting strong location-dependent performance. Surface NO₂ concentrations estimated from POMINO-GEMS VCDs are consistent with measurements from the Ministry of Ecology and Environment of China at 855 sites (NMB = – 5.6%, and R = 0.96 for diurnal correlation averaged over all sites). POMINO-GEMS will be made freely available for users to study the spatiotemporal variations, sources and impacts of NO₂.

Local environmental conditions in Southeast Asia, especially high humidity and frequent cloud cover, pose challenges in atmospheric monitoring both via modelling and when using satellite observations. We explore opportunities to address such challenges in the case of smoke haze dispersion, with the synergistic use of model outputs and satellite products. The Numerical Atmospheric-dispersion Modelling Environment (NAME) is an atmospheric pollution dispersal modelling tool capable of simulating and forecasting many atmospheric dispersion phenomena and associated physical and chemical processes. Himawari is a geostationary satellite providing high frequency multispectral observations. We use Himawari Aerosol Optical Depth (AOD) and Fire Radiative Power (FRP) products. We focus on 2019 and explore use of Himawari FRP, instead of the MODIS FRP assimilated by CAMS GFAS which is currently used as input to NAME. The aim is to evaluate the impact of the higher observation frequency of Himawari and the differences in saturation limit between the two instruments on the emission outputs of the model. We further compare model-derived AOD with Himawari AOD data, to get a view of the uncertainties of AOD estimation. Finally, we train a Machine Learning algorithm to translate AOD to column-integrated Particulate Matter emissions using NAME model simulations. This translation is traditionally done using smoke mass extinction coefficients measured in laboratory experiments or field expeditions. However, there is limited availability of such data in Southeast Asia, and their accuracy is further impacted by high levels of humidity in the region and plume aging. Use of model outputs in the ML algorithm could complement the lack of data and cover a wider range of local conditions. Based on our findings, we discuss potential synergistic use of satellite products and NAME outputs, for research regarding deriving smoke haze emissions in the region as well as for operational monitoring and forecasting of haze dispersion.

NOx (NOx = NO + NO₂) is a major component of air pollution with substantial spatiotemporal variations. Current emission inventories lag in time for several years and can hardly capture the fine-scale spatial pattern of NOx emissions. Here we develop a novel algorithm (PHLET) to retrieve NOx emissions at high spatial resolutions (≤5 km) based on tropospheric NO₂ vertical column densities (VCDs) retrieved from satellite instruments. The algorithm derives the lifetimes and emissions of NOx at individual locations by explicitly accounting for the nonlinear chemistry and horizontal transport. We apply the algorithm to obtain NOx emissions for China at 5 km resolution in summer (JJA) 2012–2022 based on POMINO NO₂ VCD data for OMI and TROPOMI instruments. Our PHLET data reveal many fine-scale emission details and considerable small-to-medium-scale sources missing in current anthropogenic inventories. Those missing sources are associated with human activities reflected in road network and Tencent user location data. Our PHLET data also show substantial inter-regional differences in interannual variations and trends of NOx emissions over the past decade, which are also absent in current inventories. Although the total NOx emission in China has been decreasing, the contribution of emissions from the western provinces to the national total has been growing and has exceeded the contribution of the eastern coastal provinces. Besides the anthropogenic sources, our PHLET retrieval discovers large NOx emissions from natural lakes on the Tibet Plateau that are previously unknown. Such emissions are likely a result of the anammox process under rapid warming over the plateau as an unaccounted feedback between climate change, lake ecology and nitrogen emissions. Overall, our NOx emission retrieval serves as a crucial tool to detect and quantify anthropogenic and natural emissions at fine scales, in support of air quality and climate modeling and targeted emission mitigation.

Organosulfates (OSs) as important constituents of atmospheric organic aerosol (OA) are widespread in ambient environments. Although the sources, formation process, and chemical composition of OSs have been diversely studied, OSs formed by anthropogenic emissions are still little known. In this study, the molecular compositions of OSs in atmospheric PM_2.5 samples collected from a winter measurement campaign (SEISO-Bohai) at Jingtang Harbor, a typical traffic environment, are characterized by ultra-performance liquid chromatography (UHPLC) coupled to electrospray ionization ultrahigh resolution mass spectrometer (UHRMS) Orbitrap. The changing trends of the port OS chemical compositions are observed from one complete haze pollution episode, which happened in this campaign. Along with the increased degree of haze pollution, the relative abundances of OSs have been apparently added and the molecule structures have become more complex. The processes of oxidation and fragmentation drive the OS formations in the haze environment. Then, the potential precursors from ship emissions are identified based on the “OS precursor map” created by previous study. The high molecular weight and low degree of unsaturation and oxidization of OSs are suggested to be mainly derived from ship intermediate/semi-volatile organic compound (I/SVOC) emissions. These OSs have appeared the wide range of molecule weight and good chemical homogeneity in the clean aerosol samples. In addition, our study also finds that ship emissions should further facilitate the production of OSs under the haze pollution condition. 

Air pollution in India is a growing concern, which has significant adverse effects on human health and ecosystems. However, there are very limited observations of air pollutants, and assessments of specific effects of air pollution are hindered. This study aims to simulate air pollutants in India from 2015 to 2019 using the Community Multi-scale Air Quality (CMAQ) model at the resolution of 36 × 36 km². The meteorological fields are generated by the Weather Research and Forecasting (WRF) model. The anthropogenic emissions are generated from Emissions Database for Global Atmospheric Research (EDGAR) and the MIX Asian emission inventory. Emissions are adjusted to simulation years with different adjustment coefficients for different sectors and states. Biogenic and wildfire emissions are also provided to CMAQ. After the model results are validation, the levels, health and ecosystem effects are analyzed. The study would reveal the importance of controlling particulate matters and ozone in India and provide information for designing effective control strategies.

With decreases in anthropogenic precursors, natural precursors may become more important in O₃ pollution. However, the biogenic volatile organic compounds (BVOCs) change and its impact remain unclear due to the lack of long-term measurements of VOCs. In this study, we emphasize the increasingly important role of BVOCs in O₃ pollution by analyzing the long-term measurements in Hong Kong obtained during 2013–2019. Driven by the warming temperature, biogenic isoprene at a suburban site in Hong Kong increased 0.05 ± 0.02 ppbv/yr (9%/yr), which is in sharp contrast to the decreases in anthropogenic precursors during study period. Detailed chemical modeling shows that increased BVOCs enhanced local O₃ production by 25%, and the most obvious effect was shown in summer. Increased BVOCs also affected the non-linear relationships between O₃ and anthropogenic precursors, i.e., increasing the O₃ sensitivity to nitrogen oxides (NOx) and decreasing the O₃ sensitivity to anthropogenic VOCs (AVOCs). Despite changes in precursors, the O₃ formation remains in VOC-limited regimes at Tung Chung. Joint control of AVOCs and NOx (at a ratio greater than 1.2) would help avoid exacerbation of O₃ pollution and reduce NO₂ pollution. Our findings also suggest the BVOCs increase likely have occurred in the larger Pearl River Delta (PRD) region, and thus the results from our study in Hong Kong may have implications for developing AVOCs/NOx reduction measures in the PRD region and beyond.

Long-chain unsaturated fatty acids (uFAs), such as oleic acid, undergo rapid degradation via heterogeneous reactions with atmospheric oxidants upon emission. The oxidation mechanism and kinetics have been extensively studied in laboratory experiments. However, quantitative knowledge of degradation rates under real-world atmospheric conditions is scarce. We obtained the nighttime decay rates of three cooking-related uFAs using a relative rate approach applied to bihourly measured data in urban Shanghai. The estimated lifetime of oleic acid was 6 h under conditions of ∼12 ppb ozone and 60%–100% relative humidity encountered at our urban location or an inferred ∼2 h at a higher ozone level of ∼40 ppb. The decay rates of elaidic and linoleic acid are determined to be 0.62 and 1.37 that of oleic acid, respectively. This work provides the first kinetic data pertaining to real-world conditions. They are valuable for constraining the modeling of heterogeneous aging of ambient organic aerosols.

Polycyclic aromatic hydrocarbons (PAHs) are unavoidably derived from combustion processes, and are contaminants of global concern because they increase the risk of lung cancer and are detrimental to human health and the ecosystem. While high concentrations of PAHs were already measured in 2008, future changes in energy use, land use, and climate policy may alter the PAHs concentrations. Integrating a global atmospheric chemistry model, a lung cancer risk model, and plausible future emissions trajectories of PAHs, we assess how global PAHs and their associated lung cancer risk will likely change in the future. Benzo(a)pyrene (BaP) is used as an indicator of cancer risk from PAH mixtures. From 2008 to 2050, the population-weighted global average BaP concentrations under all RCPs consistently exceeded the WHO-recommended limits, primarily attributed to residential biofuel use. Peaks in PAH-associated incremental lifetime cancer risk shift from East Asia (4×10^-5) in 2008 to South Asia (mostly India, 2-4×10^-5) and Africa (1-2×10^-5) by 2050. In developing regions of Africa and South Asia, PAH-associated lung-cancer risk increased by 30-64% from 2008 to 2050, due to increasing residential energy demand in households for cooking, heating, and lighting as the rapid population growth, as well as the continued use of traditional biomass use, increases in agricultural waste burning, and forest fires. With the stringent air quality policy, PAH lung-cancer risk substantially decreases by ~80% in developed countries. Climate change is likely to have minor effects on PAH lung-cancer risk compared with the impact of emissions. Future policies, therefore, need to consider efficient combustion technologies that reduce air pollutant emissions, including incomplete combustion products such as PAH.

Aiming to control the rising Particulate Matter (PM) emissions, many researchers highlighted the importance of identifying PM sources within and transboundary of the city. This study investigates the seasonal and spatial heterogeneity of PM concentration and chemical characteristics. The source apportionment technique was performed to identify the potential sources' contribution in Vijayawada city. Based on the land use land cover (LULC) pattern, the atmospheric PM₁₀ and PM_2.5 samples were collected at multiple monitoring sites with Traffic, Commercial, Industrial, Residential, and Background characteristics during winter and summer. The highest average PM concentration (PM₁₀: 154±42 µg/m³, PM_2.5: 82±21 µg/m³) was measured at the industrial site in winter followed by traffic, commercial, background, and residential sites with an estimated difference of (20%, 14%), (31%, 30%), (36%, 34%) and (36%, 25%), respectively. A similar trend was observed in the summer season, with the highest concentrations at the industrial site (PM₁₀: 86±24 µg/m³, PM_2.5: 52±15 µg/m³) followed by traffic, commercial, background and residential sites. Secondary Inorganic Aerosols (SIA) (7-35%, 6-24%) and crustal elements (4-35%, 11-18%) were observed to be the significant components in PM₁₀ and PM_2.5 mass size fraction, respectively. Low OC/EC ratio values ranging from 0.98-2.95 indicate a high proportion of gasoline and diesel emissions in the urban atmosphere. Chemical Mass Balance (USEPA CMBV5.0) model identified vehicular, industrial, construction, road dust, SIA, biomass burning, and coal combustion sources, respectively, with a significant variation in seasonal and spatial contributions. Vehicular emission is one of the primary sources, contributing 47-16% in PM₁₀ and 51-22% in PM_2.5, followed by the combined contribution of biomass and coal combustion, which varies from 35-13% and 31-20% in PM₁₀ and PM_2.5, respectively. High biomass and coal combustion emissions were attributed to several industrial units which use coal, wood, briquettes, etc., for fuel requirements.

Accurate modeling of PM_2.5 is essential for effective air quality management and mitigation. Chemical transport models (CTMs) like the Community Multiscale Air Quality (CMAQ) model, are widely used for PM_2.5 simulation through atmospheric processes of dispersion, deposition, and chemical reactions. However, CMAQ often suffers from biases due to limitations in model structure, uncertainties in initial and boundary conditions, and insufficient representation of meteorological conditions and source contributions. Traditional methods for model bias diagnosis usually rely on empirical and priori assumptions and require extensive sensitivity tests with high demands on computational resources, such as Monte Carlo methods or Latin hypercube sampling. Recently machine learning (ML) methods have been widely used in environmental science researches due to their simple structure, fast speed and ability to deal with no-linear relationships. ML also provides a new perspective on the identification of simulation biases. However, as a complex multi-phase mixture, it is still challenging to diagnose biases in PM_2.5 simulations using ML methods. In this study, we plan to diagnose the drivers of the model bias in simulating surface PM_2.5 concentrations based on the lightGBM model, an efficient ensemble ML method. The individual components of PM_2.5 will be simulated using CMAQ and the sectoral sources of PM_2.5 will be tracked using source apportionment methods. CMAQ simulation bias will be diagnosed from multiple perspectives, including meteorology, PM components, and sectoral sources. The results of this study can provide new ideas for CTMs model biases diagnosis, deepen the understanding of CMAQ simulation biases, and provide information for model improvement.

Monsoon systems are transporting water vapour and energy across the globe, making them a central component of the global circulation system. Changes in different forcing parameters have the potential to fundamentally change the monsoon characteristics as indicated in various paleoclimatic records and as projected by the latest generation of general circulation models in the context of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Here, we use 32 CMIP6 models to analyze the model projections for the Indian summer monsoon with particular focus on the seasonal summer monsoon rainfall, its interannual variability and the occurrence of extremes. Besides, we use the Atmosphere Model version 2 developed at the Geophysical Fluid Dynamics Laboratory (GFDL-AM2) and couple it with a slab ocean to analyse the monsoon's sensitivity to changes in different forcing parameters on a planet with idealized topography. This Monsoon Planet concept of an Aquaplanet with a broad zonal land stripe allows to reduce the influence of topography and to access elementary meridional monsoon dynamics behind the competing effects of different forcings.

The Asian summer monsoons are globally significant meteorological features, creating a strongly seasonal pattern of precipitation. The stability of this hydrological cycle is of extreme importance for ecosystems and the livelihoods of a large share of the world’s population. Simulations are performed with an intermediate complexity climate model, PLASIM, to assess the future response of the Asian monsoons to changing concentrations of aerosols and greenhouse gases. The radiative forcing associated with aerosol loading consists of a mid-tropospheric warming and compensating surface cooling, which is applied to India, Southeast Asia and East China, both concurrently and independently. The primary effect of increased aerosol loading is a decrease in summer precipitation in the vicinity of the applied forcing, although the regional responses vary significantly. The decrease in precipitation is only partially ascribable to a decrease in the precipitable water, and instead derives from a reduction of the precipitation efficiency, due to changes in the stratification of the atmosphere. When the aerosol loading is added in all regions simultaneously, precipitation in East China is most strongly affected, with a quite distinct transition to a low precipitation regime as the radiative forcing increases beyond 60 W/m². The response is less abrupt as we move westward, with precipitation in South India being least affected. By applying the aerosol loading to each region individually, we are able to explain the mechanism behind the lower sensitivity observed in India, and attribute it to aerosol forcing over East China. Additionally, we note that the effect on precipitation is approximately linear with the forcing. Doubling carbon dioxide levels acts to increase precipitation and weakening the circulation over the region. When the carbon dioxide and aerosol forcings are applied simultaneously, the carbon dioxide forcing partially offsets the surface cooling and reduction in precipitation associated with the aerosol response.

Because increased greenhouse gas emissions considerably warm and moisten the Earth's atmosphere, one may expect an increase in monsoon precipitation during the historical period. However, we find the observed Northern Hemisphere land summer monsoon (NHLM) precipitation has significantly decreased since 1901. Simulations from Coupled Model Intercomparison Project Phase 6 (CMIP6) well reproduce global warming and the drying of NHLM since the industrial revolution when forced by observed external forcings. Result from single forcing experiment shows that the anthropogenic aerosol (AA) dominates the Northern Hemisphere (NH) monsoon precipitation drying, while the greenhouse gases (GHG) largely control surface warming. Thus, the NH monsoon precipitation responds to AA more sensitively than the GHG. The AA can more effectively modulate downward solar radiation reaching the surface, decreasing evaporation and weakening monsoon circulations by reducing the interhemispheric temperature difference and land-ocean thermal contrast, albeit with the same efficiency of the thermodynamic effect in the two forcings. Our result indicates the future intensive reduction of aerosol emission may rapidly recover the NH monsoon precipitation.

The Indian Ocean summer aerosol regime is of greater relevance to the regionally-important monsoon system of South Asia. The effects of aerosols such as black carbon (BC) on climate and buildup of the monsoon over the Indian Ocean are poorly constrained. Uncertain contributions from various natural and anthropogenic sources impede our understanding. Here, we use multi-year (2012-2017) observations of BC and its isotope fingerprint at a remote island observatory in the northern Indian Ocean to constrain loadings and sources during the little-studied monsoon season. Carbon-14 data pin down a largely fossil (65±15%) BC source domain. Occasional plumes from African savanna fire contribute up to (50 ± 5%) of the loadings over the summertime Indian Ocean. We estimate that the mass-absorption cross-section for this region is 7.6 ± 2.5 (m²/g), with a tendency to increase with savanna fire input. Simultaneous measurement of air and rain allow us to calculate washout ratios of BC and other chemical species. The washout rates are much lower for BC than OC and inorganic ions such as sulfate, implying a longer atmospheric lifetime for BC. The wet deposition flux for BC during the high-loading winter was three times higher than during the wet summer, despite much less precipitation in the winter. Taken together, the combustion sources, longevity, and optical properties of BC aerosols over the summertime Indian Ocean are different from the more-studied winter aerosol, with implications for chemical transport and climate model simulations of the Indian monsoon.

The rise of the trends of extreme rainfall events across all the major regions of India since 1901 has been revealed in our recent study. This machine-learning driven data analysis also suggested several potential drivers including urbanization and other land-use changes behind the observed trends. However, their causal relations need to be carefully examined by using, e.g., advanced models. Specifically, it has been indicated that urbanization can modify the water cycle and precipitations, either through the modification of land-use, or through effects induced by the emissions of anthropogenic aerosols. The thermodynamical perturbations induced by the presence of urban land-use, including the urban heat island effect, are known to induce rainfall modification due to perturbation of the flow and enhancement of the convective activity. However, this impact has yet to be clarified in a large scale, highly energetic system like the Asian Monsoon system. Using the high resolution meso-scale atmospheric model Meso-NH coupled with an urban-module, we have investigated the impact of urban land use on the heavy precipitation events during the Indian Summer Monsoon. The results of this study will be presented and discussed.

