Pub Date : 2025-10-10DOI: 10.1016/j.jastp.2025.106660
Y.P. Singh , B. Badruddin
Wavelet and Lomb-Scargle periodogram analyses are applied to the data on coronal mass ejections, high-speed streams, corotating interaction regions, and interplanetary shock events from 1996 to 2023 to determine how frequently they occur. This study aims to ascertain the short-term fluctuations in the discrete time series of these structures. The Lomb-Scargle periodogram and wavelet analysis reveal important and clear patterns in how often these events occur, in addition to the regular solar rotation and longer solar cycles. The results show a clear pattern that repeats roughly every 22 days in all interplanetary structures, and a more noticeable pattern repeats about every 44 days, mainly seen in the data for interplanetary coronal mass ejections, high-speed streams, and interplanetary shock events. The 44.0 day period is prominent and significantly appears in all the time series. This fluctuation could be a multiple of ∼23.0 days, and the latter could be a multiple of ∼12 days.
{"title":"Short-term periods in the occurrence of ICME, HSS, CIR, and IP shock events in interplanetary space","authors":"Y.P. Singh , B. Badruddin","doi":"10.1016/j.jastp.2025.106660","DOIUrl":"10.1016/j.jastp.2025.106660","url":null,"abstract":"<div><div>Wavelet and Lomb-Scargle periodogram analyses are applied to the data on coronal mass ejections, high-speed streams, corotating interaction regions, and interplanetary shock events from 1996 to 2023 to determine how frequently they occur. This study aims to ascertain the short-term fluctuations in the discrete time series of these structures. The Lomb-Scargle periodogram and wavelet analysis reveal important and clear patterns in how often these events occur, in addition to the regular solar rotation and longer solar cycles. The results show a clear pattern that repeats roughly every 22 days in all interplanetary structures, and a more noticeable pattern repeats about every 44 days, mainly seen in the data for interplanetary coronal mass ejections, high-speed streams, and interplanetary shock events. The 44.0 day period is prominent and significantly appears in all the time series. This fluctuation could be a multiple of ∼23.0 days, and the latter could be a multiple of ∼12 days.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106660"},"PeriodicalIF":1.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324935","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 : 2025-10-09DOI: 10.1016/j.jastp.2025.106654
Omkar M. Patil , Debarchan Kar , Navin Parihar , Rajesh Singh , A.P. Dimri
The study investigates Super Cyclone (SuCS) Amphan, which occurred over the Bay of Bengal (BoB) during 16–21 May 2020 as a possible source of lower ionospheric perturbations. INSAT-3D satellite observations confirmed intense convective activity through low Cloud Top Brightness Temperature (∼–80 °C) and suppressed Outgoing Longwave Radiation (below 100 W/m2). Lightning analysis indicated an increase in activity within 500 km of the cyclone center, with cloud-to-cloud (CC) lightning intensifying in the eyewall during the cyclone's peak. Over 60 % of positive CC flashes exceeded 10 kA, highlighting strong convective electrical dynamics. Temperature perturbations observed by the AIRS instrument onboard NASA's Aqua satellite appeared as concentric wave patterns at stratospheric altitudes, indicating atmospheric gravity waves (AGWs) activity in the northeast of the storm center. SABER temperature profiles further revealed enhanced wave amplitudes from May 17–20, confirming AGW propagation into the mesosphere-lower thermosphere (MLT) region. Nightglow emissions observed by the Suomi-NPP/DNB sensor provided additional evidence of concentric gravity waves at the MLT heights. This enhanced AGW activity coincided with Amphan's intensification. This multi-altitude observational analysis highlights the role of intense convection and lightning in AGW generation and their subsequent influence on upper atmospheric dynamics. The observations confirm that tropical cyclones serve as a source of lower ionospheric disturbances through AGW-driven energy and momentum deposition.
