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 (Dstmin = −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.
{"title":"Planetary feature of the ionospheric current activity during 10–11 October 2024 extremely strong magnetic storm","authors":"N.G. Kleimenova , L.I. Gromova , S.V. Gromov , 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<sub>min</sub> = −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}
Pub Date : 2025-09-10DOI: 10.1016/j.jastp.2025.106621
J.B. Fashae , O.S. Bolaji , E.F. Nymphas
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).
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.
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.
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.
Plain Language Summary
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.
本文分析了2013年1月平流层突然变暖（SSW）和同期地磁风暴期间北非赤道和低纬度地区总电子含量（TEC）的变化，以评估它们对赤道电喷流（EEJ）和赤道电离异常（EIA）的综合影响。在主要的SSW阶段（2013年1月12日至16日），电离层在大气潮汐放大的驱动下表现出明显的TEC和推断的E X B漂移的半日变化。这些潮汐增强增强了东向电场，增加了推断的E × B向上漂移，导致北部EIA波峰极移，TEC相对于非SSW和SSW开始条件显著增强了71%。在2013年1月17-18日与2013年SSW事件重叠的地磁风暴期间，向西穿透电场（ppef）主导了低纬度电离层，抵消了SSW驱动的白天向东场。与赤道方向的热层风相结合，这些效应抑制了海温引起的潮汐增强，减少了向上推断的E × B漂移，减少了TEC，并使EIA波峰向赤道方向移动。这项研究揭示了电离层对SSW和地磁风暴的区域性不同响应，强调需要在空间天气模式中整合低层大气和磁层强迫，特别是对观测不足的非洲经度。本研究考察了非洲低纬度电离层如何应对2013年1月发生的一次重大的平流层突然变暖（SSW）事件，该事件与一次中度地磁风暴同时发生。利用地面GPS接收机和磁力计的观测资料，我们探讨了SSW和地磁风暴对高层大气变化的影响。结果表明，电离层总电子含量（TEC）显著降低，赤道电离异常减弱。这些变化是由风暴时电场、热层风和SSW期间产生的增强的大气潮汐的综合影响引起的。我们还在数据中观察到清晰的半日模式，突出了在此期间放大的潮汐波的作用。这项研究强调，空间天气（地磁风暴）和大气天气（SSW事件）可以相互作用，推动电离层的复杂变化，特别是在非洲地区。
{"title":"Combined influence of sudden stratospheric warming (SSW) and geomagnetic storm forcing on reduced TEC over low-latitude Northern Africa","authors":"J.B. Fashae , O.S. Bolaji , 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}
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.
估算相对湿度和太阳辐射对于了解它们对水文循环的影响至关重要，而水文循环反过来又影响水资源的可用性和分布。由于这些变量的非线性行为，准确预测它们是具有挑战性的。机器学习（ML）技术在解决这些复杂的非线性问题方面引起了极大的关注。本研究利用1990-2020年31年的长期数据，对印度新德里ICAR-IARI（印度农业研究委员会-印度农业研究所）在半干旱气候条件下的相对湿度和太阳辐射进行了预测，并开发了线性回归（LR）、多层感知器（MLP）、序列最小优化支持向量机（smoo - svm）、加性回归（AR）和随机森林（RF）等机器学习模型。采用各种统计指标，包括决定系数（R2）、Nash-Sutcliffe效率（NSE）、一致性指数(d)、平均绝对误差（MAE）、均方根误差（RMSE）、相对绝对误差（RAE）、相对均方根误差（RRSE）和平均绝对百分比误差（MAPE），对这些模型的性能进行评估。结果表明，RF模型在训练和测试阶段的相对湿度预测效果最好，测试阶段的统计指标R2、NSE、d、MAE、RMSE、RAE、RRSE和MAPE分别为0.79、0.40、0.74、6.31、12.66、54.62、74.70和9.90。SMO-SVM模型在太阳辐射预测中表现最好，其测试阶段的性能指标为：R2 = 0.89, MAE = 2.79, RMSE = 3.55, RAE = 52.54, RRSE = 59.59, NSE = 0.64, d = 0.88, MAPE = 20.50。这项研究的发现可能有助于开发和比较不同气候条件下使用类似长期数据的相对湿度预测模型。
{"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 , Nand Lal Kushwaha , Aman Srivastava , Dinesh Kumar Vishwakarma , A.K. Mishra , P.K. Sahoo , Truptimayee Suna , Lalita Rana , Malkhan Singh Jatav , Jitendra Kumar , Dimple , Shaloo , Himani Bisht , Ashish Rai , Bilel Zerouali , Chaitanya B. Pande , 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 (R<sup>2</sup>), 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 R<sup>2</sup>, 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: R<sup>2</sup> = 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}
Pub Date : 2025-09-03DOI: 10.1016/j.jastp.2025.106618
Noorfarhah Jasmin Jamaludin , Ahmad Fikri Abdullah , Nur Atirah Muhadi , Aimrun Wayayok
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.
