Journal of Atmospheric and Solar-Terrestrial Physics最新文献

{"title":"Impact of climatic factors on volume aerosol size distribution over Northern India","authors":"Nabin Sharma ,&nbsp;Sishir Dahal ,&nbsp;Sunil Kumar Chaurasiya ,&nbsp;Sarvan Kumar ,&nbsp;Kalpana Patel","doi":"10.1016/j.jastp.2025.106633","DOIUrl":"10.1016/j.jastp.2025.106633","url":null,"abstract":"<div><div>The correlation between climatic factors and the volume aerosol size distribution (V-ASD), which was extracted from AERONET data for 5 years at five monitoring sites throughout Northern India, has been investigated. Fine-mode aerosols (&lt;0.58 μm) showed negative correlations with rainfall (RF) and relative humidity (RH), where RH enhances hygroscopic growth, making particles more prone to removal by precipitation scavenging. Depending on the station, the correlation between Wind Speed (WS) and V-ASD varied. The pre-monsoon period was marked by greater WS and Boundary Layer Height, which improved aerosol dispersion and mixing, but the monsoon period (June–September) regularly raised RF and RH, according to seasonal changes. At most sites, coarse-mode aerosols (&gt;0.58 μm) showed a positive correlation with temperature, whereas fine-mode particles showed negative correlations with both temperature and relative humidity. Coarse particle development was greatly impacted by temperature-driven processes, especially at Gandhi College and Kanpur. These results demonstrate the intricate relationships that exist in North India between regional climatic factors and aerosol size distributions.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106633"},"PeriodicalIF":1.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian-optimised hybrid machine learning model for coastal wind gust prediction in a marine-influenced atmospheric boundary layer","authors":"Mohammad Reza Chalak Qazani ,&nbsp;Mahmood Al-Bahri ,&nbsp;Muhammad Zakarya ,&nbsp;Falah Y.H. Ahmed ,&nbsp;Amirhossein Mohajerzadeh ,&nbsp;Saeid Hosseini ,&nbsp;Mehdi Moayyedian ,&nbsp;Zoran Najdovski ,&nbsp;Houshyar Asadi","doi":"10.1016/j.jastp.2025.106629","DOIUrl":"10.1016/j.jastp.2025.106629","url":null,"abstract":"<div><div>Accurate prediction of wind gusts is crucial for applications in aviation, coastal and marine operations, and atmospheric dynamics research. This study presents a novel model combining a Sequencing Block and a Layer Perceptron (MLP) optimised using Bayesian Optimisation (B-MLP) to enhance the precision of coastal atmospheric wind gust forecasts. The model is validated using a 13-year dataset (January 2010 to March 2023) from Muscat International Airport, a coastal site influenced by Gulf of Oman sea–land breeze interactions. The Sequencing Block is designed and developed to capture the optimal arrangement of dataset segmentation using atmospheric and boundary layer parameters, thereby enhancing the model's predictive accuracy. The B-MLP model's efficacy is compared against traditional methods, including Decision Tree (DT) and Support Vector Regression (SVR), demonstrating a substantial enhancement in forecast quality. The B-MLP model achieves a correlation coefficient of 0.817 between actual and forecasted wind gusts, outperforming DT and SVR by notable margins in both accuracy and error reduction. The newly proposed model is validated using a 13-year dataset (January 2010 to March 2023) from Muscat International Airport, a coastal site influenced by Gulf of Oman sea–land breeze interactions, to prove its robustness and applicability on a 1-day ahead prediction horizon. The proposed B-MLP model improves forecast accuracy and offers a scalable solution for atmospheric boundary layer studies, marine safety applications, and real-time meteorological data analysis.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106629"},"PeriodicalIF":1.9,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variability in the thermospheric neutral mass density: A multiple model comparison","authors":"Yongliang Zhang,&nbsp;Patrick B. Dandenault,&nbsp;Larry J. Paxton,&nbsp;Robert Schaefer,&nbsp;Clayton Cantrall,&nbsp;Hyosub Kil,&nbsp;Rafael Mesquita,&nbsp;Matthew E. Zuber","doi":"10.1016/j.jastp.2025.106630","DOIUrl":"10.1016/j.jastp.2025.106630","url":null,"abstract":"<div><div>Variability in the thermospheric neutral mass density in LEO/VLEO altitudes has been investigated using outputs from five models (MSIS2.0, HASDM, WACCM-X, TIEGCM, and WAM-IPE) under different geophysical conditions: geomagnetically quiet, moderate