{"title":"Tropical cyclones as a possible source of lower ionosphere (thermosphere) perturbation: a case study of Amphan Super Cyclone (SuCS) over Bay of Bengal","authors":"Omkar M. Patil , Debarchan Kar , Navin Parihar , Rajesh Singh , A.P. Dimri","doi":"10.1016/j.jastp.2025.106654","DOIUrl":"10.1016/j.jastp.2025.106654","url":null,"abstract":"<div><div>The study investigates Super Cyclone (SuCS) Amphan, which occurred over the Bay of Bengal (BoB) during 16–21 May 2020 as a possible source of lower ionospheric perturbations. INSAT-3D satellite observations confirmed intense convective activity through low Cloud Top Brightness Temperature (∼–80 °C) and suppressed Outgoing Longwave Radiation (below 100 W/m<sup>2</sup>). Lightning analysis indicated an increase in activity within 500 km of the cyclone center, with cloud-to-cloud (CC) lightning intensifying in the eyewall during the cyclone's peak. Over 60 % of positive CC flashes exceeded 10 kA, highlighting strong convective electrical dynamics. Temperature perturbations observed by the AIRS instrument onboard NASA's Aqua satellite appeared as concentric wave patterns at stratospheric altitudes, indicating atmospheric gravity waves (AGWs) activity in the northeast of the storm center. SABER temperature profiles further revealed enhanced wave amplitudes from May 17–20, confirming AGW propagation into the mesosphere-lower thermosphere (MLT) region. Nightglow emissions observed by the Suomi-NPP/DNB sensor provided additional evidence of concentric gravity waves at the MLT heights. This enhanced AGW activity coincided with Amphan's intensification. This multi-altitude observational analysis highlights the role of intense convection and lightning in AGW generation and their subsequent influence on upper atmospheric dynamics. The observations confirm that tropical cyclones serve as a source of lower ionospheric disturbances through AGW-driven energy and momentum deposition.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106654"},"PeriodicalIF":1.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324937","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 : 2025-10-09DOI: 10.1016/j.jastp.2025.106652
Luciano de Souza Maria , Luis Miguel da Costa , Marcelo Odorizzi Campos , Alan Rodrigo Panosso , Luana Santamaria Basso , Carlos Antonio da Silva Junior , Newton La Scala Jr.
Forest fires represent a significant threat to the Amazon biome, contributing to elevated greenhouse gases (GHGs) emissions. This study analyzes temporal trends in column-averaged concentrations of CO2 (XCO2) and CH4 (XCH4) in the atmosphere using satellite observations from GOSAT, while exploring the relationships between surface spectral indices and wildfire activity in the region. Key variables included the Enhanced Vegetation Index (EVI), Fire Radiative Power (FRP), and MODIS-based fire foci data. Additionally, Sun-Induced Fluorescence (SIF), XCH4, and XCO2 data were obtained from GOSAT for the period 2009–2019, alongside global forest fire emissions from the VIIRS-based Fire Emission Inventory (VFEI) for 2012–2019. Our findings indicate significant increases in monthly averages of XCO2 (>1.3 ppm) starting in April 2013, identified as a change point using the Pettitt test. Similarly, FRP values increased for April (>12 MW) and July (>23 MW), suggesting an earlier onset of wildfire activity and subsequent atmospheric impacts. These results align with VFEI emissions data, which also show rising GHG levels during the study period. The observed increases in wildfires and associated XCO2 concentrations are likely linked to anthropogenic activities, particularly land-use changes, underscoring the critical role of human influence in exacerbating GHGs emissions within the Amazon biome. These findings highlight the urgent need for sustainable land management strategies to mitigate the adverse impacts of wildfires and preserve the biome's role as a vital carbon sink.
{"title":"Confronting the Amazon's fire crisis: Evidence of early fire occurrence and increased atmospheric gases from the GOSAT satellite","authors":"Luciano de Souza Maria , Luis Miguel da Costa , Marcelo Odorizzi Campos , Alan Rodrigo Panosso , Luana Santamaria Basso , Carlos Antonio da Silva Junior , Newton La Scala Jr.","doi":"10.1016/j.jastp.2025.106652","DOIUrl":"10.1016/j.jastp.2025.106652","url":null,"abstract":"<div><div>Forest fires represent a significant threat to the Amazon biome, contributing to elevated greenhouse gases (GHGs) emissions. This study analyzes temporal trends in column-averaged concentrations of CO<sub>2</sub> (XCO<sub>2</sub>) and CH<sub>4</sub> (XCH<sub>4</sub>) in the atmosphere using satellite observations from GOSAT, while exploring the relationships between surface spectral indices and wildfire activity in the region. Key variables included the Enhanced Vegetation Index (EVI), Fire Radiative Power (FRP), and MODIS-based fire foci data. Additionally, Sun-Induced Fluorescence (SIF), XCH<sub>4</sub>, and XCO<sub>2</sub> data were obtained from GOSAT for the period 2009–2019, alongside global forest fire emissions from the VIIRS-based Fire Emission Inventory (VFEI) for 2012–2019. Our findings indicate significant increases in monthly averages of XCO<sub>2</sub> (>1.3 ppm) starting in April 2013, identified as a change point using the Pettitt test. Similarly, FRP values increased for April (>12 MW) and July (>23 MW), suggesting an earlier onset of wildfire activity and subsequent atmospheric impacts. These results align with VFEI emissions data, which also show rising GHG levels during the study period. The observed increases in wildfires and associated XCO<sub>2</sub> concentrations are likely linked to anthropogenic activities, particularly land-use changes, underscoring the critical role of human influence in exacerbating GHGs emissions within the Amazon biome. These findings highlight the urgent need for sustainable land management strategies to mitigate the adverse impacts of wildfires and preserve the biome's role as a vital carbon sink.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106652"},"PeriodicalIF":1.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269014","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}