马来西亚巴生谷等城市地区日益受到快速城市化和气候变化驱动的地表温度上升的影响。准确的地表温度反演对环境监测、气候分析和城市热岛研究至关重要。然而，在验证卫星获得的地表温度与地面测量的对比方面仍然存在挑战，特别是在云层频繁覆盖的热带地区。本研究旨在利用单窗口算法（MWA）对Landsat 8和9操作陆地成像仪/热红外传感器（OLI/TIRS） 2015年至2022年的热红外数据进行地表温度检索。选择云量小于40%的图像以确保数据质量。检索到的LST值与马来西亚气象局（METMalaysia）在几个地面站获得的气温数据集进行了验证。为了提高预测精度，对回归树精细树（Fine Tree of regression Trees）、精细高斯支持向量机（Fine Gaussian Support Vector machine， SVM）和广义神经网络（Wide Neural Network， NN）等机器学习回归模型进行了测试。采用均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R2）对模型性能进行评价。Fine Tree of Regression Trees模型的准确率最高，RMSE为0.8876°C， MAE为0.7878°C， R2为0.7011。这些发现证明了将MWA与机器学习结合起来进行可靠的LST估计的潜力，并突出了其在环境和城市气候分析研究中的适用性。
{"title":"Assessment and enhancement of Landsat 8 land surface temperature retrieval using mono window algorithm and machine learning approaches","authors":"Noorfarhah Jasmin Jamaludin , Ahmad Fikri Abdullah , Nur Atirah Muhadi , 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 (R<sup>2</sup>). The Fine Tree of Regression Trees model achieved the highest accuracy, with RMSE of 0.8876 °C, MAE of 0.7878 °C, and R<sup>2</sup> 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}
Pub Date : 2025-08-30DOI: 10.1016/j.jastp.2025.106617
Chengming Zhang , Xin Liu , Shuyi Chen , Jianrong Bi , Yonghang Chen , Qing He , Ting He , Yunhong Xu , Hui Li
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. 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.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 performed better than that under the all-sky conditions, indicating that clouds had a significant impact on CERES DSSR retrieval, especially in Yanqi and Tacheng. A similar effect is observed in Ruoqiang and Hotan, where the typically low cloud cover suggested that inaccuracies may stem from clouds or the misinterpretation of dust as clouds. Six fitting models were optimized, with results showing that the linear model performed best under the both all-sky and clear-sky conditions at the most stations.
{"title":"Solar radiation on complex underlying surfaces in Xinjiang: A typical arid and semi-arid region in the northwestern China","authors":"Chengming Zhang , Xin Liu , Shuyi Chen , Jianrong Bi , Yonghang Chen , Qing He , Ting He , Yunhong Xu , 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}
Pub Date : 2025-08-27DOI: 10.1016/j.jastp.2025.106616
Deevi Prathima, A.N.V. Satyanarayana
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.
