Pub Date : 2026-02-01DOI: 10.1016/j.jastp.2026.106728
Landi Zhong , Haibo Zou , Xiaoyou Long , Jiaxin Wang , Yige Huang
{"title":"Corrigendum to ‘Optimized fuzzy logic algorithm for classifying meteorological and non-meteorological echoes in CINRAD/SA data in Poyang lake region’ [J. Atmos. Sol. Terr. Phys., Volume 278, 2026, 106708]","authors":"Landi Zhong , Haibo Zou , Xiaoyou Long , Jiaxin Wang , Yige Huang","doi":"10.1016/j.jastp.2026.106728","DOIUrl":"10.1016/j.jastp.2026.106728","url":null,"abstract":"","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106728"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073906","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}
Pub Date : 2026-02-01DOI: 10.1016/j.jastp.2026.106738
Tongsen Yue
The author investigated the degradation behavior and mechanism of FACs perovskite solar cells under high-intensity (1–7.35 suns) and multi band illumination (ultraviolet, blue, visible light) conditions. The results indicate that the degradation rate of the device is linearly related to the light intensity within the range of 1–4 solar intensities; When there are ≥5 suns, the nonlinear acceleration phenomenon is significant, mainly due to the increase of interface defects and intensified ion migration caused by the photothermal coupling effect. The multi band illumination experiment showed that ultraviolet and blue light had the greatest impact on device stability. After 500 h of ultraviolet light irradiation, PCE decreased by 42.3 %, while blue light decreased by 28.6 %. Optimized packaging (Opt-Enc-M) combined with water cooling system significantly improves stability, with PCE retention rate exceeding 80 % after 1000 h under 5 solar intensities. The research provides a theoretical basis for standardizing accelerated aging testing and improving device stability.
{"title":"Degradation behavior of perovskite solar cells under high-intensity and multi band illumination conditions","authors":"Tongsen Yue","doi":"10.1016/j.jastp.2026.106738","DOIUrl":"10.1016/j.jastp.2026.106738","url":null,"abstract":"<div><div>The author investigated the degradation behavior and mechanism of FACs perovskite solar cells under high-intensity (1–7.35 suns) and multi band illumination (ultraviolet, blue, visible light) conditions. The results indicate that the degradation rate of the device is linearly related to the light intensity within the range of 1–4 solar intensities; When there are ≥5 suns, the nonlinear acceleration phenomenon is significant, mainly due to the increase of interface defects and intensified ion migration caused by the photothermal coupling effect. The multi band illumination experiment showed that ultraviolet and blue light had the greatest impact on device stability. After 500 h of ultraviolet light irradiation, PCE decreased by 42.3 %, while blue light decreased by 28.6 %. Optimized packaging (Opt-Enc-M) combined with water cooling system significantly improves stability, with PCE retention rate exceeding 80 % after 1000 h under 5 solar intensities. The research provides a theoretical basis for standardizing accelerated aging testing and improving device stability.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106738"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073907","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}
Pub Date : 2026-01-27DOI: 10.1016/j.jastp.2026.106740
Xuehan Dong , Jiangnan Li , Zhourong Liu , Jianfei Chen , Risheng Liu
This study employs the Weather Research and Forecasting (WRF) mesoscale model to simulate an extreme warm-sector heavy rainfall event that occurred on 17–18 June 2022, over Yuanbao Mountain in Liuzhou, Guangxi. The research focuses on the cloud microphysical characteristics and latent heat budget during the heavy precipitation stage, aiming to clarify the key physical mechanisms driving the intensification of the heavy rainfall. The simulation successfully reproduces the spatio-temporal evolution of this extreme precipitation. Solid-phase hydrometeors, namely snow and graupel, are found to dominate the precipitation process, exhibiting complementary vertical distributions that formed an efficient hydrometeor conversion chain and served as the primary source of rainwater. The intense release of condensation latent heat near the 0 °C level acted as the core energy source for precipitation, while the depositional latent heat release from ice-phase particles in the mid-upper levels served as a “leading indicator” for the extreme intensification of rainfall. The center of extreme precipitation was located on the southern windward slope of the mountains. There, warm and moist air parcels underwent adiabatic cooling during upslope ascent. Upon reaching saturation, water vapor condensed, releasing substantial latent heat and establishing a typical positive feedback mechanism of “orographic lifting–condensational heating.” This process significantly altered the local thermal structure and vertical motion of the atmosphere, representing the direct cause for the triggering of the extreme heavy rainfall.