A high rate of deforestation has occurred in the Maritime Continent (MC) during recent decades due to the rapid growth of the local economy. MC deforestation is known to have a considerable influence on the local climate. However, its possible teleconnections to other regions are less understood. In this study, the influence of MC deforestation on precipitation over southern China is investigated using both reanalysis data and state-of-the-art climate models. The results show that MC deforestation could strengthen the late winter and early spring precipitation decline over southern China during 1979–2019. The enhanced regional convection due to MC deforestation leads to anomalous northward shifting of the tropical meridional circulation, with the ascending branch at 0°–10° N and descending at 20°–30° N compared with climatological ascending (10° S–0°) and descending (10°–20° N) branches. Such circulation change suppresses the moisture convergence and the development of convection over southern China. Our results suggest that, in addition to the local effects of deforestation, a further investigation of the remote impacts is essential for a thorough understanding of the climate influences of ongoing MC deforestation.

The classical understanding of monsoon onset implies a sudden increase in precipitation and sustainable rains. However, initial rain often gets stalled after monsoon onset for a week or even longer, causing disaster for farming. Here, I show that there are two types of critical transitions to monsoon: a direct transition, with a sudden increase in precipitation, and a two/multiple-step transition, with a dry spell after the initial rain. I present evidence that 70% of the last 47 years show a two/multiple-step transition, which went overlooked. Significantly, the second type of transition prevails under climate change. I uncover that the cause of rainfall cessation is the hidden phenomenon of intermittence emerging between two successive phase transitions. The new theoretical finding opens a door for the universal definition of local monsoon onset. I show how to evaluate the effect of climate change on the transition to monsoon in every state of the Indian subcontinent. 

Based on the concept of cloud water resource (CWR) and the cloud microphysical scheme developed by the Chinese Academy of Meteorological Sciences (CAMS), a coupled mesoscale and cloud-resolving model system is developed in the study for CWR numerical quantification (CWR-NQ) in North China for 2017. The results show that (1) the model system is stable and capable for performing 1-yr continuous simulation with a water budget error of less than 0.2%, which indicates a good water balance. (2) Compared with the observational data, it is confirmed that the simulating capability of the CWR-NQ approach is decent for the spatial distribution of yearly cumulative precipitation, daily precipitation intensity, yearly average spatial distribution of water vapor. (3) Compared with the CWR diagnostic quantification (CWR-DQ), the results from the CWR-NQ differ mainly in cloud condensation and cloud evaporation. However, the deviation of the net condensation (condensation minus evaporation) between the two methods is less than 1%. For other composition variables, such as water vapor advection, surface evaporation, precipitation, cloud condensation, and total atmospheric water substances, the relative differences between the CWR-NQ and the CWR-DQ are less than 5%. (4) The spatiotemporal features of the CWR in North China are also studied. The positive correlation between water vapor convergence and precipitation on monthly and seasonal scales, and the lag of precipitation relative to water vapor convergence on hourly and daily scales are analyzed in detail, indicating the significance of the state term on hourly and daily scales. The effects of different spatial scales on the state term, ad- vection term, source–sink term, and total amount are analyzed. It is shown that the advective term varies greatly at different spatiotemporal scales, which leads to differences at different spatiotemporal scales in CWR and related characteristic quantities.

Aerosol indirect effects, including the Twomey and Albrecht effects, remain a major uncertainty in current weather and climate studies. Such effects are found to vary with cloud types, and the vulnerability is often evaluated with a metric called susceptibility -- the derivatives of the measured variable to aerosol concentration. In this research, the Weather Research and Forecasting model v4.3.1 combined with the NTU microphysical scheme is used to investigate the aerosol susceptibility of marine boundary-layer clouds over the NW Pacific Ocean during cold-air outbreak events. These clouds are often found in mixed-phase upstream and liquid-phase downstream of the cold air trajectory. We found distinctive features of aerosol susceptibility in both cloud types. In the liquid-phase stratocumulus clouds, most cloud properties (e.g., liquid water path, optical depth, and cloud albedo) have positive susceptibilities to the aerosol effects, consistent with earlier studies. However, the cloud fraction showed a negative susceptibility when drizzle is active. In the mixed-phase stratocumulus clouds, on the other hand, the cloud and ice water paths, as well as cloud fraction, are found to increase in the low-end and high-end aerosol concentrations but decrease when the aerosol concentration is between 10² and 10⁴ cm^-3. Such a nonlinearity is likely associated with the transition of precipitation formation from drizzle-dominated in low aerosol concentrations to ice-dominated at high aerosol concentrations.

The role of cloud subgrid-scale structure in modulating satellite views of clouds was investigated. This was realized by implementing a stochastic cloud generator into the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package together with surrogate clouds produced by a cloud- resolving model (CRM). The subgrid-scale structural parameters are decorrelation length for overlapping cloud fraction (Lcf), decorrelation length for overlapping cloud condensate (Lcw), and the shape parameter v for measuring cloud inhomogeneity. With the use of median values of Lcf, Lcw, and v derived from CRM, the simulated satellite views bear close resemblance to those using CRM-inherent clouds. Varying these parameters in the range of lower and upper quartiles leads to differences that are about one-fifth of those caused by changing cloud microphysics in CRM. While Lcf influences clouds throughout the whole troposphere, Lcw and
v result in changes mostly within the upper layers. Increasing (decreasing) cloud inhomogeneity or overlapping degree leads to decreased (increased) occurrence of clouds, except for high-topped clouds viewed by Multiangle Imaging Spectroradiometer. Care must then be exercised when interpreting model biases in comparison with different instruments. Sensitivity tests show changing condensate distribution from gamma to lognormal makes little impact on final results. Although the differences induced by any of the parameters alone are much limited, they are getting comparable to those seen between models and observations when all parameters are synergistically altered. This brings encouraging results to the modeling community that simulator-diagnosed clouds can be potentially improved by tuning cloud subgrid-scale parameters.

The top-hat approximation, which is widely used in the mass flux type convection, is verified at different horizontal scales, especially those at the limit of vanishing cloud fraction, with the aid of large eddy model simulations (LES). Three shallow convection cases in the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) programs are conducted, including BOMEX, RICO and ATEX. Results show that convective cloud fraction increases with the increase of horizontal resolution, consequently resulting in errors of cloud component in the decomposition of scalar flux increase. These errors are however largely compensated by the decrease of errors in the environment, leading to the error of top-hat approximation almost unchanged. For either cloud or environment component, there exists an inversed relationship between convective fraction and the covariance between vertical velocity and conserved tracer. This brings encouraging results to the modeling community that the mass-flux method in parameterizing convection still works at high resolutions. However, the closure remains a big problem at the limit of vanishing cloud fraction, which is not covered in this study.

Convective parameterization in numerical models remains an important source of model uncertainty, as evidenced in the deficiency in simulations of the diurnal cycle of precipitation (DCP). In this study, the behaviors of the phase and amplitude of DCP in China in three commonly used reanalysis products, including ERA-5, JRA-55 and MERRA-2, are compared against GPM (Global Precipitation Measurement) and CMORPH (Climate Prediction Center Morphing Technique). Results show while JRA-55 and ERA-5 produce DCP that are closely consistent with observation, MERRA-2 shows 1- to 3-hour shifts in phase and significantly underestimates the amplitude. Further analysis is performed by comparing 3-D cloud fraction, apparent heating (Q1) and apparent moisture sink (Q2) against cloud-resolving model (CRM) simulations. Only ERA-5 captures the evolution of clouds in association with convective precipitation as in CRM simulations, while the other two fail to reproduce such characteristics. This implies the need to link DCP with subgrid-scale processes besides convection and to treat physical parameterizations as an integrated system.

The Mei-Yu front heavy rainfall event occurred in northern Taiwan on 2 June 2017. The largest daily accumulated rainfall was 645.5 mm at the north tip of Taiwan. The frontal system becomes quasi-stationary in northern Taiwan, and it lasts for 10 hours. A strong barrier jet over the northwest coast of Taiwan is present when the Mei-Yu front approaches northern Taiwan. The strong low-level jet brings abundant moisture to Taiwan and causes strong convergence along the frontal zone. The strong convection produces more than 600 mm of rainfall when the system becomes stationary. In order to examine how the barrier jet over northwestern Taiwan affects the movement of the Mei-Yu front, this study uses the WRF model to reproduce this heavy rain event. Meanwhile, three sensitivity tests are conducted in the numerical experiment. In the removing Taiwan terrain test, the front moves southward quickly. The accumulated rainfall was only 200 mm and the barrier jet is much weaker than the CTRL, showing that the orographic can obstruct the frontal movement. In the replacing southern Taiwan mountain test, northern Taiwan terrain provides part of the blocking effect, but the front still moves southward quickly without a strong barrier jet. In the enhanced barrier jet test with the same topography as CTRL, the barrier jet is slightly stronger than CTRL. The position of the stationary Mei-Yu front is further north than the CTRL. The strong southerly wind also enhanced the convergence, resulting in more rainfall, however, the rainfall did not fall over the land. The results show that the orographic effect contributed to part of the blocking effect for the frontal movement in northern Taiwan, and caused the barrier jet to form in northwestern Taiwan. The slow movement of the Mei-Yu front is significantly influenced by the barrier jet.

The fine spatial characteristics of hail events over Beijing metropolitan region (BMR) is performed using direct observation from quality-controlled disaster information dataset during the year of 2011-2021. Hail is concentrated in urban and northeast mountain region which is highly related with both synoptic circulations and the underlying surface. Four synoptic circulation patterns: the northwest flow in front of ridge (NWP), straight westerly flow (SWP), cold vortex (CV), and pre-trough (PT) are investigated and the hail generation is found differs under these patterns. SWP favors local hail events with the largest convective available potential energy and PT favors systematic hail events with the highest vertical wind shear. With weak low level background flows under NWP and CV, hail events concentrate over the BMR’s plains with obvious surface warm center and wind convergence near urban region which favor storm initiation and enhancement. When the low-level background flow is larger in SWP and PT, the more dominated mountain-plain circulations lead to the hail events center changing to the BMR’s northeast mountains rather than the urban region. These results suggest the potential influences of urban environment and mountain-plain circulations on the distribution of hail events under different synoptic circulation patterns.

We explored the use of modern deep learning models of Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) for objective climate predictions of summertime air temperatures in South Korea. In order to design deep learning models more appropriate for climate predictions, the CUTMIX data augmentation technique was modified and applied to climate observations and APCC Multi-Model Ensemble (MME) data. The 3-dimensional CNN model performed much better with data augmentation for validation (accuracy > 0.6 for all summer months and folds) as well as test data for June (LT1) and July (LT2). Class activation maps were examined and the contributions of the data augmentation could be observed in some cases of the test data, e.g., in July 2018 and July 2021, when the northern Pacific are and northern polar region are activated and improved the predictions respectively. Graph Convolution Network models were developed for node classification (years as nodes) and graph classification (geographical grids as nodes and years as multiple graphs). Predictions for July (LT2) of the node classification model were improved with the month-agnostic approach, where all months of data are used for training. Predictions for June (LT1) of the graph classification model were improved with the month-agnostic approach (Heidke Skill Score > 0.35 for all folds) and predictions for July (LT2) were also improved with the aforementioned data augmentation. ※ This research was supported by APEC Climate Center.

Ocean surface albedo (OSA) is essential to the ocean and climate energy balance. It is usually considered as the constant or just a simple function of the solar zenith angle (SZA) in the climate model. However recent research suggests that the OSA can be significantly affected by low-level wind, and play an important role in climate simulation, especially in high-resolution simulation. In this work, we incorporated an improved OSA parameterization scheme into the First Institute of Oceanography-Earth System Model (FIO-ESM) V2.0. The revised parameterization scheme takes into account the sea surface roughness and whitecaps induced by surface wind and simplified water volume scattering. Numerical experiments indicate that the improved scheme leads to an increase in OSA of roughly 40% at the global scale. It is remarkable that the effects of foams or whitecaps are noticeable in areas with strong winds, such as the Southern Hemisphere westerly zone. The model bias in sea surface net shortwave radiation has been reduced by an average of 3 W/m², and over the subtropical and Antarctic oceans by up to 8 W/m² and 14 W/m². The enhanced OSA parameterization also reduces the global annual mean error of sea surface temperature by up to 0.87°C.

The Global Observing Satellite for Greenhouse gases and Water cycle(GOSAT-GW) satellite is planned to be launched in 2024 as the successor to the GOSAT-1 and GOSAT-2 greenhouse gas observation missions. The GOSAT-GW satellite challenges to simultaneously observe greenhouse gases and nitrogen dioxides (NO2), major air pollutants. The grating spectrometer is equipped, and more than three million points are observed per day.
The air mass factor (AMF) is used to convert from the NO2 slant column density to the vertical column density, and is one of the largest error sources in retrieving NO2 vertical column from the observation spectrum. High speed calculation of AMF is required because of large number of observation points of GOSAT-GW. The AMF values for all observation cases are pre-calculated by radiative transfer model and saved in the look-up table (LUT).
This study discusses the optimization of LUT, i.e, how to select the nodes of input variables in LUT. We used SCIATRAN version 4.6.1 for radiative transfer calculation. The node of each input variable, such as solar zenith angle, viewing zenith angle, albedo, and terrain height, are selected by AMF gradients for the variables. Our algorithm showed 45% lower error than conventionally-made LUT. In this presentation, we show the results of all LUTs used in the NO2 retrieval data processing of the GOSAT-GW.

Ambient NH₃ plays an important role in forming particulate matter (PMs), and therefore, it is crucial to comprehend NH₃'s properties in order to reduce PMs. However, it is not easy to achieve this goal due to the lack of monitoring data on ambient NH₃ concentrations in typical air quality stations. Nor are we aware of any study that has looked into NH₃ predictions as of yet. This study thus offers the first inquiry into applying machine learning and an auto hyperparameter optimization approach to estimate NH3 concentrations. To obtain more crucial data about NH₃ concentration, we additionally created time lag and parcel tracking routines. To analyze feature importance, we applied the SHAP (SHapley Additive exPlanations) function. From 2016 to 2018, Taichung's hourly average NH₃ values were about 16.9 ppb. Such NH₃ concentrations were predicted using an optimized extra trees model that has the potential to account for up to 96% of the total variance. Agriculture activity was the most significant factor (main source) to affect NH3 concentrations in Taichung among all the characteristics.

A significant relationship between the Indian Ocean Dipole (IOD) and the following year’ s El Niño-Southern Oscillation (ENSO) has been reported in recent decades. Nevertheless, uncertainty exists regarding the associated mechanisms. Based on the statistical analysis and numerical experiments, our study proposes that the spring southeastern Indian Ocean warming (SEIOW) plays a bridging role in the teleconnections of the IOD and subsequent ENSO. A positive IOD could induce a positive tendency of sea surface temperature (SST) in the southeastern Indian Ocean from autumn to winter, primarily through the cloud-radiation-SST feedback, forming an anomalous SEIOW in the subsequent spring. As a Gill-model response to this SEIOW, an anomalous anticyclone appears over the western North Pacific, accompanied by easterly wind anomalies in the western equatorial Pacific which generate eastward propagating upwelling Kelvin waves. As a result, anomalous cooling appears over central-eastern tropical Pacific in the following seasons, manifesting as a La Niña mode. The process is vice versa for the teleconnections of the negative IOD and following year’s El Niño. Additionally, role of the ocean channel (i.e., the Indonesian Throughflow) in connecting the spring southeastern Indian Ocean and the subsequent winter eastern Pacific SSTs is also discussed.

Mixed Rossby-Gravity (MRG) waves are westward propagating synoptic scale equatorial disturbances, which play a crucial role in the formation of tropical cyclones and tropical depressions. They constitute a significant part of various modes of tropical variability, like Madden-Julian Oscillation (MJO) and Quasi-Biennial Oscillation. This study investigates the trends and Inter Annual Variability (IAV) in the occurrence of upper tropospheric MRG events using ERA-I reanalysis data for the period 1979-2018. The MRG events are identified by projecting the 200hpa meridional winds onto the theoretical spatial structure of MRG waves. A steady increasing trend is observed in the MRG events, which is contributed by the MRG events associated with intrusion of extratropical disturbances. Possible factors governing the observed trend and IAV in MRG events are El-Nino Southern Oscillation (ENSO), MJO and extratropical forcing. The MRG events over the central and eastern Pacific contribute maximum to IAV. ENSO explains about 25% of IAV and exhibits a positive correlation with non-intrusion MRG events and a negative correlation with intrusion MRG events. These observations have been investigated by exploring the properties of the westerly duct at 200hPa and Outgoing Longwave Radiation during El-Nino and La-Nina years over the central-eastern Pacific. The convectively active state of MJO over the western Pacific explains 20% of IAV. The antisymmetric heating with respect to the equator, associated with MJO, enhances non-intrusion MRG events by forbidding the intrusion of extratropical disturbances through subtropical easterlies. The increasing trend in the intrusion of extratropical disturbances explains the observed trend in MRG events.

The global atmosphere NWP system – named the Korean Integrated Model (KIM) – developed by the Korea Institute of Atmospheric Prediction Systems (KIAPS) was made operational at the Korea Meteorological Administration (KMA) in April 2020. The global data assimilation (DA) system is based on a hybrid-4DEnVar system consisting of KVAR (KIM VARiational data assimilation) and LETKF (Local Ensemble Transform Kalman Filter). Although it provides a good forecast skill within the performance range of the world’s leading NWP centers, the prediction has some systematic bias over East Asia. Forecast skill of the forecast system including DA system is sensitive to initial data and assimilated observation data. Thus, improving the model itself is important, but the performance of observation data used for DA is also important in order to improve forecast skill. An Observing System Simulation Experiment (OSSE) has been conducted to understand the model prediction sensitivity according to DA. If we consider a global gridded virtual Sonde observation network and assume that the ERA5 analysis is true of the OSSE system, simulated observations with globally 1-degree intervals can be generated from ERA5. In this study, the difference between forecasts fields assimilated with the existing observation data and the virtual Sonde data is examined for cases chosen when KIM’s East Asia forecast skills are significantly low. Through these experiments and analysis, we would like to examine the following two. First, this system will be used to investigate the effect of the initial condition and the virtual Sonde data as the observation data on the 5-day prediction accuracy of the East Asia. Second, we plan to conduct sensitivity experiments for diagnosing regions where affect the improvement of East Asia’s forecast performance to improve 5-day forecast skill.

Observing system simulation experiment (OSSE) provide a rigorous, cost-effective approach to evaluating the potential impact of new observing systems and alternate deployments of existing systems and to optimizing observing strategies (Hoffman and Atlas, 2016). One of most advantageous thing in OSSE is that researcher can verify the result of Numerical Weather Prediction (NWP) with respect to the truth (called as nature run (NR)). Furthermore, observation can be simulated for any proposed (for example, making new type of sensor, appling realistic forward operator and method, specifying the locations, and error characteristics of the observation). To use such advantage of OSSE, we developed the prototype of global OSSE system used Korea Integrated Model (KIM).
In this study, we will introduce the theoretical background of OSSE (OSSE is generally consist of the NR, simulated observation, DA and forecast, and calibration and verification), KIM forecast system, and what i done to develop the OSSE system used KIM, briefly. Finally, to verify the OSSE system used KIM, we performed the denial experiments for aircraft, not only in OSSE but also performed in OSE. In comparison result between OSE and OSSE aircraft denial experiments, OSE/OSSE showed similar performance in most atmospheric variables of analysis field. So, we evaluated the OSSE system used KIM is well made enough to simulate the real case.