storm and super storm. These models are selected to represent empirical, assimilation, and physics-based methods. We compared the global neutral mass density distribution and the time variations in the densities using equatorial and polar orbits at three fixed LEO/VLEO altitudes (100, 200, and 300 km) from the five models. Our key findings from the analyses are: (1) there are significant systematic biases among the model results; (2) WACCM-X, TIEGCM and HASDM peak densities are roughly consistent with each other during a super storm. However, their UT differences are up to a half day; (3) WAM-IPE and MSIS-2.0 models tend to give lower densities than other models; (4) the geomagnetic activity impact on neutral mass densities increases with altitude and it is negligible at 100 km altitude, becomes evident at 150 km, and is significant at 200 km; (5) geomagnetic storms tend to reduce the biases among the model densities The systematic biases among models are likely due to the different parameterizations, drivers and boundary conditions used in the models. A systematic evaluation of the models using multiple and cross-calibrated ground truth data sets is needed to fully address the biases and offer the insight required to improve the models.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106630"},"PeriodicalIF":1.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatially heterogeneous wetting and climatic drivers of precipitation variability in arid and semi-arid Northwest China since 1960","authors":"Hao Wu ,&nbsp;Xinyan Li ,&nbsp;Zuohui Cai ,&nbsp;Yao Chen","doi":"10.1016/j.jastp.2025.106632","DOIUrl":"10.1016/j.jastp.2025.106632","url":null,"abstract":"<div><div>This study analyzes the spatiotemporal evolution of precipitation across the arid and semi-arid regions of Northwest China from 1960 to 2020, focusing on long-term trends, regional disparities, and climatic drivers. Piecewise linear regression reveals a sharp wetting transition around 2000, characterized by rising precipitation frequency and intensity. However, this trend is spatially heterogeneous. Fuzzy clustering reveals four distinct change patterns that correspond with key geographic subregions. Before 2000, increases were concentrated in the arid Northern Tianshan (NT) and Tarim Basin (TB); after 2000, semi-arid Northeastern Tibetan Plateau (NETP) and Loess Plateau (LP) became dominant contributors. Precipitation become more seasonally balanced, potentially easing drought stress. Yet, extreme precipitation events have intensified, particularly in arid regions, posing escalating risks to the fragile ecosystems. Slow feature analysis isolates dominant low-varying modes, revealing that NT and NETP are primarily influenced by El Niño-Southern Oscillation, with a two-year lag in NETP. LP is modulated by the East Asian summer monsoon. TB is predominantly affected by the Eurasian wave train pattern and equatorial Indian Ocean sea surface temperature anomalies. These results highlight the complex and regionally varied hydroclimatic change across Northwest China, urgently calling for tailored adaptation and water management strategies.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106632"},"PeriodicalIF":1.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Planetary feature of the ionospheric current activity during 10–11 October 2024 extremely strong magnetic storm","authors":"N.G. Kleimenova ,&nbsp;L.I. Gromova ,&nbsp;S.V. Gromov ,&nbsp;L.M. Malysheva","doi":"10.1016/j.jastp.2025.106631","DOIUrl":"10.1016/j.jastp.2025.106631","url":null,"abstract":"<div><div>One important feature of the ionosphere E region represents the horizontal westward and eastward electric currents (electrojets) which magnetic signature is observed on the ground as negative and positive bays in the H-component of the magnetic field. The electrojets significantly enhance during magnetic storms. The magnetic storm on 10–11 October 2024 (Dst_min = −335 nT) was one of the strongest storms in the present 25th solar cycle. Large variations in the intensity of the IMF By and Bz (from +40 nT to −40 nT) were observed during the main phase of the storm at the very high solar wind dynamic pressure (Psw) up to ∼ 40 nPa. Here we present the planetary features of the configuration of the ionosphere electrojets, which were studied by applying the global maps based on the magnetic measurements on 66 LEO satellites of the AMPERE project. The results of our study demonstrated the strong dependence of the ionospheric electrojets and correspondent field-aligned current (FAC) features on the sign and values on the IMF By and Bz components of the Interplanetary Magnetic field (IMF) as well as on the solar wind dynamic pressure (Psw). It was shown also that the sign of the IMF By strongly controls the direction of the dayside polar electrojet near magnetic noon and the width of the region where it is observed. It was concluded that during a strong magnetic storm, the planetary state of the ionospheric electrojets depends on state of the interplanetary space.