{"title":"Climatological trends and variability of fog characteristics and meteorological parameters over cities along the Indo-Gangetic Plain","authors":"Deevi Prathima, 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}
This study investigates the diurnal and seasonal variations of lightning activity over the complex terrain of Nepal, which spans elevations from 59 m to 8848 m above sea level. Despite lightning being a leading weather-related hazard in Nepal, causing over 100 fatalities annually, the altitudinal distribution of lightning has received limited attention. We use lightning data from the GLD360 network and meteorological parameters from the ERA5 reanalysis to analyze the influence of Convective Available Potential Energy (CAPE), wind, temperature, humidity, and cloud structure on lightning activity. Results reveal a strong altitudinal gradient in lightning flash density (FD), with maxima in the southern foothills and significant decrease toward higher elevations. Temporally, lightning exhibits two prominent peaks: one in the afternoon and the other around midnight. Afternoon lightning in the higher elevations is associated with surface heating, upslope winds, increased CAPE and moisture convergence, whereas nocturnal lightning over foothills is linked to persistent CAPE and downslope (katabatic) wind convergence. Cloud base height (CBH) further supports this spatial and temporal convection shift. Correlation analyses show strong FD–CAPE–humidity relationships in the foothills that weaken with elevation, suggesting increasing influence of orographic and microphysical processes aloft. The ratio of negative to positive CG flashes increases with elevation, likely influenced by CBH. Vertical profiles of ice water content and vertical velocity reinforce a clear transition from nocturnal convection in the southern plains to thermally and topographically driven daytime convection in the northern mountains. This study highlights how terrain modulates convective regimes and lightning variability across Nepal.
{"title":"Spatio-temporal variations of lightning activity over Nepal's complex terrain: Links to altitude and meteorological factors","authors":"Pradip Karki , Shriram Sharma , Rupraj Biswasharma , Sunil D. Pawar , V. Gopalkrishnan , Madhu Gyawali , Khem Narayan Poudyal","doi":"10.1016/j.jastp.2025.106615","DOIUrl":"10.1016/j.jastp.2025.106615","url":null,"abstract":"<div><div>This study investigates the diurnal and seasonal variations of lightning activity over the complex terrain of Nepal, which spans elevations from 59 m to 8848 m above sea level. Despite lightning being a leading weather-related hazard in Nepal, causing over 100 fatalities annually, the altitudinal distribution of lightning has received limited attention. We use lightning data from the GLD360 network and meteorological parameters from the ERA5 reanalysis to analyze the influence of Convective Available Potential Energy (CAPE), wind, temperature, humidity, and cloud structure on lightning activity. Results reveal a strong altitudinal gradient in lightning flash density (FD), with maxima in the southern foothills and significant decrease toward higher elevations. Temporally, lightning exhibits two prominent peaks: one in the afternoon and the other around midnight. Afternoon lightning in the higher elevations is associated with surface heating, upslope winds, increased CAPE and moisture convergence, whereas nocturnal lightning over foothills is linked to persistent CAPE and downslope (katabatic) wind convergence. Cloud base height (CBH) further supports this spatial and temporal convection shift. Correlation analyses show strong FD–CAPE–humidity relationships in the foothills that weaken with elevation, suggesting increasing influence of orographic and microphysical processes aloft. The ratio of negative to positive CG flashes increases with elevation, likely influenced by CBH. Vertical profiles of ice water content and vertical velocity reinforce a clear transition from nocturnal convection in the southern plains to thermally and topographically driven daytime convection in the northern mountains. This study highlights how terrain modulates convective regimes and lightning variability across Nepal.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106615"},"PeriodicalIF":1.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097854","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 : 2025-08-23DOI: 10.1016/j.jastp.2025.106614
Fan Wu , Congming Dai , Cong Zhang , Wentao Lian , Shunping Chen , Heli Wei