The planetary boundary layer (PBL) is the lowest part of the atmosphere and interacts directly with the Earth's surface, playing an important role in weather, climate, and air quality. It also plays an important role in the exchange of aerosols, heat, humidity, and other atmospheric gases. Therefore, it is vital to understand the spatial and temporal variations of the PBL height (PBLH). Radiosonde launching has been used to determine the vertical profile of the PBL. The Korea Meteorological Administration(KMA) has been launching radiosondes twice a day (00 and 12 UTC or 9 and 21 LST) at ten different stations in Korea. In this study, the PBLH was calculated using the bulk-Richardson method with the KMA radiosonde data. The bulk Richardson method calculated the PBLH using the ratio of thermally generated turbulence generated by the vertical shear. The used variables were virtual potential temperature, wind speed, gravity, altitude, and humidity. When the bulk Richardson method reached the threshold, two types of thresholds, 0.25 and 0.5, were commonly used. In this study, 0.25 was used as the threshold. The altitude when the threshold of the bulk Richardson method calculated from the ground exceeded 0.25 was determined as the PBLH. The calculated PBLH was also compared with those determined based on the ceilometer measurements.

Currently, the operational Korean Integrated Model (KIM) Numerical Weather Prediction system at the Korea Meteorological Administration (KMA) uses 3DIAU to add deterministic analysis increments to the model. The weights are based on a Dolph filter, but with the first and last weights modified to remove discontinuities, producing a “modified Dolph filter”. Hybrid-4DEnVar scheme used for deterministic analyses produces a total of 7 analysis increments – one every hour within data assimilation window. Thus, it is possible to replace the 3DIAU scheme with a 4DIAU scheme that makes use of the hourly analysis increments. We have experimented with a 4DIAU scheme that adds each analysis increment over a 2-hour period, again using modified Dolph weights. For increments that are, the response is determined by the weighting function used for the individual increments, giving relatively light time-filtering. For increments that are not, such as the stationary increments related to the static covariance, the response is determined by the sum of the weights across all increments, giving relatively heavy time-filtering similar to that obtained with the 3DIAU scheme. Thus, for balanced increments that are consistent in time, such as increments associated with rapidly-moving features like tropical cyclones, 4DIAU should be better than 3DIAU at retaining the increments. We compared analysis and forecast performance of the new 4DIAU scheme with the original 3DIAU scheme. In analyses, there was relatively little impact on T and Q, but U and V were improved. In the Northern Hemisphere, overall forecast performance was improved, and the performance improvement of geopotential height was significant. The performance improvement in the Northern Hemisphere is particularly noticeable in Asia. In a tropical cyclone case experiment, the 4DIAU scheme gave a slightly deeper central pressure than the 3DIAU scheme, and expressed the change in tropical cyclone position over time better than 3DIAU.

NIMS works for meteorological science research and policy support for people’s safety and happiness and has been doing lots of research under the research project, called the “Research and Development for KMA Weather, Climate, and Earth System Services”.
NIMS has developed technologies needed to improve forecasting for KMA such as a medium-term forecast guideline and marine weather forecasting system. Initialization and ensembles of climate prediction system were improved for more accurate and reliable climate predictions. Due to climate change, damages caused by heavy rains in summer have increased and rainfall forecasting has become difficult. So, NIMS has developed diagnostic factors for heavy rainfall through synoptic analysis of severe weather.
There was challenged study using advanced observation equipment meteorological observation vehicles, meteorological aircraft, and meteorological drones. For understanding and analyzing of a meteorological phenomenon, NIMS observed heavy rainfall, black ice, and so on. These data are used to understand the mechanisms of severe weather. Black ice prediction based on these data has been developed to decrease winter traffic accidents. The technologies of weather modification are constantly evolving using advanced equipment. Also, Korea Cloud Physics Experimental Chamber (K-CPEC) was conducted in 2021. We expect to develop better technology and understand cloud physics.
To support national climate change response policy, NIMS produced new future projections based on New Green House Gases (GHG) and published climate change reports. Also, NIMS continuously monitors GHG. For expanding renewable energy, NIMS provides high-resolution meteorological resource maps.
In addition, NIMS has been producing convergence weather information such as airport weather prediction, health weather, and agricultural weather. More detailed results will be shown at the conference.

As a land-atmosphere coupling “hot spot”, the northern China climate transition zone has a sharp spatial gradient of hydrothermal conditions, which plays an essential role in shaping the spatial and temporal pattern of evapotranspiration-precipitation coupling, but whose mechanisms still remain unclear. This study analyzes the spatial and temporal variation of land-atmosphere coupling strength (CS) in the climate transitional zone of northern China and its relationship with soil moisture and air temperature. Results show that CS gradually transitions from strong positive in the northwest to negative in the southeast and northeast corners. The spatial distribution of CS is closely related to climatic hydrothermal conditions, where soil moisture plays a more dominant role: CS increases first, and then decreases with increasing soil moisture, with the threshold of soil moisture at 0.2; CS gradually transitions from positive to negative at soil moisture between 0.25 and 0.35; CS shows an exponential decreasing trend with increasing temperature. In terms of temporal variation, CS is strongest in spring and weakens sequentially in summer, autumn, and winter, and has significant interdecadal fluctuations. The trend of CS shifts gradually from significantly negative in the west to a non-significant positive in the east. Soil moisture variability dominates the intra-annual variability of CS in the study regions, and determines the interannual variation of CS in arid and semi-arid areas. Moreover, the main reason for the positive and negative spatial differences in CS in the study area is the different driving regime of evapotranspiration (ET). ET is energy-limited in the southern part of the study area, leading to a positive correlation between ET and lifting condensation level (LCL), while in most of the northern part, ET is water-limited and is negatively correlated with LCL.

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 ◦C for HRCLDAS and 2.3 ◦C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.

Due to concentrated buildings, low vegetation, and road paving materials, cities are likely vulnerable to weather environments such as heat waves and floods. In addition, in complex urban structures, it is difficult to predict wind changes caused by high-rise buildings, making it challenging to analyze the thermal environment and pedestrians' thermal comfort in the city.
This study attempts to simulate and analyze wind changes in complex urban environments using a PALM(Parallelized Large-Eddy Simulation Model). Haeundae Marine City in Busan, Korea, located on the coast, was selected as the study target area. This area is where concentrated high-rise residential and commercial buildings are and where light reflection by the outer walls of the building glass and damage by strong winds occur.
Simulate the actual urban structure of Marine City with a large-eddy simulation (LES)-based PALM model to identify changes in surrounding winds due to the height difference of the high-rise buildings inside the city and compare environmental changes such as pedestrian thermal comfort.

In this study, the predictability of the Western Pacific (WP) pattern is evaluated using five seasonal prediction models the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) for the winters from 1982/1983 to 2021/2022. The temporal correlation coefficient (TCC) between the observed and MME-predicted WP indices was 0.61 (0.37–0.54 for individual models) for the entire series. However, when only three Super El Niño (SEN) years (Niño3.4 ≥ 2.0) out of the 40-year series were excluded, the TCC dropped down to 0.54 (0.27–0.42). During the SEN years, the WP was strongly affected by the SEN-excited anomalies via the PNA. In observations from non-SEN years, the WP pattern was strongly related to the dipole pattern in Northwestern Pacific SST (TCC = 0.8), for the description of which we suggested a Northwestern Pacific (NWP) index, and it was significantly weakly related to the ENSO and IOD, whereas in the model simulations, the main role was played by the ENSO (TCC = 0.6). The NWP index was well predictable in MME (TCC = 0.73) and individual models (0.56–0.71). We showed that the prediction of the WP index polarity is reliable when both predicted WP and NWP anomalies are significant and indicate the same WP sign that has implications for the seasonal forecasting. Acknowledgment: This work was carried out with the support of the Research Fund of Research Institute for Basic Sciences, Pusan National University, Korea.

In this study we have conducted a survey of Mixed Rossby-Gravity (MRG) wave events in the upper troposphere and quantified their association with the intrusions of extratropical disturbances for the period 1979-2019. MRG events are identified by projecting the equatorial meridional winds at 200 hPa onto the meridional structure of theoretical MRG waves. 2390 MRG events are identified and majority (61%) of them occurred during May-October months, and 65% of the total MRG events occurred over the central-east Pacific and Atlantic Ocean domains. Not only the frequency of occurrence but also the amplitude, wavenumber and trapping scale of the MRG events are found to exhibit a clear seasonality. MRG events associated with intrusions of extratropical disturbances are identified as when the potential vorticity on the 350K isentropic surface at 15° latitude exceeded 1 PVU in the vicinity of the MRG events. We find that 37% of the MRG events are intrusion MRG events and a large majority (88%) of such events occurred over the central-east Pacific and Atlantic Ocean domains. It is also noteworthy that nearly 70% of such intrusions occurred in the winter Hemisphere where the westerly wind ducts are well developed. Over the central-east Pacific during Northern Hemispheric (NH) winter, it is observed that the amplitude of intrusion MRG events are larger and have a larger meridional extent compared to non-intrusion MRG events. They also exhibit a similar spatial scale as the extratropical disturbances implying that resonant interactions may be a primary mechanism for the genesis of MRG events. During NH summer, on the other hand, MRG events are primarily triggered by convective processes and the extratropical disturbances may be instrumental in amplifying their amplitude.

Korean Integrated Model (KIM), developed by Korea Institute of Atmospheric Prediction System (KIAPS), has been in operation at Korea Meteorological Administration (KMA) since April 2020. It has been continuously improved its performance through five updates (v3.5a (2020.06), v3.6a (2021.4), and v3.7 (2021.12), v3.8 (2023.2)) including data assimilation and physics.
One of the major systematic errors of the KIM is the cold biases in the lower Arctic atmosphere in summer, which degrades KIM’s prediction performance in the northern hemisphere. The radiative cooling effect by clouds, especially, over-estimated cloud ice, is analyzed as one of the main factors.
In this study, we will examine the possibility of improving the above-mentioned systematic errors through sensitivity test to changes in parameters related to ice crystals of WSM5, cloud microphysics scheme adopted by KIM.

The single-scattering albedo (SSA) of atmospheric aerosols is a key parameter that controls aerosol radiative effects. The variation of SSA is thought to be mainly regulated by aerosol absorption in the Himalayas and South Asia, but observations contradict this idea. In situ field campaigns conducted over two Himalayan sites revealed that SSA was strongly dependent on scattering but weakly correlated with absorption. Observational results combined with the Mie theory further illustrated that SSA was primarily modulated by size distribution rather than absorption. Aerosol Robotic Network (AERONET) data showed similar impacts of size distribution on SSA and that aerosol radiative forcing efficiencies were significantly dependent on SSA. Aerosol size distribution therefore considerably affects radiative forcing by modulating aerosol SSA over the Himalayas. This study highlighted the influence of aerosol size distribution on radiative forcing over the Himalayas, which has important implications for understanding aerosol radiative effects globally.

Polarization characteristics of the atmospheric scattering is important and not to be ignored in radiative transfer simulation. A new polarized radiative transfer method is developed for visible and near-infrared spectra, which is suited for use in remote sensing applications and can calculate the polarized radiation emerging from an atmosphere. The single-layer polarized radiative transfer equation and inhomogeneous multi-layer connection are solved by the discrete ordinates method and adding method, respectively. Monte Carlo model (MYSTIC, as the benchmark) and PolRadtran/RT3 are used to evaluate the new method in both accuracy and computational efficiency under different atmospheric conditions and view angles. Judging from the results, the accuracy of Stokes vector (I-, Q-, U-, V-component) calculated by the new method is a good agreement with the results by PolRadtran/RT3 except where near solar incident zenith angle and anti-incident zenith angle. The relative root mean square errors (RMSE) of Stokes vector for test cases between MYSTIC and the new method or RT3 can also prove the good accuracy of new method. Meanwhile, the new method has a higher computational efficiency compared to RT3, especially for the atmosphere with large scattering optical depth. As differ from RT3, the computing time of the new method cannot increase with increasing optical depth.

In this study, the Extinction coefficient(α) was calculated using lidar data installed at Seoul National University and divided by the mass concentration of fine particles to confirm the Mass Extinction Efficiency(MEE). This study used lidar data from January 2015 to June 2020 for analysis and compared the mass concentration of fine particle at the AirKorea Gwanak-gu station and relative humidity data(RH) at the Metropolitan Meteorological Administration. RH was divided into 7 sections, and we checked the change in the particle's characteristics. As a result, as the RH increased, MEE tended to increase, and it was confirmed that the PM2.5/PM10 ratio also increased. However, the α ratio observed by lidar appears different from the mass concentration. The α Fine/Total ratio rather decreased as the RH increased. Conversely, the α Coarse/Total Ratio showed an increase. We thought to be caused by differences between observation equipment. Airkorea's station method is a Beta-radiation attenuation monitor method that uses a heater to remove some moisture to lower the RH. However, in the case of LiDAR, the effect of RH is not removed by directly observing particles distributed in the atmosphere. Particles corresponding to PM2.5 in a dry atmosphere can increase to a particle size larger than PM2.5 as the RH increases, so they can be classified as coarse particles in LiDAR. However, in mass concentration measurement, the effect of humidity is partially removed, and It is classified as PM2.5. This work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2023-01-02-084).

When atmospheric information is analyzed through LiDAR, backscattering coefficients and extinction coefficients are calculated through LiDAR signals. The LiDAR ratio represents the ratio of the extinction coefficient and the backscattering coefficient. The LiDAR ratio is a variable that varies depending on the size distribution of particles in the air, the refractive index of particles, etc. Still, in most studies, it is fixed as a constant, and the extinction coefficient is calculated from the backscattering coefficient, or the backscattering coefficient is calculated from the extinction coefficient. In this study, LiDAR using a 532 nm wavelength laser and a camera attached to the lidar were utilized. The relationship between the extinction coefficient and the backscattered signal was examined by calculating the backscattered signal proportional to the backscattered coefficient with the pixel value of the laser taken in the picture while obtaining the extinction coefficient by LiDAR. Using these two variables, it was confirmed that the optimized lidar ratio could be calculated according to each atmospheric condition. In addition, in the case of LIDAR, because of the field of view (FOV), a short-range signal of about 0.5 km cannot be calculated. However, since the camera can also obtain pixel values at a short distance, the near-field extinction coefficient can also be restored by using the previously obtained LiDAR ratio and the backscattered signal received from the photograph. Through this study, it was confirmed that if the pixel signal of the camera is used, it is possible to calculate the extinction coefficient more accurately by using the LiDAR ratio optimized for each atmospheric situation, and the overlapping LiDAR short-range signal can be restored.This work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea ( NIER-2023-01-02-084).

We tried to use scanning Light Detection and Range (LIDAR) to measure PM mass concentration as a three-dimension. The scanning Lidar system can detect PM mass concentration in an area with a radius of 5 km using 1064 nm and 532 nm wavelengths. Furthermore, the time resolution of the scanning LIDAR is as short as 10 sec, so it can apply to the timely monitoring of specific aerosol emissions. In the experiment for emissions of aerosols, we faced some problems; the laser was blocked at 2500 m by mountain, and the overlap distance of the 1064 nm laser was longer than that of the 532 nm laser. For the former problem, we neglected the signal from blocking and found the reference extinction coefficient through measured signals over 10 min to increase SNR. In the case of the overlap problem, the range-corrected signal contributed to increasing the slope, which made the reference signal overestimated. Therefore, we need to correct the LIDAR signal by the geometric factor in blind-zone, in which the backscattered signal cannot reach the field of view on telescope. For those reasons, 1) we have theoretically calculated geometric factor from experimental system parameters, such as laser beam divergence, FOV, distance between laser and telescope, and others. 2) We also experimentally considered the relationship signals between 1064 nm and 532 nm lasers to find the signal loss with the assumption of constant Angstrom Exponent. 3) We have corrected geometric factors iteratively from theory to experiment. This work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea ( NIER-2023-01-02-084).

A mixing layer height (MLH) is an important factor that controls air pollution concentrations. Thus, it is necessary to determine MLH accurately and to understand its spatial and temporal variability. One of the common methods for determining MLH is to calculate MLH using the vertical profiles of aerosol backscatter measured by a ceilometer. Originally, a ceilometer has been developed to detect the cloud base height. However, its measurement has also been used to determine the MLH, and several ceilometer measurement networks have been emerged across the world. The Korea Meteorological Administration (KMA) has installed ceilometers to observe cloud amount and cloud base height, and two different types of ceilometers, Vaisala CL31 and Eliasson CBME80B have been operating at 64 stations in Korea. Since 23 December 2022, the National Institute of Environmental Research (NIER) in Korea also has deployed nine Lufft CHM 15k ceilometers to determine the MLH and they are in operation now.
In this study, the MLH was determined based on a gradient method using the aerosol backscatter data measured by KMA and NIER ceilometers, and the spatial and temporal variability of MLH were quantified. The spatial and temporal variability of MLH was further examined as a function of the distance between ceilometers. All correlation analyses were also separated for daytime (06:00 LST to 18:00 LST) and night-time (18:00 LST to 06:00 LST), which had also compared each other. Based on these analyses, the impacts of temporal and spatial variability of MLH on the determined MLH were examined.

Fog is defined as a case where the visibility distance is less than 1 km by atmospheric water droplet, and it is known that fog is difficult to predict because it is caused by various causes depending on the seasons and locations. However, the extinction coefficient of water droplet at a given wavelength of 550 nm is exactly determined by the product of the volume concentration of a water droplet with a given refractive index of 1.33 and the volume extinction efficiency defined at the given size and wavelength. So the rapid change of visibility can be simply explained only by the rapid changes of the volume extinction efficiency by particle size or the rapid changes of water droplet concentration. The volume extinction efficiency sequentially reaches the maximum value following the wavelength for the constantly increasing particle size. In this study, we developed the algorithms on how to predict the occurrence of radiation fog by measuring the growth rate of particles using the relative change in extinction coefficient that occurs sequentially at the three wavelengths of RGB (449nm, 534nm, 597nm). Based on the particle growth theory, when particles grow in various ways, how the extinction coefficient changes at three wavelengths was studied, and how the relative value of the extinction coefficient at each wavelength changes with the growth of the particle size. Based on these theoretical results, we checked the changes of the extinction coefficient obtained from the 3 wavelengths at the radiation fog events and predicted the fog disappearing in the Daecheong area around Daecheong Lake where actual radiation fog often occurs. This work was supported by the “Graduate school of Particulate matter specialization.” of Korea Environment Industry & Technology Institute grant funded by the Ministry of Environment, Republic of Korea.