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106631"},"PeriodicalIF":1.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined influence of sudden stratospheric warming (SSW) and geomagnetic storm forcing on reduced TEC over low-latitude Northern Africa","authors":"J.B. Fashae ,&nbsp;O.S. Bolaji ,&nbsp;E.F. Nymphas","doi":"10.1016/j.jastp.2025.106621","DOIUrl":"10.1016/j.jastp.2025.106621","url":null,"abstract":"<div><div>This study analyzes total electron content (TEC) variations over the equatorial and low-latitude region in the Northern African during the January 2013 sudden stratospheric warming (SSW) and concurrent geomagnetic storm to assess their combined impacts on equatorial electrojet (EEJ) and the equatorial ionization anomaly (EIA).</div><div>During the major SSW phase (12–16 January 2013), the ionosphere exhibited pronounced semidiurnal variations in TEC and inferred E X B drift, driven by the amplification of atmospheric tides. These tidal enhancements strengthened eastward electric fields, increasing the inferred upward E × B drifts, leading to a poleward shift in the northern EIA crest and TEC significantly enhanced by 71 % relative to non-SSW and SSW onset conditions.</div><div>During the geomagnetic storm overlapping the 2013 SSW event on 17–18 January 2013, westward penetration electric fields (PPEFs) dominated the low-latitude ionosphere, counteracting SSW-driven daytime eastward fields. Combined with equatorward thermospheric winds, these effects suppressed SSW-induced tidal enhancements, reducing upward inferred E × B drifts, decreasing TEC, and shifting the EIA crest equatorward.</div><div>This study reveals regionally distinct ionospheric responses to SSW and geomagnetic storms, emphasizing the need to integrate lower atmospheric and magnetospheric forcings in space weather models, particularly for under-observed African longitudes.</div></div><div><h3>Plain Language Summary</h3><div>This study examines how the low-latitude ionosphere over Africa responded to a major sudden stratospheric warming (SSW) event that occurred in January 2013, coinciding with a moderate geomagnetic storm. Using observations from ground-based GPS receivers and magnetometers, we explore how changes in the upper atmosphere were influenced by both the SSW and the geomagnetic storm. Our results show that the ionosphere experienced a significant reduction in total electron content (TEC) and a weakening of the equatorial ionization anomaly. These changes were caused by the combined effects of storm-time electric fields, thermospheric winds, and enhanced atmospheric tides generated during the SSW. We also observed clear semi-diurnal patterns in the data, highlighting the role of amplified tidal waves during this period. This study emphasizes that both space weather (geomagnetic storms) and atmospheric weather (SSW events) can interact to drive complex changes in the ionosphere, especially over the African sector.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106621"},"PeriodicalIF":1.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing machine learning models for predicting daily relative humidity and solar radiation using lagged time series data inputs in a semi-arid climate","authors":"Jitendra Rajput ,&nbsp;Nand Lal Kushwaha ,&nbsp;Aman Srivastava ,&nbsp;Dinesh Kumar Vishwakarma ,&nbsp;A.K. Mishra ,&nbsp;P.K. Sahoo ,&nbsp;Truptimayee Suna ,&nbsp;Lalita Rana ,&nbsp;Malkhan Singh Jatav ,&nbsp;Jitendra Kumar ,&nbsp;Dimple ,&nbsp;Shaloo ,&nbsp;Himani Bisht ,&nbsp;Ashish Rai ,&nbsp;Bilel Zerouali ,&nbsp;Chaitanya B. Pande ,&nbsp;A. Elbeltagi","doi":"10.1016/j.jastp.2025.106619","DOIUrl":"10.1016/j.jastp.2025.106619","url":null,"abstract":"<div><div>Estimating relative humidity and solar radiation is crucial for understanding their impact on the hydrological cycle, which in turn affects water resource availability and distribution. Accurately predicting these variables is challenging due to their