{"title":"A simulation study on the key cloud microphysical processes in an extreme warm-sector heavy rainfall over the south China mountains","authors":"Xuehan Dong , Jiangnan Li , Zhourong Liu , Jianfei Chen , Risheng Liu","doi":"10.1016/j.jastp.2026.106740","DOIUrl":"10.1016/j.jastp.2026.106740","url":null,"abstract":"<div><div>This study employs the Weather Research and Forecasting (WRF) mesoscale model to simulate an extreme warm-sector heavy rainfall event that occurred on 17–18 June 2022, over Yuanbao Mountain in Liuzhou, Guangxi. The research focuses on the cloud microphysical characteristics and latent heat budget during the heavy precipitation stage, aiming to clarify the key physical mechanisms driving the intensification of the heavy rainfall. The simulation successfully reproduces the spatio-temporal evolution of this extreme precipitation. Solid-phase hydrometeors, namely snow and graupel, are found to dominate the precipitation process, exhibiting complementary vertical distributions that formed an efficient hydrometeor conversion chain and served as the primary source of rainwater. The intense release of condensation latent heat near the 0 °C level acted as the core energy source for precipitation, while the depositional latent heat release from ice-phase particles in the mid-upper levels served as a “leading indicator” for the extreme intensification of rainfall. The center of extreme precipitation was located on the southern windward slope of the mountains. There, warm and moist air parcels underwent adiabatic cooling during upslope ascent. Upon reaching saturation, water vapor condensed, releasing substantial latent heat and establishing a typical positive feedback mechanism of “orographic lifting–condensational heating.” This process significantly altered the local thermal structure and vertical motion of the atmosphere, representing the direct cause for the triggering of the extreme heavy rainfall.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106740"},"PeriodicalIF":1.9,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077017","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}
Pub Date : 2026-01-16DOI: 10.1016/j.jastp.2026.106735
Teddy Miller Samo, Calvine Ominde, Justus Maithya, James Munyithya
This study explores the application of Artificial Neural Network (ANN) architectures, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), for forecasting CO2 concentration trends in geothermal fields. The research is motivated by the growing need to quantify and predict emissions from geothermal power generation—an important renewable energy source whose environmental impacts are often overlooked. Geothermal field measurements were combined with meteorological variables to train and validate the models. Performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and bias. The ANN (LSTM–GRU) model demonstrated superior predictive ability, achieving lower RMSE (0.0952 vs. 0.0989), MAE (0.0809 vs. 0.0828), and MAPE (0.20 % vs. 0.21 %), with a higher R2 (0.8912 vs. 0.8361) compared to the SARIMA model. Although ANN recorded a slightly higher bias (0.0797 vs. 0.002), its overall performance underscores its effectiveness in modeling complex, non-linear, and temporal patterns of CO2 concentration. The findings confirm that ANN-based models are more accurate and adaptable than conventional statistical approaches. Their application in geothermal fields provides a robust tool for forecasting emissions, enabling better planning, monitoring, and implementation of environmental strategies to mitigate the contribution of geothermal energy production to greenhouse gas emissions.