Accurate estimation of reference evapotranspiration (ET0) is essential for effective irrigation scheduling and water resource management, particularly in data-scarce regions such as India, which lack advanced automatic meteorological stations. The present study developed a hybrid model (DNN-GWO) and conducted an in-depth evaluation against standalone data-driven models, including Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Deep Belief Network (DBN) for forecasting monthly ET0 in the Upper Ganga canal command region, Uttar Pradesh, India. Three input scenarios were evaluated for their correlation to ET0 estimation. The results revealed that the DNN model showed the best performance in all three scenarios, achieving R2 = 0.958, RMSE = 0.076 mm/day, NSE = 0.954, RMSLE = 0.024, MAE = 0.055, MBE = 0.012, MSRE = 0.032, and EVS = 0.987 with solar radiation (Rs), wind speed (U), maximum temperature (Tmax), minimum temperature (Tmin), and relative humidity (RH) as inputs. The developed hybrid DNN-GWO model further improved predictive accuracy, with R2 = 0.992, RMSE = 0.0317 mm/day, NSE = 0.99, RMSLE = 0.023, MAE = 0.054, MBE = 0.018, and EVS = 0.992, reducing RMSE by nearly 60 % compared to the best-performing standalone DNN. SHapley Additive explanations (SHAP) analysis revealed that temperature and solar radiation were the most influential predictors of ET0, while the model also provided stable predictions across different input scenarios, demonstrating robustness in data-limited conditions. The developed hybrid framework, by combining deep learning, swarm intelligence, and explainability, provides a robust, accurate, and interpretable solution for agricultural water management in data-constrained environments.
{"title":"Stacked hybridization of deep learning model with grey wolf optimization for accurate and explainable reference evapotranspiration","authors":"Truptimayee Suna , Bibhuti Bhusan Sahoo , Dipali Pawar , Nand Lal Kushwaha , Pradosh Kumar Paramaguru , P.S. Brahmanand , Himani Bisht","doi":"10.1016/j.jastp.2025.106655","DOIUrl":"10.1016/j.jastp.2025.106655","url":null,"abstract":"<div><div>Accurate estimation of reference evapotranspiration (ET<sub>0</sub>) is essential for effective irrigation scheduling and water resource management, particularly in data-scarce regions such as India, which lack advanced automatic meteorological stations. The present study developed a hybrid model (DNN-GWO) and conducted an in-depth evaluation against standalone data-driven models, including Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Deep Belief Network (DBN) for forecasting monthly ET<sub>0</sub> in the Upper Ganga canal command region, Uttar Pradesh, India. Three input scenarios were evaluated for their correlation to ET<sub>0</sub> estimation. The results revealed that the DNN model showed the best performance in all three scenarios, achieving R<sup>2</sup> = 0.958, RMSE = 0.076 mm/day, NSE = 0.954, RMSLE = 0.024, MAE = 0.055, MBE = 0.012, MSRE = 0.032, and EVS = 0.987 with solar radiation (Rs), wind speed (U), maximum temperature (Tmax), minimum temperature (Tmin), and relative humidity (RH) as inputs. The developed hybrid DNN-GWO model further improved predictive accuracy, with R<sup>2</sup> = 0.992, RMSE = 0.0317 mm/day, NSE = 0.99, RMSLE = 0.023, MAE = 0.054, MBE = 0.018, and EVS = 0.992, reducing RMSE by nearly 60 % compared to the best-performing standalone DNN. SHapley Additive explanations (SHAP) analysis revealed that temperature and solar radiation were the most influential predictors of ET<sub>0</sub>, while the model also provided stable predictions across different input scenarios, demonstrating robustness in data-limited conditions. The developed hybrid framework, by combining deep learning, swarm intelligence, and explainability, provides a robust, accurate, and interpretable solution for agricultural water management in data-constrained environments.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106655"},"PeriodicalIF":1.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324924","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 : 2025-10-06DOI: 10.1016/j.jastp.2025.106651
Mutambi Songa , Amaury de Souza , José Francisco de Oliveira-Júnior , Kelvy Rosalvo Alencar Cardoso , Sneha Gautam
The Total Ozone Column (TCO) is a key atmospheric indicator for shielding against ultraviolet (UV) radiation. Although global recovery of the ozone layer has been underway since the Montreal Protocol, uncertainties persist in tropical regions. This study analyzes the spatio-temporal variability and trends of TCO in three major Brazilian biomes—Cerrado, Pantanal, and Atlantic Forest—located in Mato Grosso do Sul (MS), during the period 2005–2020. Satellite data from the OMI/Aura Level-3 product (NASA) with 1° × 1° resolution were processed into monthly, seasonal, and annual averages, applying a 13-month centered moving average for trend visualization. Trend detection was carried out using linear regression and the Mann–Kendall test. Results showed that the Cerrado had the highest average TCO (346.66 DU) and the only statistically significant positive trend (+0.031 DU.year−1, p-value = 0.012), while the Pantanal and Atlantic Forest exhibited lower averages (∼261 DU) and occasional drops below the 260 DU threshold established by the World Meteorological Organization (WMO). A marked seasonality was identified, with maxima between September and October (spring) and minima between January and March (summer). Interannual variability was strongly influenced by El Niño events and biomass burning. These findings highlight that TCO variability is shaped by the interaction of global atmospheric circulation and local anthropogenic pressures, emphasizing the need for biome-specific monitoring and public policies to reduce UV exposure and adapt to climate change, in alignment with the Montreal Protocol and the 2030 Agenda.