non-linear behaviour. Machine learning (ML) techniques have attracted significant attention for addressing such complex non-linear problems. In this study, the prediction of relative humidity and solar radiation for ICAR-IARI (Indian Council of Agricultural Research - Indian Agricultural Research Institute), New Delhi, India, under semi-arid climate, was performed using long-term data spanning 31 years (1990–2020) and developed machine learning models such as linear regression (LR), multilayer perceptron (MLP), sequential minimal optimization-support vector machine (SMO-SVM), additive regression (AR), and random forest (RF). The performance of these models was evaluated using various statistical metrics, including coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), index of agreement (d), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), relative root squared error (RRSE) and mean absolute percentage error (MAPE). The results indicated that for relative humidity prediction, the RF model yielded the best results during training and testing periodswith statistical metrics R², NSE, d, MAE, RMSE, RAE, RRSE and MAPE as 0.79, 0.40, 0.74, 6.31, 12.66, 54.62, 74.70 and 9.90, respectively during testing phase. The SMO-SVM model emerged as the best performer for solar radiation prediction, with performance metrics during the testing phase as follows: R² = 0.89, MAE = 2.79, RMSE = 3.55, RAE = 52.54, RRSE = 59.59, NSE = 0.64, d = 0.88, and MAPE = 20.50. The findings of this study could be useful for developing and comparing relative humidity prediction models under different climatic conditions, using similar long-term data.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"276 ","pages":"Article 106619"},"PeriodicalIF":1.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment and enhancement of Landsat 8 land surface temperature retrieval using mono window algorithm and machine learning approaches","authors":"Noorfarhah Jasmin Jamaludin ,&nbsp;Ahmad Fikri Abdullah ,&nbsp;Nur Atirah Muhadi ,&nbsp;Aimrun Wayayok","doi":"10.1016/j.jastp.2025.106618","DOIUrl":"10.1016/j.jastp.2025.106618","url":null,"abstract":"<div><div>Urban regions such as Klang Valley in Malaysia are increasingly affected by rising Land Surface Temperatures (LST) driven by rapid urbanization and climate change. Accurate LST retrieval is essential for environmental monitoring, climate analysis, and urban heat island studies. However, the challenges remain in validating satellite-derived LST against ground-based measurements, particularly in tropical regions with frequent cloud cover. This study aims to retrieve LST using the Mono Window Algorithm (MWA) applied to the thermal infrared data from Landsat 8 and 9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery from 2015 to 2022. Selected images with less than 40 % cloud cover were used to ensure data quality. The retrieved LST values were validated against the air temperature dataset obtained from the Malaysian Meteorological Department (METMalaysia) at several ground stations. To enhance prediction accuracy, machine learning regression models including Fine Tree of Regression Trees, Fine Gaussian Support Vector Machine (SVM), and Wide Neural Network (NN) were tested. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). The Fine Tree of Regression Trees model achieved the highest accuracy, with RMSE of 0.8876 °C, MAE of 0.7878 °C, and R² of 0.7011. These findings demonstrate the potential of combining MWA with machine learning for reliable LST estimation and highlight its applicability in environmental and urban climate analysis research.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"276 ","pages":"Article 106618"},"PeriodicalIF":1.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solar radiation on complex underlying surfaces in Xinjiang: A typical arid and semi-arid region in the northwestern China","authors":"Chengming Zhang ,&nbsp;Xin Liu ,&nbsp;Shuyi Chen ,&nbsp;Jianrong Bi ,&nbsp;Yonghang Chen ,&nbsp;Qing He ,&nbsp;Ting He ,&nbsp;Yunhong Xu ,&nbsp;Hui Li","doi":"10.1016/j.jastp.2025.106617","DOIUrl":"10.1016/j.jastp.2025.106617","url":null,"abstract":"<div><div>Xinjiang, located in the northwestern China, is a typical arid and semi-arid region characterized by abundant solar energy resources and highly diverse surface types, including oases, deserts, and snow-covered areas. Solar radiation plays a fundamental role in the Earth's energy budget, governing land–atmosphere interactions and driving essential