It is well known that tropical intraseasonal oscillation (ISO) modulates the weather in the tropics. The ISO’s importance in cyclogenesis, extreme precipitation events, and heatwave events is established in the literature. Hence understanding and simulating these oscillations can tremendously improve the extended-range predictions of weather extremes. With changing climate, the frequency and intensity of heatwaves are known to be increasing and becoming one of the primary weather catastrophes. In May 2015 southeastern states of India faced the deadliest heatwave in its history which claims to take 2500 lives. The atmospheric circulation and role of surface fluxes in forming this heatwave were studied in the recent past but the underlying physical mechanism is not known. In this study, we investigated the impact of the tropical intraseasonal oscillation (ISO) in forming the May 2015 heatwave over the southeastern regions of India. The observations show that the occurrence of a heatwave is attributed to north-eastward propagating ISO circulation which results in persistent high-pressure anomaly with anomalous downward motion favoring clear skies, adiabatic heating, and horizontal warm advection. The 2m maximum temperature anomaly shows that 60% to 75% contribution in maximum surface air temperature (SAT) during heatwave period is from ISO-related temperature anomalies. Further numerical analysis using the WRF model confirms that this heatwave event was caused by Propagating ISO. The model output shows a difference of 1.5 to 2 ℃ in maximum SAT between the control and sensitive experiment. Indicating the substantial role of ISO in the development and intensification of the heatwave. This analysis emphasizes that improving the forecasting skills of ISO may facilitate the sub-seasonal forecast of local heatwave events.

The weather observation data collected over a long period of time can be used as important data as the basis for showing the long-term climate characteristics and extreme weather of an area. Meanwhile, in the case of developing countries, it is difficult to collect high-quality weather observation data due to the lack of installation and management system of weather observation equipment. Through the Van-KIRAP (Vanuatu Klaemet Informesen blong Redy, Adapt mo Protekt, in Bislama) project, APCC (APEC Climate Center) wanted to provide observational data that could be useful in the Vanuatu region by adding the gap-filling of observational data to the OSCAR (tailored System of Climate services for AgRiculture) system. Through a comparative analysis conducted by applying various existing gap-filling algorithms to observation data, an algorithm technique suitable for precipitation and temperature, which are the target variables of this study, was selected. The performance of the gap-filling algorithm was compared using the standard statistical indicator CC (Correlation Coefficient) and RMSE (Root Mean Squared Error), and an algorithm that shows compliance performance was selected. It is expected to be used in various applied studies, including long-term climate analysis and extreme climate analysis, through correction observation data through the OSCAR system.

Long-term climate data is one of the main data used for climate analysis, but in developing countries, the development of observation data is insufficient compared to developed countries. We tried to build observation data for long-term climate analysis using satellite remote sensing as the data needed for climate analysis. Precipitation datasets were constructed using GridSat and GPM-IMERGE, and the temperature was constructed based on AQUA, TERRA on installed MODIS sensor and AIRS satellite products. The results of the 10km spatial resolution of the satellite output were developed using the IGISRM technique to construct the entire Vanuatu area with high-resolution grid datasets of 5km. It is expected that climate analysis of high-resolution grid data produced in this project will be used as data for agriculture and various applications.

Large-scale dynamics in the tropics and midlatitudes are governed by two different dominant physical processes. The tropics is governed by the weak temperature gradient system where temperature gradient is constrained to be moderate, whereas the midlatitude area is governed by the quasi-geostrophic system where the Coriolis force and pressure gradient force are nearly balanced. Presumably, for these two different governing equations to be simultaneously valid in large scales, the boundary between these two regions must be connected by phenomena with small spatial scales. Therefore, in this study, we investigate the atmospheric behavior at the tropics-extratropics boundary in the Northern Hemisphere.
The 5800 meter height line at 500 hPa is defined as the tropics-extratropics boundary. This line serves as a proxy for the northern edge of the tropical region. Next, we focus on the strong wind axis of the westerly jet stream, which moves meridionally at mid-latitudes, because the jet stream can supply vortices with small spatial scales. Then, we investigate the positional relationship between the jet stream and the tropical mid-latitude boundary.
By measuring the mean latitudinal distance between the jet stream and the boundary, it is shown that the jet stream flows near tropics-extratropics boundary in most seasons. However, only in seasons when a sea surface temperature (SST) front exists near the boundary, the westerly jet stream is anchored by the SST front and temporarily leaves tropics-extratropics boundary. In boreal spring and autumn, when the westerly jet stream is trapped above the SST front, the existence of mesoscale phenomena such as the Meiyu-Baiu front may be required to connect the tropical and midlatitude solutions in place of the westerly jet stream.

This research aims to assess the effectiveness of hydrological drought indicators in describing characteristics of drought events and to develop a composite hydrological drought index (CHDI) to better characterize the drought conditions in the northern watershed of Thailand, where hydrological droughts frequently occur as a result of climate change and land use. The study utilizes the WRF-CESM climate model and land cover data from MODIS (IGBP) as inputs for the VIC hydrological model to analyze the drought indicators. The VIC model includes surface soil moisture, runoff, precipitation, baseflow, first-layer soil moisture, evaporation, and second-layer soil moisture. The results showed that all parameters have moderate to high correlations with runoff gauge station data, respectively. The CHDI was further developed by combining the indicators using weights derived from the principal component analysis (PCA) technique. The CHDI showed a better correlation (r= 0.45-0.73) with the observed data and was closest to the low-volume water events declared by the Upper Northern Region Irrigation Hydrology Center.

The frequency and intensity of extreme high temperature (EHT) in the Northern Hemisphere exhibit remarkable low-frequency (LF) variations (longer than 10 years) in summer during 1951–2017. Five hotspots featuring large LF variations in EHT were identified, including western North America–Mexico, eastern Siberia, Europe, central Asia, and the Mongolian Plateau. The probability density functions show that the higher EHT occurrences over these hotspots in recent decades is consistent with the shifted average and increased variances in daily mean temperature. The common features of the LF variation in EHT frequency over all domains are the remarkable increasing trends and evident decadal to multidecadal variations. The component of decadal to multidecadal variations is the main contribution to the LF variations of temperature in the last century. Further analysis shows that the coherent variability of decadal to multidecadal temperature variations over western North America–Mexico, eastern Siberia, Europe, and the Mongolian Plateau are the footprints of a dominant natural internal signal: the Atlantic multidecadal oscillation. It contributes to the variations in temperature over these hotspots via barotropic circumglobal teleconnection, which imposes striking anomalous pressure over these regions. This study implies that natural internal variability plays an important role in making hotspots more vulnerable to EHT.

To investigate the recent change of tropical night in South Korea, we analyzed the duration and intensity of tropical nights (daily minimum temperature, Korea Meteorological Administration; KMA) for 40 years (1979-2018) quantitatively. Spatiotemporal analysis showed that the tropical nights in the Seoul metropolitan area were more intense and longer-lasting as compared to other South Korean regions. Specifically, the tropical nights over this region increased more prominently in intensity, frequency, and duration. The tropical night event in the metropolitan area was classified into pure-TN (no heatwave prior to tropical night) and HWTN (tropical night following heatwave). The composite analysis was conducted for two types of tropical night events, pure-TN, and HWTN, based on 40-year ERA5 reanalysis data. Pure-TN mostly occurred when the edge of western North Pacific subtropical high (WNPSH) was present over the Korean Peninsula with the southwesterly wind, leading to a positive temperature advection anomaly in the metropolitan area. Furthermore, a positive low cloud cover anomaly with enhanced downward longwave radiation also prevailed. On the other hand, HWTN mainly occurred when the WNPSH expanded northwestward until its center was located over the Korean Peninsula with a positive downward shortwave radiation anomaly. Moreover, a descending motion anomaly induced adiabatic heating over the metropolitan area was presented. The significant increasing trends in tropical night events (pure-TN: 0.143 day/year, HWTN: 0.077 day/year) were observed at 95% confidence level. To investigate these trends, the regression analysis was performed on the synoptic factors for three sub-analysis periods with 10 days (21-31 July;P1, 1-10 August;P2, 11-20 August;P3). As a result, the favorable atmospheric conditions for HWTN (pure-TN) have been frequently constructed during P2 (P1 and P3).

Understanding the changes in extreme rainfall has raised a lot of attention since it is one of the major exposures in terms of climate risk. In Taiwan, extreme rainfall usually occurs with the unique environmental condition such as tropical cyclone or Mei-yu front. While Extreme indices has been widely used for analyzing extreme rainfall, the data sample is based on each grid instead of the extreme rainfall event itself. Therefore, investigating the extreme rainfall from the event perspective can provide a new insight for the changing extremes. Here we apply the event-tracking method by using Depth-First Search algorithm on high-resolution gridded observation data to track the extreme rainfall events from 1980 to 2019 in Taiwan. Two different thresholds (80mm and 200mm) are then selected for analysis due to their potential threats to river flood and flash flood. Our results show that the extreme rainfall events have increased significantly in both frequency and intensity. Frequency changes in extreme rainfall events indicate a 16.84% increase for 200mm-event and a 10.95% increase for 80mm-event. Mean changes of total rainfall volume also show a larger increase for 75.40% in 200mm-event than 33.03% in 80mm-event. Within all the contributors to the change of total rainfall volume, mean affected area contributes the most even without the effect of increasing rainfall intensity to the threshold. Furthermore, the increasing frequency of extreme rainfall events is larger in southern Taiwan than in northern Taiwan.

Extreme precipitation events (EPEs) associated with intense extratropical cyclonic storms (Western Disturbances; WDs) during the winter season (December through March) have the potential to induce catastrophic damages to life, infrastructure, environment and agricultural sustainability over the western Himalayas (WH). However, a sparse in-situ observational network in this region conjugated with complex topography highlights the uncertainties in available coarse resolution precipitation products and emphasizes the necessity of finer grid scaled regional climate models for accurate simulation of precipitation variability, specifically EPEs with highly localized characteristics as well as underlying synoptic dynamics. The present study is an effort to investigate the performance of a ultra-resolution Weather Research and Forecasting (WRF) model through the evaluation of its sensitivity to various microphysical schemes and their ensemble for the simulation of intense episodes of WDs and associated extreme precipitation events over the WH. The sensitivity of precipitation extremes (identified using percentile approach) to different microphysics (~3 km resolution), in the WRF model configured on two two-way nested domains (9km and 3km), has been validated using multi-source rainfall datasets including gauge-based (IMD) and satellite (IMERG) observations as well as with the latest regional reanalysis dataset, IMDAA. Different rainfall skill scores (CSI, ETS, BSS, HSS, POD and FAR) provided insight into how well WRF mimicked intense WDs and the accompanying rainfall. Furthermore, the corresponding dynamics and physical processes in the model has been elucidated through a composite analysis for the simulated EPEs using the reanalyses, IMDAA and ERA5. Our findings underpin the sensitivity of model-simulated winter precipitation extremes associated with intense WDs over WH to different microphysical parameterizations. Detailed results will be discussed.

The realistic simulation and projection of the Amundsen Sea Low (ASL) are essential for understanding the Antarctic climate and global climate change. Using 14 models that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6), this study evaluates the climatological characteristics of ASL with comparison to the ERA5 reanalysis and their CMIP5 versions and assesses the future change of ASL under 1.5℃-4℃ global warming. The climatological spatial distribution of ASL is captured reasonably but with underestimated intensity by CMIP6 multi-model ensemble (MME). Among the CMIP6 models, EC-Earth3 has most accurate representation of ASL according to the pattern correlation and biases. The seasonal variation of the ASL depth and location are found to be reasonably reproduced by the CMIP6 models. Compared with CMIP5, CMIP6 MME exhibit evident reduced uncertainties and overall improvement in simulating absolute depth and location of the ASL center, which might be attributed to models’ capability of representing westerlies, while the biases in relative depth become even large in CMIP6 MME. In response to future warming from 1.5℃ to 4℃ above pre-industrial levels, the absolute depth of ASL will very likely deepen with larger amplitude in all seasons, while the relative depth might enhance only under high-level warmer world in austral autumn to winter. The CMIP6 MME also projects that the ASL will shift poleward constantly in austral summer and migrate southwestward during austral autumn with the rising global mean temperature. The enhancement and poleward movement of ASL could also be identified during the Ross Sea ice advance season under 1.5℃-4℃ global warming. The results reveal the potential of CMIP6 models in the ASL study and the impact of ASL on Antarctic climate under different global warming levels.

Using the lagged maximum covariance analysis (MCA), the present study investigates the interannual variability of the storm track in the Southern Hemisphere and the Antarctic sea ice throughout the year. The results show that the two are most tightly coupled in the austral cold seasons. Specifically, storm track anomalies in June and July are associated with a zonal dipole structure of the sea ice concentration (SIC) anomalies in the western Hemisphere, with centers in the Antarctic Peninsula and the Amundsen-Bellingshausen Seas. The storm track can modulate the large-scale atmospheric circulations, which induces anomalous meridional heat transport, downward longwave radiation, and mechanical forcing to further influence the SIC anomalies. The resultant SIC anomalies can last for several months and have the potential to feed back to the storm track. According to the MCA, the influence of the SIC anomalies to the storm track is most evident in August. The SIC dipole along with the SIC anomalies in the Indian Ocean sector have large impact on the storm track activities downstream. The SIC anomalies alters the near-surface temperature gradient and subsequently atmospheric baroclinicity. Further energetic analysis suggests that the enhanced atmospheric baroclinicity facilitates the baroclinic energy conversion from mean available potential energy to eddy available potential energy, and then to eddy kinetic energy, strengthening the storm track activities over the midlatitude Indian Ocean.

Atmospheric Rivers (AR) accounts for more than 90% of poleward moisture transport across midlatitudes of both hemispheres and has a crucial impact on hydrological cycle and surface mass balance over the Antarctic ice sheets. Here we investigate the seasonal and interannual variations of the poleward moisture transport by AR using 43-year MERRA-2 reanalysis data. Integrated water vapor transport (IVT) is used to detect AR with latitude dependent thresholds of IVT to better detect AR-like features in the polar regions. Based on 3-hourly AR statistics, four main AR genesis regions in the southern hemisphere (i.e., southern Africa and southwestern Indian Ocean, southern Pacific Ocean, South America monsoon region, and Australia) are identified. In this talk, the variations of AR frequency and intensity at each genesis regions and associated patterns of atmospheric circulations and its impact on the poleward moisture transports will be discussed on intraseasonal, seasonal, and interannual timescales.

In early February 2020, two consecutive extreme warming events occurred within three days of each other at the same location in the Antarctic Peninsula (AP). The second event on February 9, 2020, saw the temperature reach 15.5°C at Marambio station on Seymour Island, northeast of the AP, the second-highest recorded temperature. To understand the cause of the extreme warming event, observational data from Marambio station and the European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) data were analyzed to examine extreme warming events that had occurred on Seymour Island during February over the past 40 years. The analysis revealed that the extreme warming event on February 9, 2020 was caused by foehn winds and large-scale horizontal advection, which are associated with a strong blocking high in the upper and lower atmosphere. Unlike typical extreme warming events in February over the past 40 years, the event in 2020 occurred not only in the AP but also throughout the entire West Antarctica.

The El Niño–Southern Oscillation (ENSO) is well-known climate variability in the tropics that has the potential to affect the Antarctic sea ice. The sea ice concentration pattern induced by ENSO is primarily linked with an anticyclonic circulation anomaly in the Amundsen-Bellingshausen Sea (ABS) that modulates atmospheric poleward temperature advection and longwave radiation forcing via poleward moisture advection. In the current climate, the remote influences between the tropics and the Antarctic are strongest during Austral spring (September-October-November, SON). However, this seasonality can change in future conditions. Comparing the remote influences from the tropics to the Antarctic between the present (1982-2014) and the near future (2015-2050) using the CMIP6 historical and SSP585 experiments, the positive geopotential heights at 500 hPa in the ABS regressed onto the ENSO index becomes stronger in Austral winter (June-July-August, JJA). This results in a more extended melting period of sea ice by the remote forcing from the tropics. The stronger response in the upper-level geopotential height anomalies in JJA is suggested by the increased forcing in the tropics and strengthened jet stream. The global model experiments with a dynamical core only support well these changes in the dynamical mechanisms related to the Antarctic sea ice condition in the future.

The current paper investigates the weekly forecasts skills on precipitation in boreal summer over Maritime Continent (MC), which is an area featured by complex topography, warmest oceans, and characterized by great vulnerabilities to high-impact precipitation events, for the models of European Centre for Medium-Range Weather Forecasts (ECMWF) and China Meteorological Administration (CMA) derived from the S2S Project. Results indicate that the ECMWF model shows generally superior forecast performances than CMA, which is characterized by lower errors and higher correlations compared with the observations. Meanwhile, ECMWF tends to produce wet biases with increasing lead times, while the mean errors of CMA are revealed to be approximately constant throughout lead times of 2–4 weeks over most areas. Besides, the temporal correlations between model outputs and observations obviously decrease with growing lead times, with a high-low distribution presented from north to south. In order to detect the sources of predictability, the roles of large-scale drivers like ENSO and BSISO in modulating subseasonal precipitation forecast skills are also assessed in the models. Both ECMWF and CMA can reasonably capture the ENSO related precipitation anomalies for all lead times, while their capabilities of capturing BSISO related precipitation anomalies decrease with growing lead times, which is more obvious in CMA. The enhanced subseasonal precipitation forecast skills mainly respond to the BSISO associated precipitation variability. For most MC areas such as southern Indochina, western Indonesia, Philippines and the eastern ocean, the forecast skills of both ECMWF and CMA can be improved to a great extent by enhancing the capture of BSISO related precipitation anomalies, with the temporal correlations for both ECMWF and CMA increased by about 0.15 for lead times of 3–4 weeks. It provides an opportunity window for the models to improve precipitation forecasts on the subseasonal timescale.