Auroral disturbances can significantly enhance atmospheric limb infrared radiance in near space by several orders of magnitude, impacting space-based systems. This study used infrared radiance data from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument onboard the Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) satellite during the 2003 “Halloween Storm” to estimate the accuracy of the Strategic High-Altitude Radiance Code (SHARC) model under both quiet and auroral conditions. The average relative error of the SHARC simulations compared to the SABER measurements ranged from 11.8 to 19.4 %, indicating a reliable agreement with the observations. Due to the limited temporal resolution of satellite observations at fixed locations, SHARC was further used to simulate hourly variations in 4.3 and 5.3 μm infrared radiance over high-latitude regions (50–200 km) during the storm. The results showed that auroral disturbances enhanced the 4.3 μm radiance by up to two orders of magnitude, particularly above 130 km during the day and above 110 km at night. The 5.3 μm radiance was also enhanced, with peak increases of about one order of magnitude near 120 km. The study also analyzed the temporal evolution of key excited-state species (NO, CO2, and NO+), identifying their respective roles in infrared enhancement. Finally, the uncertainties in the SHARC simulations and SABER measurements were discussed, confirming the applicability of SHARC under auroral conditions and clarifying the differing enhancement mechanisms of the two bands.
{"title":"Numerical simulation study on high temporal resolution variation characteristics of atmospheric limb infrared radiation under aurora disturbance","authors":"Fan Wu , Congming Dai , Cong Zhang , Wentao Lian , Shunping Chen , Heli Wei","doi":"10.1016/j.jastp.2025.106614","DOIUrl":"10.1016/j.jastp.2025.106614","url":null,"abstract":"<div><div>Auroral disturbances can significantly enhance atmospheric limb infrared radiance in near space by several orders of magnitude, impacting space-based systems. This study used infrared radiance data from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument onboard the Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) satellite during the 2003 “Halloween Storm” to estimate the accuracy of the Strategic High-Altitude Radiance Code (SHARC) model under both quiet and auroral conditions. The average relative error of the SHARC simulations compared to the SABER measurements ranged from 11.8 to 19.4 %, indicating a reliable agreement with the observations. Due to the limited temporal resolution of satellite observations at fixed locations, SHARC was further used to simulate hourly variations in 4.3 and 5.3 μm infrared radiance over high-latitude regions (50–200 km) during the storm. The results showed that auroral disturbances enhanced the 4.3 μm radiance by up to two orders of magnitude, particularly above 130 km during the day and above 110 km at night. The 5.3 μm radiance was also enhanced, with peak increases of about one order of magnitude near 120 km. The study also analyzed the temporal evolution of key excited-state species (NO, CO<sub>2</sub>, and NO<sup>+</sup>), identifying their respective roles in infrared enhancement. Finally, the uncertainties in the SHARC simulations and SABER measurements were discussed, confirming the applicability of SHARC under auroral conditions and clarifying the differing enhancement mechanisms of the two bands.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"276 ","pages":"Article 106614"},"PeriodicalIF":1.9,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908150","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 : 2025-08-21DOI: 10.1016/j.jastp.2025.106613
Pawan S. Soyam , Pramod D. Safai , Shivdas Bankar , Kiran Todekar , Neelam Malap , Sunil Kondle , Pradeep Zambare , Mahendra Mane , Sanjay S. Kale , Thara Prabhakaran
This research article investigates the diurnal and seasonal characteristics of aerosol optical properties such as absorbing and scattering coefficients and single scattering albedo (SSA) over the semi-arid region of Solapur (17°43′45.4332″ N, 75°.51′24.4476″ E, 457 m AMSL), India. These aerosol optical properties play a crucial role in understanding the aerosol radiative forcing and thereby the climate impact of atmospheric aerosols. However, there is a lack of comprehensive studies focusing on the temporal dynamics of aerosol optical properties in peninsular India especially from the arid region. To address this gap, we conducted multiyear (July 2019 to October 2022) measurement campaign to characterize the aerosol optical properties at Solapur. Our findings reveal significant temporal variations in the aerosol optical properties on the diurnal, seasonal, and annual scales. Diurnally, the aerosol optical properties showed distinct patterns, with higher absorption and scattering coefficients during daytime due to increased anthropogenic activities and local emissions. Seasonally, the aerosol loading was influenced by changes in the prevailing meteorological conditions, including aerosol scavenging due to the monsoonal influences and regional transport of aerosols. The intra-annual variability suggested the possible influence of various factors such as changes in local emissions, land use patterns, and local boundary layer conditions. Furthermore, the SSA values showed slight annual variation, with maximum values in post monsoon and minimum during monsoon period. SSA value (mean 0.71 ± 0.02 and median 0.73 ± 0.03) indicated towards significant role of absorbing aerosols in aerosol radiative forcing and thereby atmospheric warming over this semi-arid location.