atmospheric processes. Accurate quantification of downward surface solar radiation (DSSR) over such complex terrains is critical for improving regional climate simulations, optimizing water resource allocation, and facilitating the efficient utilization of solar energy. However, the scarcity of ground-based radiation measurements in this region hinders comprehensive solar resource assessments. Consequently, satellite-derived radiation products, such as those from the Clouds and the Earth's Radiant Energy System (CERES) Single Satellite Footprint (SSF) Aqua dataset, serve as valuable substitutes. Nonetheless, uncertainties in DSSR retrievals over complex surface types necessitate systematic validation. <em>Therefore, in this study the downward surface solar radiation (DSSR) derived from satellite for cloud and all sky conditions were compared with ground-based observations.</em>The CERES-derived DSSR showed overall agreement with ground-based observations in temporal and spatial patterns but tended to overestimate radiation, especially in the southern Xinjiang. Larger discrepancies occurred in Kashgar and Hotan, mainly due to dust from the Taklamakan Desert, which affects satellite retrieval accuracy.Under the clear-sky conditions, CERES DSSR data <strong>performed</strong> better than that under the all-sky conditions, indicating that clouds <strong>had</strong> a significant impact on CERES DSSR retrieval, especially in Yanqi and Tacheng. A similar effect <strong>is</strong> observed in Ruoqiang and Hotan, where the typically low cloud cover suggested that inaccuracies <strong>may stem</strong> from clouds or the misinterpretation of dust as clouds. Six fitting models wer<strong>e</strong> optimized, with results <strong>showing</strong> that the linear model <strong>performed</strong> best under the both all-sky and clear-sky conditions at the most stations.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"276 ","pages":"Article 106617"},"PeriodicalIF":1.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climatological trends and variability of fog characteristics and meteorological parameters over cities along the Indo-Gangetic Plain","authors":"Deevi Prathima,&nbsp;A.N.V. Satyanarayana","doi":"10.1016/j.jastp.2025.106616","DOIUrl":"10.1016/j.jastp.2025.106616","url":null,"abstract":"<div><div>The Indo-Gangetic Plains (IGP) in the northern region of India experience widespread and persistent winter dense fog with significant variability and intensity that severely disrupts the transport and impacts human health. The present study investigated the climatological of trends of fog hours, days, duration and intensity, over five city regions; Amritsar (31.42° N, 74.47° E), Delhi (28.56° N, 77.1° E), Lucknow (26.7655° N, 80.8854° E), Patna (25.5941° N, 85.1376° E) and Gaya (24.7914° N, 85.0002° E) using half-hourly surface observations of visibility and meteorological observations during winter months from 1991 to 2024. The non-parametric statistical methods, including the Mann-Kendall test and Theil-Sen's slope estimator, were used to assess trends in fog variations during the winter months of the study period. The classification of fog events into radiation and advection types reveals a dominant contribution of radiation fog, accounting for more than 80 % of total fog events across all five cities. Higher and statistically significant increasing trends in fog hours and days are noticed throughout the cities, even during November and February. Short-duration fog events show predominantly declining trends, especially in Amritsar and Delhi, whereas moderate-duration and long-duration events show increasing trends in Amritsar, Delhi, and Lucknow. A higher percentage of short, moderate, and long duration fog events are noticed in shallow and moderate intensity fog cases compared to dense and very dense fog conditions. The analysis reveals a significant trend in shallow, moderate, dense, and very dense intensity of events is noticed in western IGP cities (Amritsar, Delhi, and Lucknow) compared to eastern IGP cities (Patna and Gaya). Climatological trends of air temperature exhibit increasing (decreasing) trends in air temperature during all fog intensity conditions over western IGP (eastern IGP) cities, whereas relative humidity reveals an overall increasing trend in the winter months.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"276 ","pages":"Article 106616"},"PeriodicalIF":1.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}