本研究探讨了人工神经网络（ANN）架构，特别是长短期记忆（LSTM）和门控循环单元（GRU）在地热田二氧化碳浓度趋势预测中的应用。地热发电是一种重要的可再生能源，其对环境的影响往往被忽视。结合地热场实测数据和气象变量对模型进行训练和验证。使用平均绝对误差（MAE）、均方根误差（RMSE）、平均绝对百分比误差（MAPE）、决定系数（R2）和偏倚来评估性能。与SARIMA模型相比，ANN （LSTM-GRU）模型的预测能力更强，RMSE （0.0952 vs. 0.0989）、MAE （0.0809 vs. 0.0828）和MAPE （0.20% vs. 0.21%）均较低，R2 （0.8912 vs. 0.8361）较高。尽管人工神经网络记录的偏差略高（0.0797 vs. 0.002），但其总体性能强调了其在模拟复杂、非线性和时间模式的CO2浓度方面的有效性。研究结果证实，基于人工神经网络的模型比传统的统计方法更准确，适应性更强。它们在地热领域的应用为预测排放提供了一个强有力的工具，能够更好地规划、监测和实施环境战略，以减轻地热能生产对温室气体排放的贡献。
{"title":"Artificial Neural Network (ANN) modeling for CO2 concentration prediction in geothermal fields","authors":"Teddy Miller Samo, Calvine Ominde, Justus Maithya, James Munyithya","doi":"10.1016/j.jastp.2026.106735","DOIUrl":"10.1016/j.jastp.2026.106735","url":null,"abstract":"<div><div>This study explores the application of Artificial Neural Network (ANN) architectures, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), for forecasting CO<sub>2</sub> concentration trends in geothermal fields. The research is motivated by the growing need to quantify and predict emissions from geothermal power generation—an important renewable energy source whose environmental impacts are often overlooked. Geothermal field measurements were combined with meteorological variables to train and validate the models. Performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R<sup>2</sup>), and bias. The ANN (LSTM–GRU) model demonstrated superior predictive ability, achieving lower RMSE (0.0952 vs. 0.0989), MAE (0.0809 vs. 0.0828), and MAPE (0.20 % vs. 0.21 %), with a higher R<sup>2</sup> (0.8912 vs. 0.8361) compared to the SARIMA model. Although ANN recorded a slightly higher bias (0.0797 vs. 0.002), its overall performance underscores its effectiveness in modeling complex, non-linear, and temporal patterns of CO<sub>2</sub> concentration. The findings confirm that ANN-based models are more accurate and adaptable than conventional statistical approaches. Their application in geothermal fields provides a robust tool for forecasting emissions, enabling better planning, monitoring, and implementation of environmental strategies to mitigate the contribution of geothermal energy production to greenhouse gas emissions.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106735"},"PeriodicalIF":1.9,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034950","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}
Pub Date : 2026-01-14DOI: 10.1016/j.jastp.2026.106734
Rafath Samrin , Pundru Chandra Shaker Reddy , K. Arun Kumar , Natha Deepthi , C. Mithra , S Bhargavi Latha , Sucharitha Yadala , Gopal Kumar Thakur
India is the dominant player in the cultivation of rice around the world. Rice yield prediction can be considered as a problem that needs to be solved. Accurate and timely prediction of rice yield can provide meaningful benefits to crop yield. Time series models are widely used for rice yield prediction, but their accuracy remains inadequate. Despite their prominence, they often fail to deliver the required precision. This study considers one of the most practical machine learning (ML) methods for predicting rice yield, allowing forecasts for the next five years. The research carried out shows the rice yield prediction done using the hybrid framework which integrates multiple linear regression (MLR) with long-short-term memory (LSTM) and its performance is compared with state-of-the-art models. The yield is forecasted from the current year through the next five years, up to 2029. The data used for the prediction model will be 1998 to 2023 from four districts of West Bengal and Uttar Pradesh. An important finding of the study was that it is possible to predict rice harvest five years in advance of actual harvest, providing useful information for agricultural decision making and planning. Researchers, policy makers and farmers can all benefit from better food security planning and resource management thanks to the study's findings, which shed light on the possibilities of combining remote sensing with biophysical parameters using ML models. Measures used to assess the suggested model include R2, RMSE, MAE, MSE, accuracy (Acc), F1 score (F1), recall (Re) and precision (Pe), among others. The suggested approach yields improved accuracy, R2, RMSE, MAE, and MSE values of 0.9823, 0.956, 0.1436, 0.021, and 0.198, respectively.