The Madden-Julian Oscillation (MJO) provides an important source of global subseasonal-to-seasonal (S2S) predictability, while its prediction remains great challenges. Based on an atmosphere-ocean coupled model and the widely-used nudging method, suitable initialization and ensemble schemes are explored toward an improved MJO prediction. It is found that strong but not excessive relaxation strength for the divergence and vorticity, and a mild relaxation for the air temperature are appropriate to generate good atmospheric initial conditions. Additionally, the ensemble strategy with perturbed atmospheric nudging coefficients conduces to adequate ensemble spread and hence improves the prediction skill.Here, an 18-member ensemble subseasonal prediction system called NUIST CFS1.1 is developed. Skill evaluation indicates that the NUIST CFS1.1 can extend the MJO prediction to 24 days lead, which outperforms a majority of current models in the S2S project but is far from the estimated potential predictability (~47 days). The limited skill at longer lead times corresponds to forecast errors exhibiting slower propagation and weaker intensity, which are largely owing to the model’s shortcoming in representing MJO-related physical processes. The model underestimates the diabatic heating of enhanced convection and fails to reproduce the suppressed convection within the MJO structure, collaboratively weakening the Kelvin/Rossby waves. This causes weaker horizontal winds and ultimately reduces the horizontal moisture advection on the two flanks of MJO convection. Furthermore, the underestimated Kelvin wave induces insufficient planetary boundary layer (PBL) convergence and thereby results in poor simulation of PBL premoistening ahead of MJO convection. These biases limit the MJO prediction in the NUIST CFS1.1, prompting further efforts to improve the model physics.

The prediction of monsoonal precipitation during Indian summer monsoon (ISM) remains difficult. Due to the high correlation between the Central Indian Ocean (CIO) mode index and the ISM precipitation variability, the predictability limit of the CIO mode index is investigated by the non-linear local Lyapunov exponent (NLLE) method in observations. Results show that the predictability limit of the CIO mode index can reach 38 days during boreal summer (from June to September), which is close to the upper predictability limit of intraseasonal precipitation (up to 40 days), and higher than the predictability limits of dynamical monsoon indices (under 3 weeks) and boreal summer intraseasonal oscillation (BSISO) indices (around 30 days). Such high predictability limit of the CIO mode index is mainly attributable to the long predictability limits from the intraseasonal sea surface temperature (SST) and intraseasonal zonal wind, which are the components of the CIO mode. However, the prediction skill of the CIO mode in the subseasonal-to-seasonal (S2S) air–sea coupled models is still an issue. The ECMWF and UKMO models display significantly higher skills for up to about 2 and 3 weeks, respectively, which are longer than other S2S models. The decline of the CIO mode prediction skill is due to the reduced signal of subseasonal zonal winds at 850 hPa over the tropical central Indian Ocean (especially along the equator; 5°S–5°N, 70°E–85°E). Therefore, a better simulation of tropical subseasonal zonal winds is required to improve the CIO mode prediction in models, and the improvement will benefit a better MISO simulation and a higher prediction skill during the ISM.

S2S forecast has significant potential to aid decision-makers in mitigating extreme weather risks. Efficient S2S forecasts employ a central component between Weather and Climate predictions (for two weeks to less than a season). S2S models can yield adequate information based on the probability of the occurrence of extreme events within sufficiently larger areas through the prediction of complex, large-scale predictors. However, one of the significant challenges for S2S forecast is to develop a robust relationship for warm season predictability. A warming climate amplifies extreme events, making them a greater societal threat. Therefore, the current study develops an adaptive machine learning approach to predict the weekly frequency of intense warm days and 14-day standardized precipitation index (SPI) during the warm season for the Mahanadi River basin, India. The framework incorporates the multiple S2S global forecast models and ensembles across a continuum of S2S timescales to characterize the deterministic predictability of warm season atmospheric teleconnection patterns. The current skillful S2S forecasts of extreme heat and precipitation in warm season greatly benefit multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events.

As an important parameter to measure the exchange of surface fluxes and climate sensitivity, the surface temperature is used to reflect specific responses to climate changes. The global mean surface temperature (GMST) justifiably became a primary indicator of climate volatility on a global scale to evaluate the intensity of climate change. However, the GMST peaked in 2020 under the background of moderate La Niña and even beat the highest record of GMST in 2016 when a strong El Niño occurred. The phenomenon overturned the conventional idea that the highest GMST occurs with strong El Niño. Our work used several datasets of surface temperature and adopted the ensemble empirical mode decomposition (EEMD) algorithm to distinguish the sources of direct contribution to the highest GMST of 2016 and 2020 at different timescales, especially focusing on the subseasonal-to-interannual variations. The results show that high GMST in 2016 was controlled by secular trend (SCT) and annual variability (ANV). However, the dominator of the sharp GMST rise in 2020 was SCT alone because the ANVs in different seasons canceled each other out in 2020, contributing little to the annual mean GMST. We further revealed two prominent patterns of seasonally varying ANVs, one is induced by atmospheric internal variability and the other is related to ENSO. The two patterns mainly located in Eurasia, North America, the Arctic Ocean, and the tropical eastern Pacific Ocean. Dominance by surface temperatures over the four crucial regions on the subseasonal-to-seasonal (S2S) GMST variations was also observed in nearly 80% of the years during 1982-2021, indicating a potential opportunity to improve the S2S GMST forecast by ameliorating the forecast in the four areas. Whether the contribution of ANV will become weaker and weaker in a warmer future world will be the concern of follow-up work.

The PISSARO project focuses on atmospheric and oceanic forecasting at the subseasonal scale for applications over the southwest Indian Ocean basin. It is a collaborative academic research project, developed and conducted in partnership with stakeholders from Reunion and Seychelles and a panel of scientific experts in tropical forecasting. We have exploited the IFS monthly forecast ensemble for the development of future S2S-derived forecast products to provide probabilities of tropical cyclone trajectory scenarios, rainfall anomalies and to follow the ITCZ migration in the southwest Indian Ocean. The products which have been designed and calibrated with the S2S data are now tested in real time during the 2023 austral summer by the forecasters of the Seychelles Meteorological Authority and at the Regional Specialised Meteorological Centre - Tropical Cyclones of La Réunion. A co-creation approach between scientists, forecasters and humanitarian stakeholders has also enabled the development of products to anticipate the cyclone hazard in the southwest Indian Ocean region to support the tropical cyclone disaster risk management. The PIROI is testing this product under operational conditions for the 2022-2023 cyclone season. At the end of the TC season, all these prototypes will be evaluated and the best products will be selected for an operational implementation. We will provide an overview of these products at the workshop.

The heat transfer through atmospheric circulation is vital in determining local weather patterns. In the boreal summer, latent heat transport is the main contributor to the moist static energy transport from the equator to high latitudes. Using a global coupled atmosphere-ocean model (KIM-NEMO), we analyze the impact of ocean coupling on the subseasonal-to-seasonal (S2S) prediction of latent heat transport. The results showed that the highest latent heat transport in the boreal summer was along the eastern part of the continent. The simulations in July 2017 revealed the significant differences between KIM and KIM-NEMO, particularly over the Western North Pacific Ocean. These changes were triggered by the cold SST anomalies (SSTA) in KIM. The improvement of cold SSTA through ocean coupling influenced the regional wind patterns, leading to modifications in moisture transport. Consequently, latent heat transport is simulated more realistically, when considering air-sea interactions.

Situated in the deep tropics, Singapore experiences considerable intra- and interannual variability of rainfall. Therefore, improving rainfall predictions, including the 1-month rainfall outlook, is an important component of improving climate services for Singapore. Traditionally, seasonal prediction models were used as the basis for predicting rainfall for the next month and next season. These seasonal predictions were also combined with additional information, such as the status and outlook of climate drivers such as ENSO (El Nino Southern Oscillation) and MJO (Madden Julian Oscillation) to produce an expert-based outlook. However, for users who do not require outlooks prior to the end of the month, S2S predictions can be used instead, with S2S predictions issued towards the end of the month showing improved skill compared to the seasonal predictions for the 1-month rainfall outlook. This work compares the 1-month rainfall outlook for Singapore from various sources including seasonal models (single models and multi-model ensemble), the ECWMF extended range model as well as along with expert judgment for the period 2020 – 2022. The benefits of the various types of outlooks will also briefly be discussed as part of improving climate services for the country.

Teleconnection patterns associated with the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO) impact weather and climate phenomena in the Pacific–North American region and beyond, and therefore accurately simulating these teleconnections is of importance for seasonal and subseasonal forecasts. Systematic biases in boreal midwinter ENSO and MJO teleconnections are found in eight subseasonal to seasonal (S2S) forecast models over the Pacific–North America region. All models simulate an anomalous 500-hPa geopotential height response that is too weak. This overly weak response is associated with overly weak subtropical upper-level convergence and a too-weak Rossby wave source in most models, and in several models there is also a biased subtropical Pacific jet, which affects the propagation of Rossby waves. In addition to this overly weak response, all models also simulate ENSO teleconnections that reach too far poleward toward Alaska and northeastern Russia. The net effect is that these models likely underestimate the impacts associated with the MJO and ENSO over western North America, and suffer from a reduction in skill from what could be achieved.

Weather and climate in Southeast Asia are heavily influenced by the Monsoon, with the Monsoon traditionally characterised by the seasonal change in wind directions along with seasonal changes in precipitation. Wind observations or reanalysis data are commonly used for defining the Monsoon period and therefore having wind forecast for the region can provide information on the upcoming onset and cessation of the Monsoon. A spatial approach to the verification of gridded S2S wind forecasts for Southeast Asia is investigated and analysed. The approach uses the fractional skill score (FSS), a neighbourhood spatial verification method commonly used for verifying high resolution precipitation forecast. This spatial approach verification method avoids the problems of conventional local grid-based verification metrics, such as double-penalty when predicting the correct pattern of wind but at the wrong position, and can highlight the spatial displacement of the wind patterns in the forecast. The score for the ECMWF Extended Range wind forecast for Southeast Asia region will be evaluated and compared with the conventional local grid-based verification metrics for the added information using the spatial approach. The possibility of the usage of sub-seasonal wind forecast for the Monsoon onset and cessation forecast in the region will also be discussed.

The Madden-Julian Oscillation is the dominant source of rainfall variability in the tropics on subseasonal timescales. As such, the MJO plays a significant role in forecasting tropical rainfall on 2-3 weeks timescales, and previous studies have shown an improvement in extreme rainfall prediction skill when an MJO event was present. Southeast Asia (SEA) is greatly affected by intraseasonal rainfall variability associated with the MJO, and improvement of subseasonal prediction skill of MJO-related rainfall could benefit both stakeholders and the vast population in the region. In this work, we assess the representation of MJO-related rainfall in subseasonal forecasting models. Specifically, we will explore how well the commonly used MJO monitoring indices can represent rainfall associated with MJO in SEA, both in observations and the models. The skill of the models to simulate MJO-related rainfall in sub-regions in SEA where different MJO phases show the largest subseasonal variability will be examined, as well as the seasonality in prediction skill of MJO-related rainfall in these sub-regions.

Meiyu shows substantial subseasonal variation at periods of 10–30 days and 30–60 days, which often leads to extreme precipitation and disastrous flooding over the Yangtze River Basin. Monitoring and prediction of the subseasonal variation of Meiyu is crucial for disaster prevention and mitigation. Here, we proposed two sets of novel indices for Meiyu real-time monitoring and prediction based on the compound zonal displacements of the South Asia high (SAH) and the western Pacific high (WPH) at 10–30-day and 30–60-day period. For the 10–30-day period of Meiyu, the zonal displacement of the SAH is associated with a mid-latitude Rossby wave train, whereas the WPH is related to the second mode of the boreal summer intraseasonal oscillation. On the 30–60-day timescale, the zonal displacement of the SAH and the WPH are both associated with the first mode of the boreal summer intraseasonal oscillation. The subtle differences in zonal displacement of the SAH and the WPH determine eight type configurations, corresponding to distinct influences on Meiyu. Meiyu subseasonal variation can be well reconstructed by using the relationship between these two indices and rainfall anomalies pattern over China. Given that the ECMWF S2S model is more skillful in forecasting upper- and lower-level circulation than that in directly forecasting precipitation, a hybrid dynamical–statistical model is conducted to predict the Meiyu subseasonal variation with the ECMWF model forecast indices. The hybrid model outperforms the ECMWF model in predicting the Meiyu subseasonal variation at 17–40-day lead times.

In the spring season, Korea suffers from the Asian dust storms (ADSs) sometimes originated from the Gobi Desert and Inner Mongolia. Since the ADSs are known to deteriorate respiratory or eye health as well as visibility, it is important to predict the occurrence and the intensity of ADSs through numerical prediction and satellite observations. In this study, we assimilate aerosol optical depth (AOD) retrieved from Geostationary Environmental Monitoring Spectrometer (GEMS) observing East Asia. The ADSs are affected by various atmospheric (e.g., wind, temperature, planetary boundary layer, etc.) and land surface conditions (e.g., land use, soil temperature, soil moisture, etc.). Therefore, we employ a coupled meteorology-chemistry model --- the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) --- in order to consider the complex interactions among the atmospheric and land surface variables simultaneously. The Maximum Likelihood Ensemble Filter (MLEF) is adopted to assimilate AOD. For an ADS case in March 2021, it is shown that the GEMS AOD assimilation improves the analysis and prediction of AOD and is useful for ADS prediction in the East Asia region.

In air quality prediction, initial conditions are essentially required for a coupled atmosphere-chemistry model which is based on both atmospheric and aerosol/chemistry observations. In general, a higher amount and quality of observations produce more accurate model results; however, forecast errors in a region of interest may grow from an initial error in a specific upstream region, primarily due to the lack of observations therein. Therefore, it is a milestone to find the upstream areas from which a small initial error can grow into large forecast errors in the region of interest. The conditional nonlinear optimal perturbation for initial conditions (CNOP-I) is a suitable tool for adaptive (targeted) observations. To calculate the CNOP-I, several variables are included in defining multiple energy equations (e.g., kinetic energy, dry energy, moist energy, etc.). In particular, the previous studies on improving the Asian dust storms (ADSs) prediction mainly considered the kinetic energy calculation, which included only two variables (u-wind and v-wind). The goal of this study is twofold: finding variables that strongly affect the ADSs and identifying sensitive areas for the adaptive observations. We collected and examined the ADS events that occurred during the last 32 years (1990—2021) over South Korea. Through the principal component analysis, we first recognized top variables—temperature, vertical velocity, divergence, specific humidity, ozone mass mixing ratio, and eastward wind—which affect origination and translocation of the ADSs; for example, strong downdraft and divergence make ADSs finally land on the Korean Peninsula. We are applying the CNOP-I to identify the sensitive areas for adaptive observations in air quality prediction. We expect to improve air quality forecasts by classifying the synoptic situations that brings about severe ADS outbreaks in South Korea and by identifying the upstream areas for adaptive observations to which we can enhance observations, potentially through international collaborations.

The Indo-Pacific warm pool (IPWP) is a region with one of the strongest atmospheric convections on Earth and deep convection over the IPWP plays a key role in global climate. In recent decades, it has experienced human-induced warming. In addition, it has been observed to have a non-linear relationship between Sea Surface Temperature (SST) and precipitation. Even though SST is increasing, the increase in precipitation is limited up to a specific SST, defined as saturation threshold SST (STT). Existing STT indicates that a completely different state occurs before and after STT showing a non-linear response of precipitation to SST. However, how warmer climates affect changes in the SST-precipitation relationship as well as STT is yet to be unexplored. To investigate future changes in the relationship between SST and precipitation, we used a joint distribution of the two variables with the historical data as well as three Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5) during the near future (2041-2060), and far future (2081- 2100). The results demonstrate that with the shift of mean state, the STT rises due to the involvement of atmospheric stratification in all three scenarios in both future periods, but the largest increase is observed in the SSP5-8.5 scenario during the far future. In the warming climate, greater warming in the upper troposphere compared to the surface leads to stabilization of the troposphere, supporting an increase in STT through Moist-Adiabatic Lapse Rate (MALR) adjustment. This study suggests a change in the SST-precipitation relationship and a possible mechanism for a limited increase in precipitation, and thus provides a background for a better understanding of a non-monotonic precipitation response to SST in the context of climate change.

Convectively-coupled Kelvin wave (CCKW) is one of the important large-scale oscillations in the equatorial area. It is an eastward-propagating mode that constantly causes fluctuations in pressure and winds, and thus, CCKW takes part in shaping the weather and climate through bringing countless rainfall events to countries in tropics. CCKW is relatively active in the Pacific and the Indian basins due to the absence of obstruction from topography, and yet it shows distinct properties over these two regions, including the intensity, active season, symmetry, and propagation speed. The vertical profiles also suggest the differences in tilting angle and the extent of low-level pre-moistening at the leading edge of the convection. It is hypothesized that the differences arise from the mechanisms in triggering the convection associated with CCKW. We conduct a detailed moist static energy (MSE) budget analysis to uncover the dominant factors in driving the energy recharge-discharge process of CCKW. The results indicate that the key factors might lie in the horizontal advection and moisture profile, especially within the boundary layer. The gross moist stability, a measure of development of moist convection, is also tested over the two regions. It is speculated that the background environmental conditions play a major role in determining the structures of CCKW.

In this work, the spatial and temporal distribution characteristics of 171 QLCS cases in North China identified by an objective identification method for the period 2013—2018 are analyzed. According to the statistics of intense weather produced by them, at least two types of QLCSs exist: one type with strong thunderstorm wind gusts and the other with extreme heavy rainfall. Furthermore, the characteristics of circulation pattern, environmental condition, terrain effect, and surface cold pool as a key type of mesoscale system are given for the two types of QLCSs. The findings are presented as follows. The spatial distribution of QLCSs in North China. There are significant differences in the month of occurrence, the spatial scale, the moving speed, the formation time and maintenance period between the two types of QLCSs. The circulation background, environmental conditions and cold pool are also obviously different. The atmospheric baroclinicity is relatively obvious for QLCSs with strong thunderstorm wind gusts. The large value area of BCAPE and DCAPE caused by the dry middle layer and large temperature reduction rate are important environmental conditions for the generation of strong convective wind gusts. The strong cold pool and vertical wind shear within the layer of 0—3 km altitude play an important role in the forward propagation of the QLCSs. Extreme precipitation caused by QLCSs with heavy rainfall is more prominent for this type of QLCSs than that for the previous type. The second type of QLCSs usually occur in weak synoptic-scale forcing systems with sufficient water vapor supply. The back propagation maintained by the interaction between the weak cold pool or the windward slope and the low level southerlies is the main mechanism for the development and slow movement of the QLCS, which is also directly responsible for extreme heavy rainfall.

