Atmospheric Research最新文献

{"title":"The effects of aerosol on the growth of hydrometeors in deep convective clouds","authors":"Xinyi Lin ,&nbsp;Qian Chen ,&nbsp;Zeyong Zou ,&nbsp;Ying He ,&nbsp;Chunsong Lu ,&nbsp;Zhiliang Shu","doi":"10.1016/j.atmosres.2025.108088","DOIUrl":"10.1016/j.atmosres.2025.108088","url":null,"abstract":"<div><div>The impact of aerosol on the development of deep convective clouds and the growth of hydrometeors was investigated using the Weather Research and Forecasting model with a detailed spectral bin microphysics scheme. The simulated cloud top temperature, the vertical profile of moving speed of hydrometeors, and the spatial distributions of precipitation rate were compared with observations from satellite, cloud radar, and weather stations, respectively. The results show that the smaller cloud droplets in the polluted condition have greater mobility with ambient air, which can reach up to 10 km of altitude comparing with 7 km in clean condition, thereby increase the collecting efficiency between ice crystals and supercooled liquid droplets therein. Moreover, ice crystals move slowly around 8 km, thereby facilitating the riming of ice particles by supercooled water to form hailstones. The efficient upward transport of cloud droplets in the convective core area further amplifies this process. Increased aerosol concentration enhances the hail production rate by 2 to 3 orders of magnitude, and results in a 3.48 % increase in effective terminal velocity of hailstone from surface to 4.5 km. The aerosol-induced hail growth effect is stronger over convective cores than that over non-core area. The intensified sedimentation of hail and its accompanying melting in strong downdraft regions contribute to the increased surface precipitation at late stage of convection in polluted condition.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108088"},"PeriodicalIF":4.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing frequency of warm-dry climate-year type in Northeast China: A major contributor to maize yield shocks","authors":"Sichao Yan ,&nbsp;Huopo Chen ,&nbsp;Shengping He","doi":"10.1016/j.atmosres.2025.108084","DOIUrl":"10.1016/j.atmosres.2025.108084","url":null,"abstract":"<div><div>Under the context of climate change, the climatic conditions for maize during different growth stages in Northeast China (NEC) exert a markedly varying influence on the final yield. We define nine Climate-Year Types (CYTs) based on the probability density functions of temperature and precipitation, aiming to explore the underlying drivers of climatic factors contributing to yield shocks. By incorporating a panel regression model, we quantitatively analyze the key compound climate conditions that influence maize yield across different growth stages. The results indicate that the main CYTs are biased towards drier conditions (Cold-Dry, Rainless, Warm-Dry) from 1992 to 2021, while the probability of warmer conditions (Normal-Warm, Warm-Wet, Warm-Dry) is expected to increase throughout the whole growth stage in the future. The overall yield shock is approximately 20 % in the historical period, with projected exacerbation under the future warming scenarios. At present, the dominant CYTs that determines the final yield of maize is changing from colder and wetter conditions in the growing stage to drier and warmer conditions in the reproductive stage. Moreover, the Warm-Dry CYT emerges as the most significant climatic factor influencing maize yield, with each occurrence associated with a 3.70 % decrease in yield throughout the whole growth stage in the historical period. Notably, the frequency of Warm-Dry CYT is expected to increase in most cities of NEC, which remains a major contributor to yield shocks, with the magnitude of its impact likely to intensify in the future. In summary, these studies identify the key climate types affecting maize yield at different growth stages in NEC, emphasizing the impact of compound heat and drought. This will provide a scientific basis for targeted measures to enhance yield and mitigate risks.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108084"},"PeriodicalIF":4.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How well do the reanalysis datasets capture hot and cold extremes and their trends in India?","authors":"Suman Bhattacharyya ,&nbsp;Marwan A. Hassan ,&nbsp;S. Sreekesh ,&nbsp;Vandana Choudhary","doi":"10.1016/j.atmosres.2025.108073","DOIUrl":"10.1016/j.atmosres.2025.108073","url":null,"abstract":"<div><div>Much of the Earth's surface lacks long-term in-situ measurement of essential meteorological variables. Climate reanalysis datasets provide an alternative in data-sparse regions, sometimes replacing gauge-based observations for climatological studies, however, they have inherent biases. Reanalysis is now available at finer spatial and temporal resolutions, that can be considered for hydrological and climatological studies. Although the assessment of reanalysis datasets is common at a daily, monthly, or seasonal scale, how the recent generation reanalysis captures the spatial pattern of extreme temperature events, and their trends remains an open question.