{"title":"A hybrid deep learning based framework for prediction of rice yield through integration of biophysical parameters and optical remote sensing data in India","authors":"Rafath Samrin , Pundru Chandra Shaker Reddy , K. Arun Kumar , Natha Deepthi , C. Mithra , S Bhargavi Latha , Sucharitha Yadala , Gopal Kumar Thakur","doi":"10.1016/j.jastp.2026.106734","DOIUrl":"10.1016/j.jastp.2026.106734","url":null,"abstract":"<div><div>India is the dominant player in the cultivation of rice around the world. Rice yield prediction can be considered as a problem that needs to be solved. Accurate and timely prediction of rice yield can provide meaningful benefits to crop yield. Time series models are widely used for rice yield prediction, but their accuracy remains inadequate. Despite their prominence, they often fail to deliver the required precision. This study considers one of the most practical machine learning (ML) methods for predicting rice yield, allowing forecasts for the next five years. The research carried out shows the rice yield prediction done using the hybrid framework which integrates multiple linear regression (MLR) with long-short-term memory (LSTM) and its performance is compared with state-of-the-art models. The yield is forecasted from the current year through the next five years, up to 2029. The data used for the prediction model will be 1998 to 2023 from four districts of West Bengal and Uttar Pradesh. An important finding of the study was that it is possible to predict rice harvest five years in advance of actual harvest, providing useful information for agricultural decision making and planning. Researchers, policy makers and farmers can all benefit from better food security planning and resource management thanks to the study's findings, which shed light on the possibilities of combining remote sensing with biophysical parameters using ML models. Measures used to assess the suggested model include R<sup>2</sup>, RMSE, MAE, MSE, accuracy (Acc), F1 score (F1), recall (Re) and precision (Pe), among others. The suggested approach yields improved accuracy, R<sup>2</sup>, RMSE, MAE, and MSE values of 0.9823, 0.956, 0.1436, 0.021, and 0.198, respectively.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106734"},"PeriodicalIF":1.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979917","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}
For solar photovoltaic and solar thermal systems to be effectively integrated into the energy grid, accurate prediction of solar radiation in a given area is essential. This prediction facilitates more effective planning, management, and optimization of energy production by utilities and renewable energy providers. From this vantage point, this article aims to forecast the daily global solar irradiation data of five cities in Cameroon (Bamenda, Bertoua, Ebolowa, Maroua, and Yaounde), which primarily differ in terms of solar irradiation distribution. In the study, ten different machine learning algorithms (Artificial Neural Network (ANN), Linear Regression (LR) algorithms, K-Nearest Neighbors (K-NN), Convolutional Neural Networks (CNNs), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT), Long-Short Term Memory (LSTM), Feed forward Neural Network (FNN), and Recurrent Neural Network (RNN)) are used. In the training of these algorithms, date, UT time, temperature, relative humidity, pressure, wind speed, wind direction, rainfall and solar irradiation of these cities are used. The data originate from the National Aeronautics and Space Administration and span the forty-one years (January 1, 1980, to December 31, 2021). Seven distinct statistical indicators are discussed to evaluate the effectiveness of these algorithms: t-statistic, Mean Absolute Percentage Error (MAPE), Maximum Absolute Bias Error (MABE), Means Bias Error (MBE), Root Mean Squared Error (RMSE), R-squared (R2) and relative Root Mean Squared Error (rRMSE). According to the findings, the R2, MAPE, and RMSE values of every algorithm range from 0.718 to 0.937, from 12.2 % to 25.9 %, and from 232 to 978 kJ/m2, respectively. When it came to R2 and MAPE metrics, LR consistently showed the worst performance, and the algorithms that surpassed the t-critic value were KNN, RF, and ANN. The current study concludes that, although each of the machine learning techniques investigated in this research have the ability of reliably forecasting data on global solar radiation, the KNN algorithm proves to be the most suitable choice. Next in order of precedence are RF, LSTM, ANN, GBM, CNN, RNN, FNN, DT, and LR.