The latest Multi-angle Imaging Spectro Radiometer (MISR) V23 aerosol optical depth (AOD) products were released, with an improved spatial resolution of 4.4 km, providing an unprecedented opportunity for the refined regional application. To ensure the reliability of their applications and build a scientific reference for the further optimization, it is imperative to conduct a comprehensive evaluation, especially for the unique size-resolved AOD products: small-size AOD (AOD_S, representing the contribution of fine-mode aerosols), medium-size AOD (AOD_M), and large-size AOD (AOD_L), and AOD_M+L represents the AOD part of coarse-mode aerosols. AErosol RObotic NETwork (AERONET) and MODerate-resolution Imaging Spectroradiometer (MODIS) C6.1 aerosol products from 2001 to 2020 are utilized for the validation, analysis, and inter-comparison, considering three spatial scales and four key factors. In general, MISR V23 aerosol products show a good accuracy compared with AERONET. The best performance for all AOD products appears in forest units (the highest R ~ 0.93, data percentage within Expected Error bounds, %EE > 93), related to the inactive human activity and dark underlying surface. Dependences of retrieval deviations illustrate that the performance of MISR AOD deteriorates as aerosol loading increases. Namely, with the increase of aerosols, total AOD (AOD_T) and AOD_S show increasing negative deviations, while increasing positive deviations are observed for AOD_M+L. This suggests that the Empirical Orthogonal Functions do not perform well in this situation, since numerous aerosol particles can obstruct the underlying reflection and reduce the surface spectral contrast. In addition, AOD_T and AOD_S often exhibit anomalous positive deviations in areas with low vegetation cover, such as deserts, revealing that MISR will overestimate aerosol content over bright surfaces and in environments dominated by coarse-mode particles. The above findings not only deepen the understanding of MISR aerosols products from multiple perspectives, but also provide useful information for the product improvement.

The global methane pledge paves a fresh, critical way toward Carbon Neutrality. However, it remains largely invisible and highly controversial due to the fact that planet-scale and plant-level methane retrievals have rarely been coordinated. This has never been more essential within a narrow window to reach the Paris target. Here we present a two-tiered spaceborne architecture to address this issue. Using this framework, we patrol the world, like the United States, China, the Middle East, and North Africa, and simultaneously uncover methane-abundant regions and plumes. These include new super-emitters, potential leakages, and unprecedented multiple plumes in a single source. More importantly, this framework is shown to challenge official emission reports that possibly mislead estimates from global, regional, to site scales, particularly by missing super-emitters. Our results show that, in principle, the above framework can be extended to be multi-tiered by adding upcoming stereoscopic measurements and suitable artificial intelligence, thus versatile for immediate and future monitoring of the global methane pledge.

This study used Weather Research and Forecasting model (WRF) 4.4 and Weather Research and Forecasting model data assimilation system (WRFDA) 3D-var assimilation system to analyze and predict two extreme precipitation events in North China during summer 2022. Based on the system, different satellite data were assimilated in the initial field to improve the accuracy of the model forecast. The satellite data used in this study are NOAA AMSU-A/B, FY-3D MWHS-II/MWTS-II, FY-4A AGRI and Himawari-8 AHI. Artificial intelligence algorithm was applied to these satellite data to retrieve atmospheric temperature and moisture profiles, and these results were used for data assimilation in the system. In this study we have designed three experiments: 1 control run and 2 experiments. The control run only assimilated conventional data to the initial field. Besides the conventional data, the first experiment assimilated AI retrieved atmospheric temperature and moisture profiles and the second experiment assimilated brightness temperature from these sensors. We conducted 72 hours forecast for all the experiments and evaluated the precipitation amount and area with observed data. The results showed that the AI algorithm that we have developed in this study performed well in atmospheric temperature and moisture profile retrieval and the temperature and moisture profiles had positive contributions to precipitation forecast. According to TS, FAR and POD sores, the second experiments showed high accuracy in 0-6 hour precipitation forecast.

Total precipitable water (TPW), a column of water vapor content in the atmosphere, is an important meteorological factor and play a critical role in the occurrence of precipitation and atmospheric river. Traditionally TPW has been calculated from using vertical profiles of temperature and humidity. But operational TPW derived from GK2A provides under only clear sky conditions and TPW calculated from radiosonde observations provides in several stations at 4 times in a day. To overcome temporal and spatial limitation of these TPW, this study proposes a retrieval algorithm based on convolutional neural network (CNN) model to retrieve TPW from geostationary Korea multi-purpose satellite 2A (GEO-KOMPSAT 2A, GK2A) equipped with the Advanced Meteorological Imager (AMI). The TPW is prepared by integrating the water vapor profiles from radiosonde observations over Korea. The algorithm performances are assessed using radiosonde observations for other period as the reference data. Through statistical verification, the results show that the CNN model performs with a root mean square error (RMSE) of 9.42 (5.32) mm, a bias of 0.02 (-0.76) mm, and a correlation coefficient of 0.88 (0.94) under all (clear) sky conditions. This study addressed the availability of satellite-derived TPW to resolve the limitations of in situ measurements and cloudy sky conditions. Both the quantity and the quality of data are important in machine learning approach. We will expand training data like as GNSS and numerical weather forecast model to improve retrieval of TPW.

The National Meteorological Satellite Center of the Korea Meteorological Administration has developed an algorithm to retrieve the atmospheric profiles for Geostationary Interferometric Infrared Sounder (GIIRS) onboard FengYun-4A/B (FY-4A/B). Hyperspectral infrared sounder with thousands of channels has a high vertical resolution, so it can estimate more accurate atmospheric temperature and humidity profiles and is useful for monitoring severe weather such as typhoons and heavy rainfall because it highly depends on atmospheric instability and water vapor information. To develop the algorithm for GIIRS of FY-4A/B, channel selection, and systematic bias correction were performed. The channel was chosen to consider the vertical distribution of the weighting function, except for the channel with strong trace gas absorption and channels with large observation error among 1650 channels of FY-4A GIIRS and 1682 channels of FY-4B GIIRS. The retrieved temperature and humidity profiles with GIIRS/FY-4 for the summer of 2022 were compared with radiosonde and the profiles of the GK2A AMI. The validated results of GIIRS/FY-4 were similar to the GK2A even though more channels were used. So it is needed various sensitivity tests to get more accurate temperature and humidity profiles using hyperspectral infrared sounder such as the number of channels and wavelength selection used by the algorithm. The detailed results and plans are presented at the conference. This work was funded by the Korea Meteorological Administration’s Research and Development Program “Technical Development on Weather Forecast Support and Convergence Service using Meteorological Satellites” under Grant (KMA2020-00120)

This study investigates the radial distribution of deep convective clouds 24 hr prior to the rapid intensification (RI) of the tropical cyclones (TCs) in the Western North Pacific. TCs are categorized according to the 24 hr future intensity (V_max) change (ΔV_max), as follows: non-RI (ΔV_max < 30 kt), short RI (RI-S, ΔV_max > 30 kt for less than a day), and long RI (RI-L, ΔV_max > 30 kt continuously for at least 1 day). RI-S TCs have the strongest intensity and shortest RMW within 24 hr prior to RI, whereas non-RI TCs have the weakest intensity and longest RMW within 24 hr prior to its lifetime maximum V_max. In all categories, highest DCC percentage (DCC-P) and coldest temperature (DCC-T) are found within 3 RMW from the storm’s center. Non-RI has the lowest DCC-P and warmest DCC-T anywhere within 10 RMW, whereas RI-L has the highest DCC-P and coldest DCC-T within 2 RMW especially at the onset of RI. DCC-P starts to increase 12 hr prior to RI with sharpest 3-hr increase of 17% at 2-3 RMW in RI-L. DCC-T within RMW is always colder than that within 2-3 RMW, 9 hr prior to RI. Sharpest DCC-T difference of 3^oC is found in RI-L, at least 3 hr prior to RI. Results in the study can be used to identify the TCs that are more likely to undergo prolonged RI even before the RI onset.

Alisov's climate classification was proposed in 1954, and it focuses on the January–July changes in large-scale air mass zones and their fronts. In this study, data clustering by machine learning was applied to global reanalysis data assimilated from some satellite data to quantitatively and objectively determine air mass zones, which were then used to classify the global climate. The differences in air mass zones between two half-year seasons were used to determine climatic zones, which were then subdivided into continental or maritime climatic regions or according to east–west climatic differences. This study began by questioning whether the global climate can be divided into four air mass zones as Alisov did in the 1950s. The results showed that Alisov's four air mass zones from the 1950s were supported from a modern data-driven perspective using high-quality global reanalysis data. In addition, the clustering technique accurately captured frontal precipitation between air mass zones in the mid-and high latitudes. This study, thus, renews Alisov's climate classification for the first time in almost 70 years and applies data-driven machine learning to establish a standard for air-mass-based climate classification. This paper has been peer-reviewed once in Progress in Earth and Planetary Science (PEPS) and a revised manuscript is in preparation.

Numerical prediction models cannot correctly predict the extended-range (10-30-day) precipitation amount and pattern over Taiwan due to the low predictability. However, demand for extended-range precipitation forecasts by users in different sectors has grown significantly. In this study, a statistical post-processing technique combining Analog Post-processing (AP) and Probability-Matched mean (PM), called APPM, is used to perform bias correction and downscaling for week 3-4 precipitation forecasts in Taiwan. The purpose is to provide users with more accurate Quantitative Precipitation Forecasts (QPF) and more reliable Probabilistic Quantitative Precipitation Forecasts (PQPF). Previous study shows that 1-14-day post-processed precipitation forecasts using APPM, including QPF and PQPF, are promising. Here we want to confirm that the APPM method also works for the week 3-4 forecast range, which was thought to be a “predictability desert” with little forecast skill. Forecast evaluation shows that the raw ensemble is under-dispersive, while the calibrated ensemble distribution well represents the observation variability. Compared to the raw forecasts, the AP-based probabilistic forecasts have better reliability and higher skill in discrimination. Users with a much wider spectrum of cost/loss ratio can benefit more from the calibrated forecasts in decision making as compared to the raw forecast. In addition, the calibrated QPF removes most bias and displays obviously higher correlation and reduced error.

This study examines the environmental conditions that influences the tropical cyclone (TC) occurrence frequency (TCOF) as well as the rain microphysics of TCs over the North Indian Ocean (NIO) during major monsoon stages (pre-monsoon, monsoon, and post-monsoon) in 2014-2021. Two regions in NIO domain are a particular focus, namely, the Arabian Sea (AS) and the Bay of Bengal (BOB). TCs that formed within the NIO domain and reached at least the tropical storm stage were selected. High TCOF is attributed to the combined presence of warm sea surface temperature and cyclonic wind or positive 850-hPa relative vorticity. Moreover, high reflectivity and heavy rainfall are found within the 50 km radius from the storm’s center. Monsoon season TC Vayu (2019) have the heaviest rainfall and highest reflectivity within the inner band region, as compared to pre-monsoon TC Fani (2019) and post-monsoon TC Kyaar (2019). The probability density functions of the rain microphysics parameters vary with the season (pre-monsoon, monsoon, and post-monsoon) and the mode of rain (total, stratiform, and convective). In all season, the breakup process is found to be dominant in total and stratiform precipitating clouds, whereas the break-up and coalescence processes are comparable in convective precipitating clouds.

The Mei-yu season over Taiwan, mainly associated with frontal systems, is a transition period (May and June) between the winter and summer seasons. The interactions of Mei-yu season frontal systems with the complex topography of Taiwan lead to heavy precipitations across the island. The present study investigates rain and microphysical attributes of the Mei-yu season over Taiwan using Global precipitation measurement mission dual-frequency precipitation radar (GPM DPR). To analyze the regional and intra-seasonal characteristics of the Mei-yu season, May and June are segregated into pre-Mei-Yu (5/15-5/31), mid-Mei-Yu (6/1-6/15), post-Mei-Yu (6/16-6/30). There are clear distinctions in the raindrop size distributions among pre-, mid-, and post-Mei-yu seasons, with abundant large drops in the post-Mei-yu. Furthermore, raindrop size distributions also showed apparent differences among four regions (north, south, central, and eastern part) of Taiwan, with relatively bigger drops in central Taiwan. The contoured frequency by altitude diagrams of rain rate, radar reflectivity, mass-weighted mean diameter, and normalized intercept parameters are used to understand the microphysical processes responsible for the regional and intra-seasonal variations. 

From the climatological views, 20-yr (1999-2018) June CFSR Dongsha wind vertical profiles are used to define the MBLJ days. It is found that when MBLJ occurs at Dongsha, the above 90/95-percentile extreme rainfall occurrence frequency over low land (< 1km) of western Taiwan and northern Taiwan is larger than that of NoTC days, with the peak values occur over southwestern slope of the Snow Mountains and the Central Mountain Range, and southwestern Taiwan. It is similar that the above 90/95-percentile extreme rainfall occurrence frequency over high land (> 1 km) of Taiwan is larger during MBLJ days than NoTC days, with peak values occur over southwestern slope of the Snow Mountains and the Central Mountain Range. During early summer rainy season (June) over the South China Sea, there are six CMBLJs (Coastal MBLJs) with occurrence frequency peaks (> 35 %). Their formation mechanisms are related to terrain. (1) and (2) BLJ-East and BLJ-West (East and West of Hainan Island over South China); (3) and (4) Orographic enhanced southwesterly (southwesterly flow impinging on southeastern tip of Vietnam and Taiwan); (5) Barrier jet off the northwestern Taiwan; (6) Gap wind flow through mountain gap over central eastern Vietnam.

Under climate change, the number of typhoons in the Northwest Pacific region will decrease and the proportion of strong typhoons will increase. In order to understand whether the interdecadal changes in the ocean and atmospheric environment affect the development of typhoon intensity. This study compares the changes in the ocean and atmospheric environment from June to November in the past two decades (1980-1999) and the current twenty years (2000-2019), and discusses the relationship between changes in the ocean and atmospheric environment and the typhoon intensity. Compared with the past, the sea surface temperature has increased by an average of 0.42°C, the ocean mixed layer depth has deepened by an average of 2.7 meters, and the ocean heat content (OHC) has increased by 9.89 kJ/cm². The results show that under climate change, the thermal environment of the upper ocean will be more conducive to the development of typhoon intensity. In terms of atmospheric environment, the Pacific subtropical high strengthened and extended westward, and the monsoon trough also tended to retreat westward compared with the past. Under climate change, atmospheric environment has both positive and negative contributions to the development of typhoon intensity, and changes in the Pacific subtropical high, monsoon trough, and vertical wind shear will be unfavorable for the formation of typhoons. Using the observed maximum wind speed of typhoon and potential intensity to analyze the relationship between ocean and atmospheric environment and typhoon intensity. The results show that compared with the atmospheric environment, the ocean environment has a more direct relationship with typhoon intensity. Compared with sea surface temperature, OHC has a higher relationship with typhoon intensity. Moreover, the correlation between OHC and stronger typhoon is even more higher than that of weak typhoon. In conclusion, among all variables, OHC has highest correlation with typhoon intensity.

The coronavirus disease 2019 (COVID-19) is responsible for the worldwide disorder in livelihood and enormous loss of human lives. To curtail the COVID-19 spread, the government of China imposed a lockdown over most of its provinces from 23 January to 25 March 2020. The effects of the COVID-19 lockdown on public health, transport, aviation, and meteorological parameters were well documented globally; however, its influence on rain microphysics is not fully understood. In the present study, we tried to report the distinction in precipitation microphysics, especially the raindrop size distribution between the COVID-19 lockdown (23 January to 25 March 2020) and non-lockdown (23 January to 25 March: 2015- 2029) periods over Taiwan. The raindrop size distributions between the lockdown and non-lockdown periods showed clear distinctions, with more mid- and large-size drops during the lockdown periods. Furthermore, possible microphysical differences between the lockdown and non-lockdown periods are explored using reanalysis and remote-sensing data sets.

Studying the glacial climate provides opportunities to examine the change in climate systems in response to external forcing, e.g., solar radiation, greenhouse gases and the abrupt change in cryosphere. In addition, the climate is one of its external conditions which impacts the development of safety assessment scenarios, such as glacial and interglacial conditions causing sea level variations and the surface system changes. For example, the change in sea level in glacial and interglacial periods strongly determines the groundwater system and the ecosystem. The groundwater system dominates the flow rate which influences the hydrogeological condition in the near field and the potential release path from underground facility to the ecosystem in the far field. Also, the landform controls the release location of radioactive and how it will influence the biosphere. To have a better picture for the processes above and the potential risks, we leverage a GCM for simulating the glacial climate and downscale to the regional states of Taiwan. Specifically, we use ISCA, an idealized global circulation modelling framework, to simulate the glacial climate since 12,000 B.P. (last glacial maximum, LGM) until the present day. Following the standard of Held–Suarez experiment, we modulate the global energy budget through changing TOA radiation, solar zenith angle and proxy surface temperature (due to changed terrain in Taiwan and adjacent region) to mimic different climate states. Our study demonstrates the potential for safety assessment of spent nuclear fuel disposal repository based on model outputs from the idealized GCM along with the regional model downscaling.

The afternoon thunderstorm event is the common nature disaster in Taiwan area. Because of the short life cycle and high variety, it is difficult to observe the microphysics feature for whole system, especially the Drop Size Distribution (DSD), one of the fundamental microphysical parameters. Using dual-polarimetric radar to retrieve DSD is useful to obtain the variation of microphysical processes for the entire weather system. In general, the retrieved method is based on gamma distribution to reduce the degree of freedom and get the DSD parameter, such as shape parameter (µ), slope parameter (Λ), and intercept parameter (N₀). However, the shape of DSD is incompatible with the gamma distribution if the specific microphysical processes are dominated. There is a new method with the moment-based operator to convert the dual-polarimetric variables to the moments of DSD which can avoid the model error. In this study, the new retrieved method is used to study DSD during different life period of the afternoon thunderstorm in Taipei area. Moreover, we try to categorize the feature of DSD under the influence of collisional coalescence, breakup, or drop settling processes. Base on the results of retrieved DSD, the evaporation and coalescence are the main microphysics processes in initial stage and mature stage respectively.

In Summer, high temperatures and humidity are suitable for forage planting, but long days of sunshine are needed for hay preparation in this period to cope with the lack of fresh forage in autumn and winter over Taiwan. Therefore, the livestock industry has a demand for probabilistic forecasts of consecutive days without rainfall. However, due to the complex topographic distribution in Taiwan, rainfall forecast has high uncertainty in space and time and it is very difficult to capture the fine information from model output with low resolution. The purpose of this study is to provide users with reliable and skillful forecasts, which help users obtain more economic benefits in decision making. In this study, Analog post-processing (AP) is applied in 20-year re-forecasts of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) to produce calibrated and downscaled probabilistic forecasts of consecutive 5 days without measurable rainfall over Taiwan. The evaluation results show that (1) The under-dispersion of the raw forecasts is effectively improved through the AP method. (2) The probabilistic forecasts calibrated by AP method have better reliability, brier skill score and discrimination (potential usefulness) than raw forecasts. (3) The calibrated probabilistic forecasts can provide higher relative economic values compared to the raw forecasts, and it indicates the enhancement of forecast value.