</div><div>In this study, two regional (IMDAA and EARS) and five global (ERA5-Land, ERA5, MERRA2, CFSR, and JRA3Q) reanalysis datasets are evaluated with a gauge-based gridded temperature dataset from the India Meteorological Department (IMD) to assess their suitability for studying extreme temperature events and their trends over India. Fifteen hot and cold extremes indices are identified to characterize extremes covering frequency, intensity, and duration of extreme temperature events.</div><div>The study finds that no single reanalysis outperforms others for all the extreme indices when compared to the IMD gridded data, however, a select few (e.g., ERA5, ERA5L, MERRA2, and JRA3Q) better perform in reproducing the observed spatial pattern of extreme events and their changes across different regions of India. It is also noted that in response to global warming, the frequency, duration, and magnitude of extreme hot events are rising, and cold events are decreasing in India which is also captured by most of these reanalyses. Overall, the increase in hot extremes is more prominent in the south of the tropics and the decline in cold extremes is more evident in the north. However, the trend areas and magnitudes of the reanalysis datasets were not similar in comparison to trends from a regional station-based gridded dataset. Thus, care should be taken when selecting datasets for such applications and interpreting their trends.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108073"},"PeriodicalIF":4.5,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction of path integrated attenuation using data from two X-band ground-based radars and GPM","authors":"Eleni Loulli ,&nbsp;Silas Michaelides ,&nbsp;Johannes Bühl ,&nbsp;Athanasios Loukas ,&nbsp;Diofantos G. Hadjimitsis","doi":"10.1016/j.atmosres.2025.108080","DOIUrl":"10.1016/j.atmosres.2025.108080","url":null,"abstract":"<div><div>This study attempts to correct the attenuation of the radar reflectivity factor from two X-band radars in Cyprus, using a constrained attenuation correction method. This research was implemented under the assumption that the difference between the reflectivity measured by each of the ground radars, on the one hand, and the reflectivity measured by the Global Precipitation Measurement (GPM) mission, on the other hand, equals the path integrated attenuation (PIA). This is because solely the reflectivity data at horizontal polarization have been used so far. Various parameter combinations of linear coefficients for the constrained correction were tested empirically, using GPM DPR data as a reference. The results showed that no single parameter combination can be consistently effective, and highlighted the need for event- and radar-specific attenuation correction.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108080"},"PeriodicalIF":4.5,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal variations of global precipitation concentration and potential links to flood-drought events in past 70 years","authors":"Jianing Sun ,&nbsp;Huilan Zhang ,&nbsp;Tiezheng Wang ,&nbsp;Yuan Xu ,&nbsp;Can Huang ,&nbsp;Shuyi Dan","doi":"10.1016/j.atmosres.2025.108086","DOIUrl":"10.1016/j.atmosres.2025.108086","url":null,"abstract":"<div><div>The evaluation of precipitation events is crucial for predicting severe droughts and floods, particularly in the context of global warming. This study presents a comprehensive spatiotemporal analysis of the global precipitation concentration index (CI) from 1950 to 2020, comparing CI with nine extreme precipitation indices to assess its applicability. The world map was divided into three distinct regions related to flood-drought events using the standardized precipitation index (SPI). Key findings include: 1) CI exhibited significant spatial heterogeneity, with elevated values in seasonally warm coastlines and arid regions. Seasonal changes in CI were not synchronized, particularly lower in Northern Hemisphere winters, while the Southern Hemisphere displayed minor seasonal variation, alongside a long-term growth trend in approximately 36.5 % of the global area. 2) CI's spatiotemporal variations helped delineating regional precipitation patterns, revealing a strong correlation (R² = 0.87) with the extreme precipitation index R95c, but a negligible relationship with total precipitation (P) (R² = -0.05). This facilitated the global partitioning into three regions: Humidity Amplification Region (I), Dryness Intensification Region (II), and Climate Fluctuation Region (III). 3) Flood-drought events were potentially linked to variations in CI and P, where In Region I experienced increased flood risk due to rising CI and P, Region II faced heightened drought risk from rising CI and declining P, and Region III showed opposing effects on floods due to reduced CI and increased P, with P variability significantly influencing flood frequency. 4) The main atmospheric circulation factors varied by region, typically including the Antarctic Oscillation (AAO), Atlantic Multi-decadal Oscillation (AMO) and Arctic Oscillation (AO), with AAO being most significant. This study highlights the potential of CI in improving the prediction of drought and flood risks, emphasizing the need for enhanced regional data and dynamic modeling to better assess the impacts of climate change on precipitation patterns and hydrological extremes.