{"title":"Prediction of Cameroon's global solar radiation using deep learning and machine learning algorithms","authors":"Fodoup Cyrille Vincelas Fohagui , Yemeli Wenceslas Koholé , Clint Ameri Wankouo Ngouleu , Donald Noutchogouin Tedom , Ghislain Tchuen","doi":"10.1016/j.jastp.2026.106733","DOIUrl":"10.1016/j.jastp.2026.106733","url":null,"abstract":"<div><div>For solar photovoltaic and solar thermal systems to be effectively integrated into the energy grid, accurate prediction of solar radiation in a given area is essential. This prediction facilitates more effective planning, management, and optimization of energy production by utilities and renewable energy providers. From this vantage point, this article aims to forecast the daily global solar irradiation data of five cities in Cameroon (Bamenda, Bertoua, Ebolowa, Maroua, and Yaounde), which primarily differ in terms of solar irradiation distribution. In the study, ten different machine learning algorithms (Artificial Neural Network (ANN), Linear Regression (LR) algorithms, K-Nearest Neighbors (K-NN), Convolutional Neural Networks (CNNs), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT), Long-Short Term Memory (LSTM), Feed forward Neural Network (FNN), and Recurrent Neural Network (RNN)) are used. In the training of these algorithms, date, UT time, temperature, relative humidity, pressure, wind speed, wind direction, rainfall and solar irradiation of these cities are used. The data originate from the National Aeronautics and Space Administration and span the forty-one years (January 1, 1980, to December 31, 2021). Seven distinct statistical indicators are discussed to evaluate the effectiveness of these algorithms: t-statistic, Mean Absolute Percentage Error (MAPE), Maximum Absolute Bias Error (MABE), Means Bias Error (MBE), Root Mean Squared Error (RMSE), R-squared (R<sup>2</sup>) and relative Root Mean Squared Error (rRMSE). According to the findings, the R<sup>2</sup>, MAPE, and RMSE values of every algorithm range from 0.718 to 0.937, from 12.2 % to 25.9 %, and from 232 to 978 kJ/m<sup>2</sup>, respectively. When it came to R<sup>2</sup> and MAPE metrics, LR consistently showed the worst performance, and the algorithms that surpassed the t-critic value were KNN, RF, and ANN. The current study concludes that, although each of the machine learning techniques investigated in this research have the ability of reliably forecasting data on global solar radiation, the KNN algorithm proves to be the most suitable choice. Next in order of precedence are RF, LSTM, ANN, GBM, CNN, RNN, FNN, DT, and LR.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106733"},"PeriodicalIF":1.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034949","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}
Pub Date : 2026-01-13DOI: 10.1016/j.jastp.2026.106736
Chengyu Song , Jing Wang , Yanju Liu
Based on the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data and the Global Precipitation Climatology Project (GPCP) data, this research examines the atmospheric anomalies associated with the interannual variability of the South China Sea (SCS) summer monsoon (SCSSM) onset, focusing particularly on its connection with positional changes of the subtropical westerly jet (SWJ) and thermal conditions over the eastern Tibetan Plateau (TP). The analysis reveals distinct difference of the circulation patterns between early and late monsoon onset years, marked by pronounced cyclonic anomalies with intensified rainfall during early onsets, contrasted by anticyclonic patterns and reduced precipitation during delayed onsets. A key finding demonstrates that early SCSSM onset years coincide with a southward displacement of the upper-level SWJ north of the SCS. This positional shift generates upper-level ageostrophic southerly winds over the SCS, establishing a dipole pattern of vertical motion. Upper-level divergence and low-level convergence happen over the SCS (south of the jet core), and upper-level convergence with low-level divergence occur over the Yangtze River basin (north of the jet). Such configuration amplifies the meridional circulation anomaly, enhancing ascending motions in low-latitude East Asia while strengthening subsidence in mid-latitude regions. The study also further shows that thermal anomalies over the eastern TP significantly affect SWJ positioning and subsequent monsoon onset timing. Positive heating anomalies initiate an upper-tropospheric anticyclone, triggering eastward-propagating Rossby waves and downstream cyclonic circulation. This prompts a southward migration of the SWJ east of the TP, altering East Asian circulation patterns to facilitate an earlier SCSSM establishment. These results shed new light on the TP's role in regional climate modulation via upper-level jet dynamics, offering potential predictive value for monsoon onset forecasting.
