Atmospheric Research最新文献

{"title":"Quantitative study of snow sublimation in the Altai Mountains","authors":"He Jianqiao ,&nbsp;He Xiaodong ,&nbsp;Jiang Xi ,&nbsp;Zhang Wei","doi":"10.1016/j.atmosres.2025.108109","DOIUrl":"10.1016/j.atmosres.2025.108109","url":null,"abstract":"<div><div>The sublimation of snow has a major impact on the global climate. We present a simple empirical formula that allows snow sublimation to be quantified on the interannual scale in the Altai Mountains. This empirical formula is based on the fitting of measured temperature and snow water equivalent (SWE) data for midwinter collected between 2011 and 2018 at the Koktokay snow station, located at the outlet of the Kayiertesi River Basin. The results suggest that there is a best-fitting linear relationship (<em>r</em> = −0.98; <em>p</em> &lt; 0.001) between the temperature and snow sublimation rates. The low sublimation rate, which was only 0.2 mm day⁻¹, corresponded to a low air temperature and high relative humidity, and the sublimation loss accounted for 2.6 % and 5.6 % of the annual precipitation and snowfall, respectively. Based on the proposed empirical formula and the hourly meteorological data from the ERA5 Land reanalysis, we calculated the sublimation rate in the Irtysh River Basin from 2011 to 2018. The results reveal that the cumulative snow sublimation loss was 14.3 mm y⁻¹, comprising 8.2 % of the snowfall and 3.9 % of the annual precipitation. Due to the relative ease of collecting field observations of the temperature and SWE, this simple formula, which has a high level of goodness of fit, is more applicable to the study of issues related to snow mass balance over long time scales in the Altai Mountains, and it also provides support for local snowmelt flood warning and water resource management.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108109"},"PeriodicalIF":4.5,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738170","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":"Deeper NOx emission reductions toward better air quality in the Yangtze River Delta: Numerical evidences from NOx and VOCs emissions control measures","authors":"Chaoshun Liu ,&nbsp;Junyue Wang ,&nbsp;Chungang Fang ,&nbsp;Kaixu Bai","doi":"10.1016/j.atmosres.2025.108103","DOIUrl":"10.1016/j.atmosres.2025.108103","url":null,"abstract":"<div><div>Mitigating air pollution in the Yangtze River Delta (YRD), one of China's most densely populated regions, is critical for reducing pollution-related health impacts. This study uses the WRF-Chem model to simulate the concentrations of two key pollutants, PM_2.5 and O₃, and to assess their responses to various emission control measures. Our objective is to provide actionable insights for designing effective clean air policies to improve future air quality in the YRD. The sensitivity analysis using the Comprehensive Air Quality Index (CAQI) underscores the complex interactions between PM_2.5, O₃, and reductions in NOx and VOC emissions. Notably, NOx reductions exhibit the greatest potential for lowering CAQI in summer, but in winter, the positive effects on PM_2.5 reduction may be offset by higher O₃ levels. Despite this trade-off, deep NOx emission cuts remain the most effective strategy for controlling both PM_2.5 and O₃ pollution in the YRD. These findings provide critical numerical insights and serve as a strong foundation for policymakers to develop targeted air quality management strategies.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108103"},"PeriodicalIF":4.5,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738172","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":"Development of high-resolution summer precipitation data for Xinjiang Region by fusing satellite retrieval products and Gauge observations","authors":"Qian Huang ,&nbsp;Ze Chen ,&nbsp;Qing He ,&nbsp;Chen Jin ,&nbsp;Wanpeng Qi ,&nbsp;Suxiang Yao","doi":"10.1016/j.atmosres.2025.108105","DOIUrl":"10.1016/j.atmosres.2025.108105","url":null,"abstract":"&lt;div&gt;&lt;div&gt;High-resolution precipitation data aid climate research and forecasting, reveal precipitation mechanisms, assess extreme events, provide empirical support for models, enhance prediction accuracy, and have application value for weather forecasting and beyond. The Xinjiang region of China, characterized by its vast expanse and complex terrain, exhibits a pronounced spatial and temporal disparity in precipitation distribution. Traditional ground meteorological observation stations are sparse and unevenly distributed, leading to considerable limitations and uncertainties in precipitation observation data. The Integrated Multi-satellite Retrievals for Global Precipitation Measurement products (i.e., IMERG) provide new-generation satellite precipitation measurements, but they are inaccurate in regions with complex terrain. Leveraging the advantages of multiple data sources to achieve complementary fusion of precipitation data can effectively increase the accuracy and spatiotemporal resolution of data. In this study, we proposed a merged (automatic weather station and IMERG measurements) high-spatiotemporal resolution (0.1° × 0.1°) hourly precipitation product (M-AWSI), and then evaluated its applications. For the 2027 AWS in Xinjiang, the RBFN (radial basis function neural network) method was used to obtain the gridded data, and RBFN can overcome the insufficient of traditional interpolation in local approximation ability. Furtherly, the gridded data is fused with the IMERG data by using an optimized probability matching total correction scheme, where multiple constraints are incorporated, such as effective correction radius and distance weight correction to avoid temporal and spatial discontinuity of the data in neighboring areas. Compared with observational data, the IMERG product effectively captures the spatial distribution characteristics of precipitation in the Xinjiang region. However, it exhibits significant underestimation of heavy precipitation and overestimations of weak precipitation, while failing to accurately depict the peak time in the diurnal precipitation variation. The M-AWSI data have markedly elevated the representation indices for daily precipitation across various intensities, with particularly prominent performance in augmenting the hit rate for identifying heavy rain and rainstorm events. Furthermore, in relation to the hourly probability density distribution and the attributes of daily precipitation variability, the alignment between M-AWSI and observational data has been significantly strengthened. Additionally, the M-AWSI data demonstrates a substantial improvement in its ability to represent extreme precipitation zones and their evolutionary characteristics compared to IMERG data. The M-AWSI data effectively overcomes the limitations of IMERG, which tend to underestimate heavy precipitation and overestimate weak precipitation. The establishment of this dataset will contribute to a deeper understanding of precipita","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108105"},"PeriodicalIF":4.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738171","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":"Pacific decadal oscillation modulates the decadal variations of spring Eurasian snow cover","authors":"Taotao Zhang ,&nbsp;Haishan Chen","doi":"10.1016/j.atmosres.2025.108102","DOIUrl":"10.1016/j.atmosres.2025.108102","url":null,"abstract":"<div><div>The prominent decadal variations can be observed in the warming-induced long-term decline of Eurasian spring snow cover, which have reversed or accelerated the decreasing trend during some periods, making the understanding and prediction of the response of snow cover to climate warming more complicated. However, it remains unknown what contributes to such decadal variations. This study reveals that the spring snow cover over the mid-high latitudes of Eurasia features a consistent oscillation on the decadal timescale, which is tightly associated with the preceding winter Pacific Decadal Oscillation (PDO). The sea surface temperature anomaly related to the winter PDO can persist into the following spring and excite an anomalous wave train type circulation extending eastward from North Pacific to Eurasia. In the positive PDO phases, there is an anticyclonic and a cyclonic circulation over northern Europe and northeast Eurasia that would reduce the surface air temperature over northern Eurasia via favoring the cold advection and negative diabatic heating. Consequently, the decreased air temperature is conducive to forming the positive anomaly of snow cover. Our results can provide a valuable clue for the decadal prediction of spring Eurasian snow cover variations.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108102"},"PeriodicalIF":4.