Radar echo extrapolation utilizes the observed composite reflectivity to estimate the motion fields of radar echoes and provide advection and rotation information for extrapolation. The uncertainty of motion fields is one of the major error in radar extrapolation. In addition, the weather system may have different moving directions at different heights. In this study, 3-Dimension motion fields is estimated by the entire volume scanned data and applied to MAPLE (McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation) nowcasting system. With autumn precipitation events in the Yilan area and the Meiyu front events in Taiwan, the characteristic of 3D motion fields in space and time are analyzed. The results show that u-velocity changes a lot in both time and vertical levels in Mei-yu front event. As for autumn precipitation, the standard deviation and mean of u-velocity are similar to v-velocity. Then, the added value of 3D motion fields for the nowcasting is evaluated by continuous and categorical verification. It is found that when applying 3D motion fields in radar extrapolation nowcasting, it can capture more complete advection and rotation due to orographic effect over Taiwan area. Overall, the improvement of the nowcasting with 3D motion fields can be up to 3-h.

This study mainly discusses the synoptic environment, observed wind speed, wind direction and raindrop size distribution (DSD) when typhoon Maria (2018) and Lekima (2019) approach Taiwan. When typhoon Maria moves westward approaching northern Taiwan coastal ocean, the near surface wind speed strengthens to > 17 m/s which is observed by wind profiler at National Central University (NCU) over northwestern Taiwan. In addition, the observed wind direction using profiler gradually changes from northwesterly to southwesterly. The mass-weighted mean diameter in millimeters (Dm), observed by NCU impact-type Joss-Waldvogel disdrometer (JWD), gradually decrease to ~ 1.5 mm. On the other hand, typhoon Lekima moves northwestward from east of Taiwan. The NCU wind profiler observed near surface wind speed is smaller than 17 m/s and wind direction changes gradually from northeasterly to southwesterly. The Dm observed by NCU JWD changes irregularly as time processes. Research results show that wind speed and wind direction have impacts on DSD. Moreover, observed radar reflectivities show that the principal rainband of typhoon Maria passes northern Taiwan. At that time, the NCU JWD observations show that Dm is large (~ 2.5 mm) and the continuous rainfall > 12 hr. On the other hand, only the distant rainband of typhoon Lekima passes northern Taiwan and the observed Dm is small. The rainfall maintains less than 4 hr. Research results show that if principal rainband passes Taiwan or not influences DSD and the duration of rainfall.

From 0200 to 1000 LST 2 June 2017, the shallow, east–west-oriented mei-yu front (<1 km) cannot move over the Yang-Ming Mountains (with peaks ∼1120 m) when it first arrives. The postfrontal cold air at the surface is deflected by the Yang-Ming Mountains and moves through the Keelung River and Tamsui River valleys into the Taipei basin. The shallow northerly winds are anchored along the northern side of the Yang-Ming Mountains for 8 h. In addition, the southwesterly barrier jet with maximum winds in the 900–950-hPa layer brings in abundant moisture and converges with the northwesterly flow in the southwestern flank of the mei-yu frontal cyclone. Therefore, torrential rain (>600 mm) occurs over the northern side of the Yang-Ming Mountains. From 1100 to 1200 LST, with the gradual deepening of the postfrontal cold air, the front finally passes over the Yang-Ming Mountains and arrives at the Taipei basin, which results in an east–west-oriented rainband with the rainfall maxima over the northwestern coast and Taipei basin. From 1300 to 1400 LST, the frontal rainband continues to move southward with rainfall over the northwestern slopes of the Snow Mountains. In the prefrontal southwesterly flow, the orographic lifting of the moisture-laden low-level winds results in heavy rainfall on the southwestern slopes of the Snow Mountains and the Central Mountain Range. With the terrain of the Yang-Ming Mountains removed in the high-resolution model, the mei-yu front moves quickly southward without a rainfall maximum over the northern tip of Taiwan.

Global cloud-resolving climate simulation is believed to promote our understandings of the atmospheric phenomena with multi-scale nature and also improve reliability of their climate projections such as tropical cyclones and cloud feedback. With this background, the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) has been developed and used for more than two decades. The first realistic global cloud-resolving (here, 3.5 km mesh) simulation was published in 2007, and its extension toward climate scale is now in sight with the supercomputer Fugaku, the latest flagship machine of Japan. The value of such climate simulation is guaranteed by a series of recent model improvements to better simulate both mean state and disturbances. We are also revealing the climatological performance of the ocean-coupled version of NICAM through a series of seasonal to decadal simulations. Other important challenges include a global O(10³) ensemble prediction experiments with 14 km mesh and a global 220 m experiments toward an era of global large-eddy simulation. The above challenges on the supercomputer Fugaku would be harder without an effective co-design activity with the application developers. In terms of post-processing, we recently modified our MPI-based remapping tool and confirm its applicability to a global 870 m mesh data.

Mesoscale convective systems (MCSs) contribute a large fraction of warm-season precipitation and generate hazardous weather with substantial socio-economic impacts. Uncertainties in convection parameterizations in climate models limit our understanding of MCS characteristics and reliability of future projection. We examine MCSs simulated by the global 14-km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) without cumulus parameterization against satellite observation from Global Precipitation Measurement (GPM) during 2001-2008. We focus on MCSs over the central U.S. and eastern China where MCSs are prevalent from March to August. A process-oriented tracking method incorporating both cloud and precipitation criteria is used to identify and track MCSs. About 140/100 MCSs initiate in the central U.S./eastern China per warm season and most of them initiate east of high mountains and in coastal regions. The frequency distribution of MCS lifetime is well captured by NICAM. However the simulated MCSs have stronger precipitation, smaller precipitation area, and larger cold cloud system than observed in both regions, which may be caused by weak entrainment as it is not well resolved at 14 km resolution. The simulated MCS number is also underestimated in summer. By examining the climatological and MCS large-scale environments, the significant underestimation of MCS number in summer over the central U.S. may be attributed to the large climatological dry bias in the atmosphere. For China, mean moisture in summer is well simulated but deficiency in capturing the dynamic condition related to the coastal topography for triggering convection may have contributed to underestimation of MCS even in a sufficiently moist environment.

Machine learning technology has recently been applied to weather and climate simulations. Compared to atmospheric models that include various algorithms, data-driven models can reduce the amount of computation (= elapsed time) and achieve significant speedups. A data-driven model for weather forecasting is being developed using datasets such as weather reanalysis (e.g., ERA5) and multi-model intercomparison (e.g., CMIP6). However, for meteorological/climate researchers who want to elucidate processes and mechanisms, data-driven models shouldn't be used as complete black boxes. We need high-speed, on-demand "surrogate models" to imitate the physical models that algorithms are based on understanding processes and continuously developed.
We developed a coupling library h3-Open-UTIL/MP (Arakawa et al., 2022) to bridge legacy weather/climate models and modern machine learning tools. This library was initially designed to connect each component of the earth system model. We extract the necessary training data from the part of the model code written in Fortran and pass it to ML frameworks in Python on-the-fly. The proposed system enables the creation of the necessary surrogate models step-by-step using a large amount of simulation data. We will present an overview of the library and initial experimental results.

Nowadays, extreme precipitation has brought very serious losses. How to timely and accurately forecast precipitation has become the top priority of disaster prevention. The emergence of deep learning technology has provided a new idea for the precipitation nowcasting, and achieved good results in the short forecast period, but there is still a problem that as the forecast period becomes longer, the forecast effect becomes worse. On the basis of deep learning technology, this study attempts to introduce additional physical information and simulate the future information of numerical model forecast to improve the accuracy of precipitation nowcasting. This study uses precipitation data, and two-dimensional water vapor flux data in the input and forecast stages respectively. Because there are errors in the real forecast, random errors are introduced when additional future data is added into the forecast stage. The results show that, compared with the simple application of precipitation data, the introduction of more physical information in the input stage can improve the prediction effect by 4.75%, while the introduction of predicted future physical information in the forecast stage can improve the effect by 16.69%, showing a strong application prospect.

Urban expansion has been rapid in North China Plain (NCP) since 2000, and the metropolitan area of 2015 increased by 4.92% compared with 2000. The change in Land-Use and Land-Cover (LULC) directly affects the meteorological conditions, impacting the atmospheric pollutant concentrations. Therefore, based on the MERIS300 LULC product of 2015, this study simulates a continuous heavy pollution incident from 05 December 2015 to 02 January 2016 in NCP, to explore whether the urban expansion causes LULC changes that will worsen the atmospheric environment. The T2 increases by 0.19 ℃ while the relative humidity (RH) decreases by 1.28%. Urbanization aggravates the pollution of O₃ but reduces the other four concentrations of pollutants. The concentration of PM_2.5 increased significantly. In addition, the trends of PM_2.5 and O3 are significantly negatively correlated in vertical distribution. Therefore, it is necessary for PM_2.5 and O₃ to cooperate in emission reduction.

Changes in land use and land cover (LULC) influence meteorological fields and biogenic emissions, further affecting the atmospheric chemistry and air quality. Combing the satellite measurements and WRF-Chem model simulations, we evaluate impacts of the LULC change between 2001 and 2018 on the summertime ozone (O₃) formation in North China Plain and surrounding areas (NCPs). Satellite measurements have revealed that from Taihang to Yanshan Mountain, the fraction of broadleaf and needle forest coverage has increased by 5%-20% and the urban area has increased by up to 25% in the NCP. Additionally, the vegetation density has increased significantly in the NCPs except for urban areas. The LULC change generally enhances biogenic volatile compounds emissions in the NCPs, particularly over Taihang and Yanshan mountain, but the O₃ variation is divergent. The maximum daily 8-hours average (MDA8) O₃ concentrations are reduced by 1%-7% over Taihang and Yanshan Mountain because the raised vegetation density increases O₃ dry deposition velocity to accelerate the O₃ loss. The raised vegetation density enhances the evapotranspiration to decrease the near-surface temperature by 0.1 ℃ ~ 1.5 ℃, which also generates a divergence in the low-level atmosphere in the NCPs, causing secondary northerly or easterly winds in the NCP. The O₃ enhancement along the coastal areas of the NCP is attributed to the perturbation of wind fields and photolysis induced by the LULC change. The divergent variation of the MDA8 O₃ concentrations in the NCP is generally caused by the variations of biogenic emissions and photolysis.

Solar farms consist of arrays of solar panels to convert solar energy to electricity. Building solar farms has become an acknowledged approach to mitigate global climate change. Meanwhile, solar farm deployments change the surface radiative properties and, thus, can impact the local surface radiation budget and climate. So far, global modeling studies have primarily focused on simulating the impact caused by surface albedo changes due to large-scale solar farm deployments. Such modeling studies lack long-term observational constraints and do not consider complex atmosphere-panel-surface interactions. Using Moderate Resolution Imaging Spectroradiometers (MODIS) aboard the NASA Aqua satellite, we have quantified the change of surface spectral reflectances, spectral emissivities, and land surface temperature over six solar farms commissioned in the southwestern U.S. Results suggest a 20-25% reduction of surface reflectance over the seven MODIS shortwave bands due to the solar panel installation, leading to a ~23% decrease in the surface upward shortwave broadband flux and a ~14-18% decrease in the clear-sky reflected shortwave flux at the top of the atmosphere. Despite the uncertainty and disagreement in current surface retrieval algorithms, results show that the outgoing longwave radiances over the MODIS infrared window channels are reduced. Based on these observations, we will implement a more realistic representation of solar farms in global climate models. Specifically, we will put a translational layer in the Community Terrestrial System Model, the land component in CESM. This layer will modify the radiative and momentum fluxes given solar panels’ geometrical and radiative properties on top of the natural surfaces. The new scheme can output panel temperature to be compared to and explain in-situ and satellite observations. It will help us understand the potential impact on the climate system if an increasing number of solar farms are deployed in the future.

The critical zone (CZ), extending from the bottom of groundwater to the top of the lower atmosphere, plays a crucial role in shaping the environment and human well-being (Brantley 2007). The CZ and climate systems are affected by various natural and human-driven processes. In this study, we utilized a modified version of CESM2 that incorporated data on groundwater, irrigation, and human water use. The groundwater scheme has also been enhanced to simulate lateral flow and aquifer pumping. To assess the impact of irrigation on climate, we conducted two experiments spanning from 1901 to 2014: one with a fixed irrigation demand in 1901, and the other with a changing irrigation demand (Sibert 2015). Our results indicate that irrigation is concentrated in India, China, and North America. In India, the irrigation effect leads to increased soil moisture, latent heat, and decreased sensible heat, resulting in a cooling effect. Rainfall is also seen to increase, particularly in the Ganges river region. Conversely, the west coast of the USA experiences contrasting feedback with decreased rainfall and soil moisture. References: Brantley SL, Goldhaber MB, Ragnarsdottir KV. (2007). Crossing disciplines and scales to understand the Critical Zone. Elements 3: 307-314. Siebert S, Kummu M, Porkka M, et al. A global data set of the extent of irrigated land from 1900 to 2005[J]. Hydrology and Earth System Sciences, 2015, 19(3): 1521-1545.

Severe groundwater depletion due to intensive irrigation has begun to emerge and has been clearly detected by satellite-based estimates in major agricultural areas, particularly the North China Plain. The effects of groundwater overexploitation are not restricted locally, but an incursion of dormant groundwater into the active hydrological cycle can critically affect the hydroclimatology of remote places and the global water budget, which eventually leads to terrestrial contributions to rising sea levels. This study explores how much groundwater depletion as a result of intensive irrigation will contribute to accelerating global sea level rise. For this, the long-term simulations with/without groundwater pumping are performed using the fully coupled configuration in Community Earth System Model (CESM). The groundwater-fed irrigation is activated in the North China Plain, the entire China, and global scale to discern different responses to the locations of the irrigation forcing. A comparative analysis of a suite of sensitivity experiments can facilitate identifying the obscure effect of groundwater pumping from the background climates. In particular, the quantification of the contributions of groundwater pumping to sea level changes will be focused, which will strengthen our understanding of the nonlinear climate processes that occur when the atmosphere, land surface, and subsurface are perturbed by groundwater pumping. [Acknowledgements]This research was supported by project GRF16309719, which was funded by the Research Grants Council (RGC) of Hong Kong. 

Wildfire hazards have witnessed a substantial increase in frequency, extent, and severity globally. While wildfire outbreaks and spread are strongly affected by non-climatic factors such as campfires, forest management, and monitoring capacity, there is a growing scientific consensus that increasing temperatures and resultant dryness may be responsible for a favorable environment for wildfires. A potential escalation of wildfires under global warming could result in changes to the land cover and carbon emissions, which may in turn have an impact on regional climate. However, complex climate-soil-vegetation-interacting processes leave numerous uncertainties in wildfire projections and their impacts under the acceleration of global warming. In this study, Forest Fire Danger Index (FFDI) and Fire Weather Index are calculated based on the Coupled Model Intercomparison Project (CMIP) multi-model projections in order to investigate future changes in fire weather danger under different emission pathways. The regional hotspots which are expected to face a heightened risk of fire weather are then focused on to further comprehend the wildfire and land cover feedback. (Acknowledgements: This study was supported by the Special Research Support Scheme from the Hong Kong University of Science and Technology (R9055), which was funded with the donation from the Chau Hoi Shuen Foundation.)

In this study, the fast response of East Asian summer precipitation to COVID-induced aerosol emission reductions is examined using the Community Earth System Model version 2.2 (CESM2.2). The emission reductions decreased aerosol optical depth and cloud cover over northern China in June 2020. The troposphere became warmer, strengthening the land-sea thermal contrast and anomalous southerly winds. The subtropical westerly jet accelerated and shifted southwards, favoring the low-level convergence, upward air motions, and subsequent condensational heating over the Yangtze River Basin (YRB). The feedback of condensational heating in return strengthened the convergence and ascent. The western North Pacific Subtropical High was intensified, which further enhanced the moisture advection and convergence over the YRB. Both the enhanced moisture convergence and ascent increased precipitation over the YRB during June 2020. Furthermore, local and remote emission reductions show different impacts on convection and moisture transport over the YRB. The emission reductions over China caused stronger convective precipitation (1.15 mm/day versus 0.63 mm/day), but weaker larger-scale precipitation (1.17 mm/day versus 2.24 mm/day) than the emission reductions outside China did.

The presence of biological (airborne pollen) and non-biological (PM_2.5, PM₁₀, O₃, CO, NO₂, SO₂, etc.) particles in the atmosphere is associated with the incidence of adverse allergenic reactions affecting human health. Considering all the probable effects of air pollutants and airborne pollen on allergic reactions, the present study mainly examines the relationship between the daily concentrations of airborne pollen, PM_2.5, PM₁₀, O₃, CO, NO₂, SO₂, and the number of patients with allergic rhinitis (AR) at the same time in northern Beijing, China by statistical analysis. The results showed that the daily visiting number of AR patients was positively associated with the concentration of airborne pollen, as well as the pollutants NO₂ and PM₁₀ particles, while, it was negatively associated with the concentrations of O₃ and CO. Airborne pollen and part air particles (e.g., NO₂, PM₁₀) had a significant impact on the morbidity of AR patients. Thus, public health and clinical approaches are needed to anticipate and reduce the morbidity of allergic disease caused by airborne pollen and non-biological particles.

Deep convective clouds and associated anvils exert opposite radiative effects. The impact of different aerosol types on these two categories of clouds remains a major challenge in understanding aerosol-cloud interactions. Using 11-year satellite retrievals, we find that cloud top height (CTH) and ice cloud fraction of deep convective clouds and anvil cirrus identified by Cloud-Aerosol Lidar with Orthogonal Polarization increase with small aerosol loadings and level off or even decrease with further aerosol increase. Compared with continental aerosols, CTH affected by marine aerosols starts to decrease at smaller aerosol loadings. Moreover, cloud optical depth (COD) of deep convective clouds decreases with aerosol loadings. COD of anvil cirrus increases with increased loadings of most aerosol types but decreases with smoke aerosol. These relationships are mainly attributed to the aerosol effect rather than the meteorological effects. Our findings contribute to the development of models and better assessment of aerosol-cloud radiative forcing.