{"title":"How the East Asian subtropical westerly jet shapes the interannual variability of the South China Sea summer monsoon onset and the associated thermal forcing effect from the Tibetan plateau","authors":"Chengyu Song , Jing Wang , Yanju Liu","doi":"10.1016/j.jastp.2026.106736","DOIUrl":"10.1016/j.jastp.2026.106736","url":null,"abstract":"<div><div>Based on the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data and the Global Precipitation Climatology Project (GPCP) data, this research examines the atmospheric anomalies associated with the interannual variability of the South China Sea (SCS) summer monsoon (SCSSM) onset, focusing particularly on its connection with positional changes of the subtropical westerly jet (SWJ) and thermal conditions over the eastern Tibetan Plateau (TP). The analysis reveals distinct difference of the circulation patterns between early and late monsoon onset years, marked by pronounced cyclonic anomalies with intensified rainfall during early onsets, contrasted by anticyclonic patterns and reduced precipitation during delayed onsets. A key finding demonstrates that early SCSSM onset years coincide with a southward displacement of the upper-level SWJ north of the SCS. This positional shift generates upper-level ageostrophic southerly winds over the SCS, establishing a dipole pattern of vertical motion. Upper-level divergence and low-level convergence happen over the SCS (south of the jet core), and upper-level convergence with low-level divergence occur over the Yangtze River basin (north of the jet). Such configuration amplifies the meridional circulation anomaly, enhancing ascending motions in low-latitude East Asia while strengthening subsidence in mid-latitude regions. The study also further shows that thermal anomalies over the eastern TP significantly affect SWJ positioning and subsequent monsoon onset timing. Positive heating anomalies initiate an upper-tropospheric anticyclone, triggering eastward-propagating Rossby waves and downstream cyclonic circulation. This prompts a southward migration of the SWJ east of the TP, altering East Asian circulation patterns to facilitate an earlier SCSSM establishment. These results shed new light on the TP's role in regional climate modulation via upper-level jet dynamics, offering potential predictive value for monsoon onset forecasting.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106736"},"PeriodicalIF":1.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979918","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}
Pub Date : 2026-01-11DOI: 10.1016/j.jastp.2026.106731
Na Guo, Hongyu Zheng, Qihuan Zhou, Xinjian Yin
The accurate prediction of short-term wind power is of great significance for wind power grid integration and grid stability. Short-term wind power is not only related to historical wind power, but also influenced by meteorological factors. This paper proposes a novel combination forecasting model for short-term wind power. The max-relevance min-redundancy feature selection algorithm is used to select meteorological feature data with high correlation and low redundancy. In response to the intermittent and non-stationary characteristics of short-term wind power, variational mode decomposition algorithm is used to decompose short-term wind power, and the generated components reduce the noise and redundancy of the original data. The components obtained by variational mode decomposition are combined with the main features of the extracted meteorological data as inputs to the long short-term memory network, and the outputs of each corresponding long short-term memory network are added to obtain the final prediction result. An improved sparrow search algorithm with better optimization performance is proposed and applied to hyper-parameters optimization of long short-term memory network. Two short-term wind power datasets from different regions and sampling intervals are selected as the research objects. The proposed combination forecasting model showed 28.99 %–89.31 % decrease in RMSE, 30.81 %–86.37 % decrease in MAPE, and 11.07 %–85.38 % decrease in MAE compared with other models on the first dataset. On the second dataset, three indicators decreased by 12.21 %–80.91 %, 50.18 %–87.54 %, and 9.99 %–83.01 %. The comparison results confirmed that the proposed combination forecasting model has high prediction accuracy for short-term wind power while ensuring small system deviations, and its real-time performance can also meet the needs of practical applications.