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108086"},"PeriodicalIF":4.5,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina","authors":"Lorenzo Ricetti ,&nbsp;Santiago I. Hurtado ,&nbsp;Eduardo Agosta Scarel","doi":"10.1016/j.atmosres.2025.108082","DOIUrl":"10.1016/j.atmosres.2025.108082","url":null,"abstract":"<div><div>This study evaluates the spatio-temporal coherence of regional extreme precipitation indices in subtropical Argentina (STAr) derived from rain gauge station data from 1991 to 2021. For the regionalization two machine learning clustering algorithms are used—Ward's method and K-means—and a novel stepwise regionalization approach, HAZ. While machine learning algorithms require the apriori definition of the optimal number of clusters, which varies considerably with the used metric and selection criteria, the HAZ method relies on a Pearson's correlation coefficient threshold and avoids this limitation. In most cases machine learning algorithms struggled to produce coherent regions, with fewer clusters prioritizing spatial coherence at the expense of temporal consistency, and vice versa. Conversely, the HAZ method systematically outperformed machine learning approaches, providing regions with adequate spatio-temporal coherence. Notably, HAZ permits some stations to remain unclustered, allowing to reflect the local variability in extreme precipitation. The overall good performance of the HAZ method demonstrates its potential for broader applications in hydro-climatic studies. Moreover, two intensity indices were unsuitable for regionalization due to poor coherence, while the other three were prone to regionalization throughout the year. The Accumulated index, particularly using the 95th percentile as a threshold, emerged as the most representative, effectively synthesizing extreme precipitation characteristics in STAr. Finally, the necessity of validating the spatio-temporal internal coherence of clustering algorithms outputs is emphasized to avoid mischaracterization and ensure robust regionalization results.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"320 ","pages":"Article 108082"},"PeriodicalIF":4.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and characteristics of regional rainstorm events in China based on a dual-threshold method","authors":"Siyuan Dai,&nbsp;Qi Zhang,&nbsp;Shaofeng Huang","doi":"10.1016/j.atmosres.2025.108081","DOIUrl":"10.1016/j.atmosres.2025.108081","url":null,"abstract":"<div><div>Regional extreme precipitation can cause severe flooding with major environmental and social impacts. While most existing studies either ignored their spatiotemporally contiguous or abnormally break down the events with multiple rainstorm cores, rapid movement, or transient interruptions into multiple isolated fragments. Here, we propose a novel approach for identifying regional rainstorm events (RREs), which integrates dual-threshold with a three-dimensional (longitude, latitude, and time) image connectivity perspective. There were 1163 RREs identified across mainland China from 1991 to 2022 based on the CN05.1 dataset. The spatial, temporal, and movement patterns of RREs were analyzed. Results showed that 71.2 % of RREs lasted only one day, with intensity mostly around 60–70 mm/day. The cumulative magnitude of RREs was most positively correlated with its affected area (<em>r</em> = 0.95). The occurrence frequency of RREs had a significant upward trend during 1991–2022, approximately increased 2.7 events per decade, while the severity slightly declined. Spatially, the highest frequencies of RREs were observed in southeastern China. Based on the movement direction and distance, RREs were classified into four patterns: Eastward Moving, Southwestward Moving, Localized, and Northward Moving. Northward Moving RREs exhibited the largest affected area, the greatest cumulative magnitude, and the longest movement distance, followed by Eastward Moving RREs. Localized RREs had the shortest duration, the smallest affected area and movement distance, but the highest intensity (averaging 92.6 mm/day). Our findings provide new insights for understanding the characteristics of regional extreme precipitation and their driving mechanisms, which are valuable for forecasting and adapting to the resultant disasters.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"320 ","pages":"Article 108081"},"PeriodicalIF":4.