本文研究了印度Solapur（17°43′45.4332″N, 75°0.51′24.4476″E, 457 m AMSL）半干旱区气溶胶吸收散射系数和单次散射反照率（SSA）等光学特性的日变化特征和季节特征。这些气溶胶光学特性在理解气溶胶辐射强迫和大气气溶胶对气候的影响方面起着至关重要的作用。然而，对于印度半岛特别是干旱地区气溶胶光学特性的时间动态，目前还缺乏全面的研究。为了解决这一差距，我们进行了多年（2019年7月至2022年10月）的测量活动，以表征Solapur的气溶胶光学特性。我们的发现揭示了气溶胶光学特性在日、季节和年尺度上的显著时间变化。日变化过程中，气溶胶光学特性表现出明显的变化规律，由于人为活动和局地排放的增加，白天气溶胶的吸收和散射系数较高。在季节上，气溶胶负荷受主要气象条件变化的影响，包括季风影响的气溶胶清除和气溶胶的区域运输。年际变率反映了局地排放、土地利用模式和局地边界层条件变化等因素的可能影响。此外，SSA值的年际变化不大，季风后最大，季风期最小。SSA值（平均值0.71±0.02，中位数0.73±0.03）表明，在该半干旱地区，气溶胶的吸收在气溶胶辐射强迫中起着重要作用，从而导致大气变暖。
{"title":"Absorbing and scattering aerosols over semi-arid region in India: Temporal variation and possible sources","authors":"Pawan S. Soyam , Pramod D. Safai , Shivdas Bankar , Kiran Todekar , Neelam Malap , Sunil Kondle , Pradeep Zambare , Mahendra Mane , Sanjay S. Kale , Thara Prabhakaran","doi":"10.1016/j.jastp.2025.106613","DOIUrl":"10.1016/j.jastp.2025.106613","url":null,"abstract":"<div><div>This research article investigates the diurnal and seasonal characteristics of aerosol optical properties such as absorbing and scattering coefficients and single scattering albedo (SSA) over the semi-arid region of Solapur (17°43′45.4332″ N, 75°.51′24.4476″ E, 457 m AMSL), India. These aerosol optical properties play a crucial role in understanding the aerosol radiative forcing and thereby the climate impact of atmospheric aerosols. However, there is a lack of comprehensive studies focusing on the temporal dynamics of aerosol optical properties in peninsular India especially from the arid region. To address this gap, we conducted multiyear (July 2019 to October 2022) measurement campaign to characterize the aerosol optical properties at Solapur. Our findings reveal significant temporal variations in the aerosol optical properties on the diurnal, seasonal, and annual scales. Diurnally, the aerosol optical properties showed distinct patterns, with higher absorption and scattering coefficients during daytime due to increased anthropogenic activities and local emissions. Seasonally, the aerosol loading was influenced by changes in the prevailing meteorological conditions, including aerosol scavenging due to the monsoonal influences and regional transport of aerosols. The intra-annual variability suggested the possible influence of various factors such as changes in local emissions, land use patterns, and local boundary layer conditions. Furthermore, the SSA values showed slight annual variation, with maximum values in post monsoon and minimum during monsoon period. SSA value (mean 0.71 ± 0.02 and median 0.73 ± 0.03) indicated towards significant role of absorbing aerosols in aerosol radiative forcing and thereby atmospheric warming over this semi-arid location.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"275 ","pages":"Article 106613"},"PeriodicalIF":1.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892807","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 : 2025-08-19DOI: 10.1016/j.jastp.2025.106605
Madhan Veeramani, Sudhakar M.S.