Interactions between aerosols and gases in the atmosphere have been the focus of an increasing number of studies in recent years. Here, we focus on aerosol effects on tropospheric ozone that involve meteorological feedbacks induced by aerosol–radiation interactions. Specifically, we study the effects that involve aerosol influences on the transport of gaseous pollutants and on atmospheric moisture, both of which can impact ozone chemistry. For this purpose, we use the UK Earth System Model (UKESM1), with which we performed sensitivity simulations including and excluding the aerosol direct radiative effect (ADE) on atmospheric chemistry, and focused our analysis on an area with a high aerosol presence, namely China. By comparing the simulations, we found that ADE reduced shortwave radiation by 11 % in China and consequently led to lower turbulent kinetic energy, weaker horizontal winds and a shallower boundary layer (with a maximum of 102.28 m reduction in north China). On the one hand, the suppressed boundary layer limited the export and diffusion of pollutants and increased the concentration of CO, SO₂, NO, NO₂, PM_2.5 and PM₁₀ in the aerosol-rich regions. The NO/NO₂ ratio generally increased and led to more ozone depletion. On the other hand, the boundary layer top acted as a barrier that trapped moisture at lower altitudes and reduced the moisture at higher altitudes (the specific humidity was reduced by 1.69 % at 1493 m on average in China). Due to reduced water vapour, fewer clouds were formed and more sunlight reached the surface, so the photochemical production of ozone increased. Under the combined effect of the two meteorology feedback methods, the annual average ozone concentration in China declined by 2.01 ppb (6.2 %), which was found to bring the model into closer agreement with surface ozone measurements from different parts of China.

Central Asia is prone to wildfires, but the relationship between wildfires and climatic factors in this area is still not clear. In this study, the spatiotemporal variation in wildfire activities across Central Asia during 1997–2016 in terms of the burned area (BA) was investigated with Global Fire Emission Database version 4s (GFED4s). The relationship between BA and climatic factors in the region was also analyzed. The results reveal that more than 90% of the BA across Central Asia is located in Kazakhstan. The peak BA occurs from June to September, and remarkable interannual variation in wildfire activities occurs in western central Kazakhstan (WCKZ). At the interannual scale, the BA is negatively correlated with precipitation (correlation coefficient r = –0.66), soil moisture (r = –0.68), and relative humidity (r = –0.65), while it is positively correlated with the frequency of hot days (r = 0.37) during the burning season (from June to September). Composite analysis suggests that the years in which the BA is higher are generally associated with positive geopotential height anomalies at 500 hPa over the WCKZ region, which lead to the strengthening of the downdraft at 500 hPa and the weakening of westerlies at 850 hPa over the region. The weakened westerlies suppress the transport of water vapor from the Atlantic Ocean to the WCKZ region, resulting in decreased precipitation, soil moisture, and relative humidity in the lower atmosphere over the WCKZ region; these conditions promote an increase in BA throughout the region. Moreover, the westerly circulation index is positively correlated (r = 0.53) with precipitation anomalies and negatively correlated (r = –0.37) with BA anomalies in the WCKZ region during the burning season, which further underscores that wildfires associated with atmospheric circulation systems are becoming an increasingly important component of the relationship between climate and wildfire.

This study examines the seasonal and diurnal variations of carbon dioxide and energy fluxes over three land cover types of Nepal by using the eddy covariance method from March to November 2016. The surface energy balance closures were moderate with the values of about 56%, 61%, and 64% closure at Kirtipur, Simara, and Tarahara sites respectively. The monthly average values of net radiation flux and latent heat flux peaked in August at Kirtipur and Tarahara sites whereas in June at the Simara site respectively. The maximum monthly average measured sensible heat flux was 37 W m⁻², 43.6 W m⁻², and 36.3 W m⁻² in April for all the sites whereas soil heat flux was 5.1 W m⁻² and 2.9 W m⁻² in April for Kirtipur and Simara sites and 6.2 W m⁻² in June for the Tarahara site. The magnitude of diurnal peak of net ecosystem CO2 exchange (NEE) reached up to 11.04 μmol m⁻² s⁻¹ at Kirtipur, 15.04 μmol m⁻² s⁻¹ at Simara, and 10.44 μmol m⁻² s⁻¹ at Tarahara sites respectively. Among the three study sites, the ecosystem at the Kirtipur site was a good carbon source; the ecosystems at Simara and Tarahara sites were low and good carbon sink in the growing season. In addition, all three different land cover ecosystem were carbon source when accounted for the measurement period.

In this study, we performed a synoptic analysis to investigate the reason for the spatial difference in PM_2.5 concentration despite a similar synoptic pattern. Synoptic pressure patterns associated with high PM_2.5 episodes (greater than 35 ㎍/㎥) were classified into three sub-groups related to high concentrations occurring only in Busan and Seoul metropolitan areas using K-means cluster analysis, based on the 900 hPa geopotential height of NCEP FNL data. Although the synoptic patterns of high PM_2.5 episodes that occur independently in Busan and Seoul metropolitan areas were similar, there was a difference in the intensity of pressure gradient and its direction, which tends to be an important factor determining the movement time of pollutants. The spatial difference in PM_2.5 concentration in the Korean Peninsula is due to the difference and direction of the atmospheric pressure gradient that develops from southwest to northeast direction.

With fast economic development, urban expansion and haze pollution are two main eco-environmental challenges confronting China. Though their individual impacts on urban climate and environment are investigated by multiple studies, the synthetic effect and intricate interaction of these two effects have not been fully understood. Here, based on in-situ measurements and numerical simulations at the densely populated and heavily polluted Beijing-Tianjin-Hebei (BTH) city cluster, the respective impact of aerosol and urbanization is quantitatively identified. Under polluted conditions, the aerosol radiative effect dominates with 3 ^oC cooling at ground surface and 1 ^oC warming in the upper boundary layer. In contrast, urban heat island (UHI) effect is more prominent during clean days, leading to over 1 ^oC warming in the lower atmosphere. Furthermore, aerosols are found to enhance UHI via confining the dispersion of anthropogenic heat and perturbing longwave radiation budget. Since the implement of China’s Clean Air Action in 2013, aerosol reduction contributes to a decreasing trend of both aerosol radiative effect and UHI intensity in BTH city cluster. The study highlights a comprehensive understanding of air pollution and urbanization as well as their interactions over megacities.

The Global Environmental Measurement and Monitoring (GEMM) Initiative is an international project of Optica and the American Geophysical Union seeking to provide precise and usable environmental data for local impact. The Initiative brings together science, technology, and policy stakeholders to address critical environmental challenges and provide solutions to inform policy decisions on greenhouse gases (GHGs) and air and water quality. GEMM Centers are currently established in Scotland, Canada, New Zealand, and the United States. These Centers represent partnerships with leading institutions that are actively working toward developing or deploying new measurement technology and improved climate models. Additional Centers are under development in India and Australia with plans to expand to Asia and Africa. In addition to establishing monitoring centers worldwide, GEMM actively engages with other sectors (including industry, standards organizations, and regional or national governments) to support the incorporation or adoption of these evidence-based approaches into decision making processes. For example, Glasgow, Scotland is piloting the GEMM Urban Air Project, deploying a low-cost, real-time, ground-based network of devices that continuously monitors GHGs and air pollutants at a neighborhood scale. The sensor network in Glasgow is increasing the precision of local models that can provide the city with information to assess current policies and support future action. Here we will share the progress and outputs of the GEMM Initiative to date and highlight paths forward to grow the network.

Antarctic sea ice plays an important role in polar ecosystems and global climate, while its variability is affected by many factors. Teleconnections between the tropical and high latitudes have profound impacts on Antarctic climate changes through the stationary Rossby wave mechanism. Recent studies have connected long-term Antarctic sea ice changes to multidecadal variabilities of the tropical ocean, including the Atlantic Multidecadal Oscillation and the Interdecadal Pacific Oscillation. On interannual timescales, whether an impact exists from teleconnection of the tropical Atlantic is not clear. Here we find an impact of sea surface temperature (SST) variability of the tropical Atlantic meridional dipole mode on Antarctic sea ice that is most prominent in austral autumn. The meridional dipole SST anomalies in the tropical Atlantic force deep convection anomalies locally and over the tropical Pacific, generating stationary Rossby wave trains propagating eastward and poleward, which induce atmospheric circulation anomalies affecting sea ice. Specifically, convective anomalies over the equatorial Atlantic and Pacific are opposite-signed, accompanied by anomalous wave sources over the subtropical Southern Hemisphere. The planetary-scale atmospheric response has significant impacts on sea ice concentration anomalies in the Ross Sea, near the Antarctic Peninsula, and east of the Weddell Sea.

The El Niño-Southern Oscillation (ENSO) influences West Antarctica via atmospheric teleconnection, but it is delayed by one season because the Amundsen Sea Low (ASL) response to the ENSO has the strongest intensity in May. However, the mechanism related to the delay has not been fully discovered yet. This study investigates the formation mechanism of the ENSO-driven teleconnection in each month by examining the kinetic energy conversion (CK), total wavenumber, and wave activity flux. The perturbation induced by the ENSO gains energy from the basic state as the ENSO appears, however it does reach high latitudes only in May. Although the ENSO becomes weak in May, the developed subtropical jet makes waves to move further south, resulting in the strong ASL anomaly. Numerical experiments considering the decay of the ENSO forcing also show that the ENSO-related teleconnection is the strongest in May. The results demonstrate that the basic state is essential for the ENSO teleconnection to extend to West Antarctica.

In November 2020, Taiwan implemented a 5-day intensive observation experiment on the Lanyang Plain in the northeast, which we call the Yilan Experiment for Severe Rainfall (YESRain). The reason for this project is that according to the statistics of the Taiwan Central Meteorological Bureau over the years, when the northeast monsoon occurs, the Lanyang Plain often receives the most precipitation in Taiwan, and this precipitation is even higher than the Keelung area on the windward side.In order to analyze the reasons for the rainfall in the Lanyang Plain under the strong northeast monsoon, this study analyzed and simulated the moveable rain belt, rainfall time and rainfall intensity caused by the strong northeast monsoon on November 23, 2020. In addition, various observation tools such as laser wind profiler are used to analyze the relationship between the wind field and the rain belt after the interaction between the bell mouth terrain in Yilan area and the wind field at different heights, and to verify each other with the WRF numerical simulation results. It was found that the northeastern part of Taiwan was affected by the strong northeast monsoon on the 23rd, and the severe rainfall in the Yilan area not only appeared on the south windward side, but also occurred in the Lanyang Plain and the outer sea. The precipitation distribution is highly related to the direction and speed of the wind field, while The surface flux has a considerable contribution to the severe rainfall in Yilan.

There are typical spatial rainfall patterns in the northeast Taiwan area (Yi-Lan) under the northeasterly in wintertime. Such rainfall features are related to the interaction of large-scale background mean flow and complex topography. According to the climatology of rainfall, most precipitation hot spots occur on the windward side of the terrain and decrease toward the plain area. However, the difference in large-scale environmental conditions can lead the variations in spatial rainfall characteristics. By analyzing the gridded rainfall data in the Yilan area in the past 60 years, we noticed that the average rainfall has an increasing trend. The increasing trend was more significant in the southern mountain region on the windward side. The rainfall increase in winter was more extensive than in autumn and spread to the plains in coastal areas. We have also noticed that there will still be heavy rain (HR, >80mm/day) events in the Yilan area in autumn and winter when Taiwan has less rainfall. The probability of HR events has increased significantly in the past 30 years, and HR events also occurred in plain areas. On the other hand, although the probability of occurrence was low, there still were torrential rain (TR, >200mm/day) events in the mountainous areas of Yilan. The frequency and extent of occurrence of these types of extreme rainfall events have significantly increased during 2016-2020 compared with 1961-2015. According to the reanalysis data, the background large-scale circulation field has also changed in the past 60 years. The change in the background circulation led to changes in the frequency of occurrence and hot-spot locations of convection initiation, which also caused variations in the spatial distribution of rainfall.

Mountain-waves, also known as Lee wave, can generate strong rotational forces or up and down air currents that can affect aircraft operations. If mountain-wave clouds exist during the calculation of atmospheric motion vectors using satellite data, the vector may become inaccurate. Therefore, it is important to analyze the atmospheric flow characteristics and generation mechanisms of mountain-wave formation. In this study, we present a satellite-based characteristics of mountain-wave events in the Taebaek Mountain range in Gangwon-do, Korea. Our analysis was based on detailed visual inspection of infrared satellite imagery in the ch13 (10.5㎛) of the GK-2A satellite during 2020-2022. Two analysts independently inspecting the same satellite images using visual inspection methods, and cross-verifying the results. If there are enough moisture in the incoming airstream, mountain-wave clouds are generated parallel to the mountain range from the ridge of mountain-wave that have the characteristics of normal waves. Therefore, we detected clouds whose positions did not change over time and observed the duration of their occurrence. Previous research used visible satellite imagery in 15-min intervals during daylight hours. This had limitations as it could not identify mountain-wave cloud that occurred or lasted at nighttime hours and the 15-min interval was too long to detect the exact duration. But, using the infrared imagery from the GK-2A satellite with 2-min intervals, we were able to overcome these limitations and achieve more accurate observations. The maximum number of mountain-wave events occurred in winter season, with the largest number in February. Conversely, the minimum number of mountain-wave events occurred in summer season, with the fewest number in July. The average duration of mountain-wave events was longest in October and shortest in June. The maximum duration is 2268-min, while the minimum is 48-min.

As the latitude of the continental cold high-pressure center was higher and gradually moves eastward to the sea, the environmental wind field in northern Taiwan was mostly northeasterly or easterly winds introduced by continental high-pressure peripheral circulation. Though the distance between the Taipei Basin and the Yilan of northern Taiwan were closed, the local wind fields were quite different. In order to understand the relationship between the environmental wind field (northeast monsoon), local circulation, and complex terrain effects, this study applied the Weather Research and Forecasting Model, simulating the change of local circulation in the Taipei Basin and Yilan under the northeast monsoon on October 25 and November 26, 2022.It was found that when the wind field in the Taipei Basin is weak northeasterly, local circulation will be generated after noon due to the influence of the terrain, and the wind field will gradually change to easterly or southeasterly easterly. With such airflow characteristics, and the weather tended to be stable. When the wind field of the same environment passed through the Lanyang Plain, it was affected by the terrain effect, and the area where the wind field convergence zone was formed in the Lanyang Plain has Nimbostratus and indirect precipitation. In addition, a northwest-southeast trending wind direction convergence zone was formed in the vicinity of Sansing. As the terrain blocking and local circulation became more obvious, the wind direction gradually turned to westerly and went out to ocean. When the northeast monsoon was strong, the large-scale ambient wind field would directly change the boundary layer structure of the Taipei Basin and the Lanyang Plain. However, under the same northeast monsoon environment, there would still be obvious differences in the boundary layer wind field characteristics between the Taipei Basin and the Lanyang Plain.

The rain gauges over Yilan observed the heavy rainfall during 15 Oct 2022 to 17 Oct 2022 with maximum accumulated rainfall is 972.5 mm within 40 hours. During 14 Oct 2022 to 18 Oct 2022, typhoon Nesat carrying abundant moisture moved westward, passing through the sea south of Taiwan. After Nesat moved into South China Sea, the northeasterly wind enhanced. In this case, the maximum moisture flux exceeded 300 g kg^-1 m s^-1 near 950-hPa level. Typhoon Nesat provided abundant moisture when it was on southeast and south of Taiwan. The northeasterly wind played an important role in moisture transport after Nesat passed by Taiwan. The wind profile radar at Dongshan observed that during 1600 UTC 15 Oct to 0800 UTC 16 Oct, the low-level wind over Yilan was consistent easterly wind with the maximum wind speed ~20 m s^-1. After 2000 UTC 16 Oct, the northeasterly wind advanced into Yilan, and the thickness of northeasterly wind was ~3 km. The maximum wind speed of northeasterly wind was near 1 km height, and the wind speed exceeded 20 m s^-1. After 1600 UTC 17 Oct, the northeasterly wind weakened, and the westerly winds were present in the planetary boundary layer. In this case, the characteristics of drop size distribution over southern Yilan plain showed two different types. One was affect by typhoon circulation mainly (during 1200 UTC 15 Oct to 1200 UTC 16 Oct), the rain rate was stronger with larger diameter (D_m) and smaller number concentration (N_w). The other type was under northeasterly wind, and the rain rate was smaller with smaller D_m and larger N_w.

The heavy rainfall caused by Mesoscale Convective Systems (MCS) has been increasing recently. As a result, there is a growing emphasis on studying MCS. The formation and development of MCS are associated with the thermodynamic and dynamical environment, which has been mostly investigated using a numerical model. There have been limited attempts to utilize in situ rawinsonde measurements, which provide an accurate and high-resolution thermodynamic and dynamic profile, to characterize the MCS over South Korea. In this study, the sounding-derived parameters of the MCS occurring on the Korean Peninsula were presented using the data obtained in the summer of four years (2018–2021).
We classified the MCS cases into four types: convective cells (CC), mesoscale convective complex (MCC), diagonal squall line (SLD), and parallel squall line (SLD). The results show that CC was generated by thermal instability and had the largest convective potential available energy at 470.8 m² s^-2. The total Richardson number had 27.0, exhibiting a distinctly large value compared to other types, but mechanical instability was the smallest in the MCS types. MCC had a considerable convective inhibitory energy of 174.5 m² s^-2 and a large low-layer shear of 7.8 m s^-1. SLD and SLP had a large vertical wind shear, with mechanical instability contributing to formation and development. Sounding-derived parameters during the formation, development, and extinction cycles of MCS suggested that the thermodynamic values gradually increased until they reached their highest values at the peak of convective system development, followed by decreasing trend during the dissipating stage. This study will present the favorable environmental condition for each MCS type to identify the key parameters in determining the MCS type and the development stage.

ACKNOWLEDGEMENT
This work was supported by the National Research Foundation of Korea grant funded by the Korea government (MSIT) (No. 2021R1A4A1032646).

Improving the predictability of precipitation requires a comprehensive understanding of precipitation growth and decay (GD). Investigating the precipitation GD has mostly relied on the Eulerian approach, which examines the physical characteristics in a fixed coordinate. On the other hand, Lagrangian approach follows the precipitation system motion and enables capturing its temporal evolution. This study quantitatively retrieves radar-based GD of precipitation in South Korea in a framework of semi-Lagrangian advection. This study is followed by the investigation of the dependence of GD on the following factors: flow direction, flow speed, and relative geographical location to the topography and land–ocean boundary. The results indicate that the flow direction determines the spatial distribution of GD. The growth generally tends to occur on the windward side of the mountain range and vice versa on the lee side. The flow speed affects the intensity of GD. The study further explores the diurnal variability of the GD. Under the strong orographic forcing on the diurnal variation, unique geographical features of South Korea created interesting diurnal patterns near Seoul by land–ocean breeze circulation and on the windward side of the mountain ranges (inland) by solar heating. The GD in South Korea shows two diurnal peaks of growth: the early morning peak over the ocean and the afternoon peak in the land areas. In addition, the GD in South Korea exhibits a monthly variation with the most intense growth occurring in August.

ACKNOWLEDGMENT
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A4A1032646).