{"title":"A novel combination forecasting model for short-term wind power","authors":"Na Guo, Hongyu Zheng, Qihuan Zhou, Xinjian Yin","doi":"10.1016/j.jastp.2026.106731","DOIUrl":"10.1016/j.jastp.2026.106731","url":null,"abstract":"<div><div>The accurate prediction of short-term wind power is of great significance for wind power grid integration and grid stability. Short-term wind power is not only related to historical wind power, but also influenced by meteorological factors. This paper proposes a novel combination forecasting model for short-term wind power. The max-relevance min-redundancy feature selection algorithm is used to select meteorological feature data with high correlation and low redundancy. In response to the intermittent and non-stationary characteristics of short-term wind power, variational mode decomposition algorithm is used to decompose short-term wind power, and the generated components reduce the noise and redundancy of the original data. The components obtained by variational mode decomposition are combined with the main features of the extracted meteorological data as inputs to the long short-term memory network, and the outputs of each corresponding long short-term memory network are added to obtain the final prediction result. An improved sparrow search algorithm with better optimization performance is proposed and applied to hyper-parameters optimization of long short-term memory network. Two short-term wind power datasets from different regions and sampling intervals are selected as the research objects. The proposed combination forecasting model showed 28.99 %–89.31 % decrease in RMSE, 30.81 %–86.37 % decrease in MAPE, and 11.07 %–85.38 % decrease in MAE compared with other models on the first dataset. On the second dataset, three indicators decreased by 12.21 %–80.91 %, 50.18 %–87.54 %, and 9.99 %–83.01 %. The comparison results confirmed that the proposed combination forecasting model has high prediction accuracy for short-term wind power while ensuring small system deviations, and its real-time performance can also meet the needs of practical applications.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106731"},"PeriodicalIF":1.9,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979915","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}
Deterioration of air quality due to increasing anthropogenic activities adversely affect human health and the environment. This study presents the space-time variability of primary pollutants, namely columnar concentrations of atmospheric Carbon monoxide (XCO), Nitrogen dioxide (XNO2), and Sulphur dioxide (XSO2), across four major Indian cities, Hyderabad, New Delhi, Chandigarh, and Guwahati during 2019–2023 using the Sentinel-5P/TROPOMI open datasets from Google Earth Engine platform. Across the study sites, atmospheric CO indicates less variability, ranging from ±5.78 ppb to ±7.7 ppb, whereas atmospheric NO2 and SO2 observed moderate to high variability of distribution ranging from ±0.04 ppb to ±0.12 ppb, and ±0.99 ppb to ±1.52 ppb, respectively. The Suomi-NPP VIIRS night-time light product used in this study signifies the extent and intensity of urbanization. Alarming trends in spatially and temporally increased pollutant concentrations are observed in Guwahati due to rapid urban expansion and unregulated biomass burning. TROPOMI and MOPITT sensors demonstrated strong agreement in CO retrievals, with a relative biases ranging from −0.40 % to 5.16 %. TROPOMI derived XCO and XNO2 retrievals show good agreement with Central Pollution Control Board measurements data. Thus, comprehensive analysis of these pollutants over these cites revealed a general increase in pollutant concentrations driven by urban development and seasonal wind patterns. The findings demonstrate the robustness of multi-sensor remote sensing datasets and urbanization indicators for monitoring air quality over rapidly developing Indian cities. The study provides valuable baseline information for developing and strengthening city-specific action plans to achieve clean air under the National Clean Air Programme (NCAP).