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the CMIP6 models for simulating the trend of the Barents-Kara Sea compound heatwaves in boreal autumn","authors":"Yue Xin ,&nbsp;Wenting Hu ,&nbsp;Anmin Duan ,&nbsp;Bin Tang ,&nbsp;Yuheng Tang","doi":"10.1016/j.atmosres.2025.108077","DOIUrl":"10.1016/j.atmosres.2025.108077","url":null,"abstract":"<div><div>Compound heatwaves (CHWs) pose more severe environmental and socio-economic risks than individual heatwave events, with their frequency increasing rapidly worldwide. In the Arctic, particularly the Barents–Kara Sea (BKS), CHWs are intensifying, accelerating ice melt, permafrost thaw, and ecosystem disruptions. Accurate climate projections require a robust assessment of climate models' ability to simulate these extreme events. This study evaluates the performance of 24 CMIP6 models in reproducing autumn CHW trends in the BKS, revealing substantial intermodel discrepancies in trend magnitudes and spatial patterns. The observed BKS regional CHW trend is 0.55 events per decade. Group A models (e.g., ACCESS-CM2, CanESM5, GFDL-CM4) exhibit smaller errors, with a mean absolute bias of −0.03 events per decade, while Group B models (e.g., FGOALS-g3, GFDL-ESM4, MIROC6) show larger discrepancies, with a mean absolute bias of −0.40 events per decade. Further analysis reveals that Group A models better simulate the enhanced moisture transport from the Atlantic to the BKS, a key driver of CHWs, with smaller deviations in latent heat flux and net longwave radiation. The results highlight the significant role of water vapor in the occurrence of CHWs and suggest that improving the representation of these processes could enhance the accuracy of future Arctic CHW projections. This study underscores the importance of model evaluation for improving climate predictions and understanding the dynamics of extreme heat events in high-latitude regions.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"320 ","pages":"Article 108077"},"PeriodicalIF":4.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Land-atmosphere coupling characteristics in summer based on microwave radiometer data at Nagqu site of Tibetan Plateau","authors":"Guantian Wang ,&nbsp;Zeyong Hu ,&nbsp;Haipeng Yu ,&nbsp;Genhou Sun ,&nbsp;Ruijia Niu ,&nbsp;Xin Wang","doi":"10.1016/j.atmosres.2025.108074","DOIUrl":"10.1016/j.atmosres.2025.108074","url":null,"abstract":"<div><div>The energy transfer within the planetary boundary layer (PBL) is a crucial variable influencing weather processes both on the Tibetan Plateau (TP) and in its downstream areas. This paper assesses the accuracy of ground-based microwave radiometer (MWR) in comparison with radiosonde data in different weather conditions. The effects of surface heat flux and precipitation on the atmosphere in different weather conditions are quantified to facilitate the study of land- atmosphere coupling in the Nagqu region. The findings indicate that microwave radiometer can provide accurate measurements of air temperature within 4 km height and specific humidity below cloud cover. The variation in surface sensible heat aligns with changes in the convective boundary layer (CBL) height, whereas latent heat shows no significant correlation during the diurnal cycle. Furthermore, daily precipitation process influences the lower atmosphere in the Nagqu region, with the impact scaling with precipitation intensity. The MWR observations indicate that precipitation generated by the Tibetan Plateau vortex (TPV) has the most significant impact on the land-atmosphere energy exchange.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"320 ","pages":"Article 108074"},"PeriodicalIF":4.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightning nowcasting based on high-density area and extrapolation utilizing long-range lightning location data","authors":"Yi Liu ,&nbsp;Jialei Wang ,&nbsp;Yang Song ,&nbsp;Shi Liang ,&nbsp;Min Xia ,&nbsp;Qilin Zhang","doi":"10.1016/j.atmosres.2025.108070","DOIUrl":"10.1016/j.atmosres.2025.108070","url":null,"abstract":"<div><div>Lightning poses varying degrees of threat to human production, life and personal safety because of its huge destructive power. Lightning prediction with intensity estimation is an important reference for disaster warning, determining lightning protection level and real-time and dynamic lightning risk assessment. Real-time observation of total lightning can compensate for the problem of limited range or shielding of radar observation and is of great significance for thunderstorm tracking and lightning nowcasting. In this paper, the VLF Lightning Location Network (VLF-LLN) is used to obtain real-time long-range lightning location data. Through density estimation based on the Gaussian mixture model and weighted according to time proximity, the spatio-temporal range and frequency information of lightning high-density area are obtained. Then, a spatio-temporal data extrapolation model ME-RNN is proposed to predict the intensity of lightning activity and its evolution pattern in the thunderstorm region, so as to realize the nowcasting of lightning activity. Based on the PredRNN architecture, the model integrates the MotionGRU operator optimized for the characteristics of lightning data, and integrates the generative adversative network (GAN) to capture the transient changes of lightning phenomena and predict its movement trend. At 6 min, 18 min and 36 min before the thunderstorm reached the area of concentration (AOC), the prediction accuracy of the model was 94.1 %, 79.4 % and 65.8 %, respectively. Furthermore, the ME-RNN model can track the active thunderstorm cells associated with lightning activity, and realize the prediction of the separation and merging of lightning high-density area, as well as the prediction of moving trend.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108070"},"PeriodicalIF":4.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}