Novel contributions engaging white and black-box models for Sunspot detection neglect optimization which is highly essential in solar activity monitoring. This demanded the quest for automated mathematical programming methods, warranting accurate forecasting of solar activity. Accordingly, in this paper, an energy-optimized adaptive curve fitting sunspot detection model coined Sunspot Attribute Extraction via Energy-tuned Minimax Optimization (SAEEMO) is introduced for characterizing diverse solar constituents followed by feature extraction from full disk images. SAEEMO initially considers the overall image energy as the sum of energies concerned with the solar disk to distinguish the solar variations that are intensity enhanced, facilitating segmentation. Later, the Sunspots are further disintegrated into umbra and penumbra, characterized as edge sensitivity and regularization terms, and adaptively thresholded using the novel energy-based Minimax optimizer. The adopted energy function packs the intensity variations within the Minimax bounds to render a globally optimal solution in an iterative manner using the line search operation. SAEEMO’s Receiver Operating Characteristics (ROC) investigation on Helioseismic and Magnetic Imager (HMI) continuum images obtained from Solar Dynamics Observatory (SDO) reveals its preservative and distinctive nature in detecting and characterizing diverse solar features. Also, relative investigations of the extracted solar features with the catalogs of Debrecen Photoheliographic Data (DPD), Heliophysics Feature Catalog (HFC), Sunspot Index and Long-term Solar Observations (SILSO), and Space Weather Prediction Center (SWPC) demonstrate SAEEMO’s consistency.
{"title":"Automatic Detection of Sunspots on full-disk continuum images using the MiniMax Optimization and Feature Extraction","authors":"Madhan Veeramani, Sudhakar M.S.","doi":"10.1016/j.jastp.2025.106605","DOIUrl":"10.1016/j.jastp.2025.106605","url":null,"abstract":"<div><div>Novel contributions engaging white and black-box models for Sunspot detection neglect optimization which is highly essential in solar activity monitoring. This demanded the quest for automated mathematical programming methods, warranting accurate forecasting of solar activity. Accordingly, in this paper, an energy-optimized adaptive curve fitting sunspot detection model coined Sunspot Attribute Extraction via Energy-tuned Minimax Optimization (SAEEMO) is introduced for characterizing diverse solar constituents followed by feature extraction from full disk images. SAEEMO initially considers the overall image energy as the sum of energies concerned with the solar disk to distinguish the solar variations that are intensity enhanced, facilitating segmentation. Later, the Sunspots are further disintegrated into umbra and penumbra, characterized as edge sensitivity and regularization terms, and adaptively thresholded using the novel energy-based Minimax optimizer. The adopted energy function packs the intensity variations within the Minimax bounds to render a globally optimal solution in an iterative manner using the line search operation. SAEEMO’s Receiver Operating Characteristics (ROC) investigation on Helioseismic and Magnetic Imager (HMI) continuum images obtained from Solar Dynamics Observatory (SDO) reveals its preservative and distinctive nature in detecting and characterizing diverse solar features. Also, relative investigations of the extracted solar features with the catalogs of Debrecen Photoheliographic Data (DPD), Heliophysics Feature Catalog (HFC), Sunspot Index and Long-term Solar Observations (SILSO), and Space Weather Prediction Center (SWPC) demonstrate SAEEMO’s consistency.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"275 ","pages":"Article 106605"},"PeriodicalIF":1.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879497","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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