{"title":"Nighttime light data as a proxy for assessing air pollution in urban landscapes of India: A remote sensing perspective","authors":"Anisha Jalathota , Mahesh Pathakoti , Jaya Saxena , Kanchana Lakshmi Asuri , Mahalakshmi Venkata Dangeti , Ramesh H. Gowda , Sampath Kumar , Srinivasa Rao Goru , Prakash Chauhan","doi":"10.1016/j.jastp.2026.106732","DOIUrl":"10.1016/j.jastp.2026.106732","url":null,"abstract":"<div><div>Deterioration of air quality due to increasing anthropogenic activities adversely affect human health and the environment. This study presents the space-time variability of primary pollutants, namely columnar concentrations of atmospheric Carbon monoxide (<em>X</em>CO), Nitrogen dioxide (<em>X</em>NO<sub>2</sub>), and Sulphur dioxide (<em>X</em>SO<sub>2</sub>), across four major Indian cities, Hyderabad, New Delhi, Chandigarh, and Guwahati during 2019–2023 using the Sentinel-5P/TROPOMI open datasets from Google Earth Engine platform. Across the study sites, atmospheric CO indicates less variability, ranging from ±5.78 ppb to ±7.7 ppb, whereas atmospheric NO<sub>2</sub> and SO<sub>2</sub> observed moderate to high variability of distribution ranging from ±0.04 ppb to ±0.12 ppb, and ±0.99 ppb to ±1.52 ppb, respectively. The Suomi-NPP VIIRS night-time light product used in this study signifies the extent and intensity of urbanization. Alarming trends in spatially and temporally increased pollutant concentrations are observed in Guwahati due to rapid urban expansion and unregulated biomass burning. TROPOMI and MOPITT sensors demonstrated strong agreement in CO retrievals, with a relative biases ranging from −0.40 % to 5.16 %. TROPOMI derived <em>X</em>CO and <em>X</em>NO<sub>2</sub> retrievals show good agreement with Central Pollution Control Board measurements data. Thus, comprehensive analysis of these pollutants over these cites revealed a general increase in pollutant concentrations driven by urban development and seasonal wind patterns. The findings demonstrate the robustness of multi-sensor remote sensing datasets and urbanization indicators for monitoring air quality over rapidly developing Indian cities. The study provides valuable baseline information for developing and strengthening city-specific action plans to achieve clean air under the National Clean Air Programme (NCAP).</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106732"},"PeriodicalIF":1.9,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979916","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}
Pub Date : 2026-01-09DOI: 10.1016/j.jastp.2026.106729
Huang Yongming , Xie Yi , Que Mingyi , Lu Yong , Liu Gaochuan , Teng Yuntian
Detecting pre-earthquake anomalies in Schumann Resonance (SR) data is a significant challenge due to the low signal-to-noise ratio, with faint precursor signals often obscured by strong electromagnetic background noise. To address this, this paper proposes a novel, two-stage hybrid filtering method. The approach first uses a one-dimensional convolutional neural network (1D-CNN) to learn the patterns of a robust sliding interquartile range (IQR) detector, thereby acquiring “prior knowledge,” and then applies a fine-tuning stage to the network’s weights to selectively enhance pre-seismic patterns. The method was developed and validated using a multi-year dataset (2013–2021) of SR spectrograms and corresponding seismic events in California. Experimental results demonstrate a significant improvement in signal clarity: the average proportion of anomalies occurring within the 20 days prior to an earthquake increased from 69.91 % before filtering to 83.46 % after, representing a noteworthy average uplift of 13.55 %. This study confirms that our fine-tuned prior knowledge network is an effective approach for enhancing the visibility of potential seismic precursors in noisy SR data, reinforcing the potential of SR as a tool for short-term earthquake studies.
{"title":"Fine-tuning prior knowledge networks for seismic anomaly filtering in Schumann resonance","authors":"Huang Yongming , Xie Yi , Que Mingyi , Lu Yong , Liu Gaochuan , Teng Yuntian","doi":"10.1016/j.jastp.2026.106729","DOIUrl":"10.1016/j.jastp.2026.106729","url":null,"abstract":"<div><div>Detecting pre-earthquake anomalies in Schumann Resonance (SR) data is a significant challenge due to the low signal-to-noise ratio, with faint precursor signals often obscured by strong electromagnetic background noise. To address this, this paper proposes a novel, two-stage hybrid filtering method. The approach first uses a one-dimensional convolutional neural network (1D-CNN) to learn the patterns of a robust sliding interquartile range (IQR) detector, thereby acquiring “prior knowledge,” and then applies a fine-tuning stage to the network’s weights to selectively enhance pre-seismic patterns. The method was developed and validated using a multi-year dataset (2013–2021) of SR spectrograms and corresponding seismic events in California. Experimental results demonstrate a significant improvement in signal clarity: the average proportion of anomalies occurring within the 20 days prior to an earthquake increased from 69.91 % before filtering to 83.46 % after, representing a noteworthy average uplift of 13.55 %. This study confirms that our fine-tuned prior knowledge network is an effective approach for enhancing the visibility of potential seismic precursors in noisy SR data, reinforcing the potential of SR as a tool for short-term earthquake studies.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106729"},"PeriodicalIF":1.9,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034966","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}
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