{"title":"Intraseasonal and interannual variability of the Total Ozone Column (TCO) in Brazilian biomes: An analysis of the multibiome trend in Mato Grosso do Sul - Brazil","authors":"Mutambi Songa , Amaury de Souza , José Francisco de Oliveira-Júnior , Kelvy Rosalvo Alencar Cardoso , Sneha Gautam","doi":"10.1016/j.jastp.2025.106651","DOIUrl":"10.1016/j.jastp.2025.106651","url":null,"abstract":"<div><div>The Total Ozone Column (TCO) is a key atmospheric indicator for shielding against ultraviolet (UV) radiation. Although global recovery of the ozone layer has been underway since the Montreal Protocol, uncertainties persist in tropical regions. This study analyzes the spatio-temporal variability and trends of TCO in three major Brazilian biomes—Cerrado, Pantanal, and Atlantic Forest—located in Mato Grosso do Sul (MS), during the period 2005–2020. Satellite data from the OMI/Aura Level-3 product (NASA) with 1° × 1° resolution were processed into monthly, seasonal, and annual averages, applying a 13-month centered moving average for trend visualization. Trend detection was carried out using linear regression and the Mann–Kendall test. Results showed that the Cerrado had the highest average TCO (346.66 DU) and the only statistically significant positive trend (+0.031 DU.year<sup>−1</sup>, p-value = 0.012), while the Pantanal and Atlantic Forest exhibited lower averages (∼261 DU) and occasional drops below the 260 DU threshold established by the World Meteorological Organization (WMO). A marked seasonality was identified, with maxima between September and October (spring) and minima between January and March (summer). Interannual variability was strongly influenced by El Niño events and biomass burning. These findings highlight that TCO variability is shaped by the interaction of global atmospheric circulation and local anthropogenic pressures, emphasizing the need for biome-specific monitoring and public policies to reduce UV exposure and adapt to climate change, in alignment with the Montreal Protocol and the 2030 Agenda.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106651"},"PeriodicalIF":1.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269012","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 : 2025-10-02DOI: 10.1016/j.jastp.2025.106650
Shanshan Wu , Jing Zheng , Haibo Zou , Hai Wang , Lizhi Tao
ECMWF ERA5 and NCEP FNL are the two most commonly used datasets to drive WRF model, and the former has relatively higher spatiotemporal resolution than the latter. In order to explore the performances of these two datasets driving WRF model to conduct sensitivity experiments of small-scale underlying surface changes, four experiments are designed for the convective event near Poyang Lake (PL) in China on May 4, 2020. These experiments are ERA-Water (ERA5-driven, including the water body of PL), ERA-Cropland (ERA5-driven, the water body of PL is replaced by cropland), FNL-Water (FNL-driven, including the water body of PL) and FNL-Cropland (FNL-driven, the water body of PL is replaced by cropland), respectively. Simulation results show that ERA-Water and FNL-Water successfully reproduce this convection, while ERA-Cropland and FNL-Cropland don't simulate it, reflecting the role of PL in the convection development. However, the simulations of ERA-Cropland and FNL-Cropland differ greatly, and the former simulates a strong convection in the middle and east of the north shore of PL, while the latter did not simulate a convection. Diagnostic analysis indicates that the strong convection in ERA-Cropland is false, and is mainly caused by the mismatch between the high-resolution ERA5 and the changed underlying surface information. This suggests that when conducting sensitivity experiments of small-scale underlying surface changes, it may be more appropriate to use NCEP FNL than ECMWF ERA5.