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747051","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":"Characterizing optical properties of HULIS versus WSOC in cloud water of Eastern China - Insights into secondary formation in cloud processes","authors":"Xinghui Liu ,&nbsp;Huiting Mao ,&nbsp;Xiaoling Nie ,&nbsp;Jiebo Zhen ,&nbsp;Ping Du ,&nbsp;Tao Li ,&nbsp;Xinfeng Wang ,&nbsp;Likun Xue ,&nbsp;Yan Wang ,&nbsp;Jianmin Chen","doi":"10.1016/j.atmosres.2025.108091","DOIUrl":"10.1016/j.atmosres.2025.108091","url":null,"abstract":"<div><div>Effects of humic like substances (HULIS) on cloud condensation nuclei, initiation of ice, and global radiative forcing of clouds highlight their significant influence on climate dynamics. However, optical properties of HULIS in cloud water and their indications remain poorly understood. Cloud water samples were gathered from June through August 2021 at Mt. Tai, China, and HULIS and water-soluble organic carbon (WSOC) within these samples were quantitatively analyzed. The optical characteristics of these substances were examined using UV–Vis and fluorescence spectroscopy. The average concentration of HULIS was 3.14 ± 2.14 mgC L⁻¹ contributing 38 ± 15 wt% to WSOC (9.42 ± 7.50 mgC L⁻¹). A mass absorption efficiency value at 365 nm (MAE₃₆₅) of 0.72 m² g⁻¹ and an absorption Ångström exponent (AAE₃₀₀_–₄₀₀) value of 3.79 were measured for HULIS compared to 0.54 m² g⁻¹ and 3.58 for WSOC. Three fluorescent components, i.e., less oxygenated HULIS (LO-HULIS), highly oxygenated HULIS (HO-HULIS), and protein-like substances (PRLIS), were identified in both HULIS and WSOC employing EEM and parallel factor analysis. HULIS light absorption was dominated by HO-HULIS, followed by that of LO-HULIS and PRLIS. Compared to WSOC, HULIS exhibited enhanced light absorption and a higher degree of humification, attributed to its elevated levels of HO-HULIS and LO-HULIS alongside reduced PRLIS. Furthermore, the degradation of PRLIS was posited as a potential pathway for LO-HULIS formation based on the increasing trend in the ratio of LO-HULIS:PRLIS with decreasing PRLIS in WSOC. This study explores secondary HULIS formation in cloud water, advancing our understanding of HULIS evolution.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108091"},"PeriodicalIF":4.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725067","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":"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":"A lightning augmented recurrent nowcasting model based on self-supervised learning and multi-modal fusion method","authors":"Liang Zhang ,&nbsp;Qian Li ,&nbsp;Zeming Zhou ,&nbsp;Kangquan Yang","doi":"10.1016/j.atmosres.2025.108089","DOIUrl":"10.1016/j.atmosres.2025.108089","url":null,"abstract":"<div><div>The heavy class imbalance problem and the multi-source observations fusion remain challenges for lightning nowcasting based on deep learning method. To address the problems, this paper proposes a novel lightning augmented recurrent nowcasting (LARN) model which trained with a two-step training approach. The first training stage is designed as a lightning augmented pretraining (LAP) module based on self-supervised learning method, which can focus on the critical lightning events to solve the heavy class imbalance problem. The second training stage is designed as a multi-modal data fusion module (MDF), which can effectively fuse lightning, radar and satellite observations to nowcasting lightning. The results of experimental evaluations demonstrate the performance of LARN model outperforms the existing nowcasting models with lead times for up to 90 min. The ablation study shows that the two training stages cooperate well, with the LAP module improving the hit rate and the MDF module reducing the false alarm rate. For the radar and satellite modalities, the vertically integrated liquid (VIL) exhibits the most informative power for lightning nowcasting, followed by 10.7 μm brightness temperatures (IR107) and then 6.9 μm brightness temperatures (IR069). Case studies show that the LARN model can better predict the lightning evolution under different type of thunderstorms. Since the LARN model can reflect the lightning distribution in the reality scenes without adopting under-sampling strategy and subjective loss function design, therefore it can apply to different lightning datasets.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"321 ","pages":"Article 108089"},"PeriodicalIF":4.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715703","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":"Corrigendum to “Black carbon pollution over India simulated with recent emission inventories and WRF-CHEM model” [Atmospheric Research, 315 (2025) 107931].","authors":"Rahul Sheoran ,&nbsp;Sanhita Ghosh ,&nbsp;U.C. Dumka ,&nbsp;D.G. Kaskaoutis ,&nbsp;R.E.P. Sotiropoulou ,&nbsp;S.D. Attri ,&nbsp;V.K. Soni ,&nbsp;Suresh Tiwari ,&nbsp;Siddharth Singh ,&nbsp;Rahesh K. Tiwari","doi":"10.1016/j.atmosres.2025.108083","DOIUrl":"10.1016/j.atmosres.2025.108083","url":null,"abstract":"","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"320 ","pages":"Article 108083"},"PeriodicalIF":4.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716176","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":"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}