{"title":"The influence of ERA5 and FNL driven WRF on sensitivity experiments of small-scale underlying surface changes: a case study of poyang lake in China","authors":"Shanshan Wu , Jing Zheng , Haibo Zou , Hai Wang , Lizhi Tao","doi":"10.1016/j.jastp.2025.106650","DOIUrl":"10.1016/j.jastp.2025.106650","url":null,"abstract":"<div><div>ECMWF ERA5 and NCEP FNL are the two most commonly used datasets to drive WRF model, and the former has relatively higher spatiotemporal resolution than the latter. In order to explore the performances of these two datasets driving WRF model to conduct sensitivity experiments of small-scale underlying surface changes, four experiments are designed for the convective event near Poyang Lake (PL) in China on May 4, 2020. These experiments are ERA-Water (ERA5-driven, including the water body of PL), ERA-Cropland (ERA5-driven, the water body of PL is replaced by cropland), FNL-Water (FNL-driven, including the water body of PL) and FNL-Cropland (FNL-driven, the water body of PL is replaced by cropland), respectively. Simulation results show that ERA-Water and FNL-Water successfully reproduce this convection, while ERA-Cropland and FNL-Cropland don't simulate it, reflecting the role of PL in the convection development. However, the simulations of ERA-Cropland and FNL-Cropland differ greatly, and the former simulates a strong convection in the middle and east of the north shore of PL, while the latter did not simulate a convection. Diagnostic analysis indicates that the strong convection in ERA-Cropland is false, and is mainly caused by the mismatch between the high-resolution ERA5 and the changed underlying surface information. This suggests that when conducting sensitivity experiments of small-scale underlying surface changes, it may be more appropriate to use NCEP FNL than ECMWF ERA5.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106650"},"PeriodicalIF":1.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269013","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 : 2025-09-27DOI: 10.1016/j.jastp.2025.106644
Oluwasegun M. Adebayo , Esfhan A. Kherani , Alexandre A. Pimenta , Babatunde Rabiu
Predicting natural disasters such as earthquakes remains a major challenge in geosciences, with critical implications for early warning systems and disaster risk reduction. Among various precursory signals, ionospheric anomalies have gained increasing attention as potential indicators of impending seismic events. In this study, we examine pre-seismic ionospheric disturbances (PIDs) associated with the Mw 7.5 Northern Peru earthquake on November 28, 2021, using Global Navigation Satellite System (GNSS) Total Electron Content (TEC) and ground vertical velocity data. Significant sequential negative TEC anomalies were observed up to two hours prior to the mainshock, with multiple disturbances recorded by several GNSS receivers. The amplitudes of these disturbances increased as the earthquake approached, suggesting a progressive ionospheric response to the buildup of tectonic stress. Spectral analysis using the Short-Time Fourier Transform revealed center frequencies between 3.63 mHz and 4.80 mHz — within the acoustic/infrasonic range — indicating that such waves, possibly generated by foreshocks, may be responsible for these PIDs. To rule out other sources of TEC anomalies, we examined the TEC data for the previous day along the same trajectories and found no similar disturbances. Furthermore, geomagnetic conditions were quiet during the period, as indicated by Kp and Dst indexes two days before and after the earthquake. These findings contribute to the understanding of seismo-ionospheric coupling and highlight the potential role of ionospheric monitoring as a complementary approach to conventional seismic methods in earthquake early warning systems.
{"title":"Pre-seismic ionospheric disturbances (PIDs) associated With 2021 Mw 7.5 Northern Peru earthquake: GNSS and ground uplift observations","authors":"Oluwasegun M. Adebayo , Esfhan A. Kherani , Alexandre A. Pimenta , Babatunde Rabiu","doi":"10.1016/j.jastp.2025.106644","DOIUrl":"10.1016/j.jastp.2025.106644","url":null,"abstract":"<div><div>Predicting natural disasters such as earthquakes remains a major challenge in geosciences, with critical implications for early warning systems and disaster risk reduction. Among various precursory signals, ionospheric anomalies have gained increasing attention as potential indicators of impending seismic events. In this study, we examine pre-seismic ionospheric disturbances (PIDs) associated with the Mw 7.5 Northern Peru earthquake on November 28, 2021, using Global Navigation Satellite System (GNSS) Total Electron Content (TEC) and ground vertical velocity data. Significant sequential negative TEC anomalies were observed up to two hours prior to the mainshock, with multiple disturbances recorded by several GNSS receivers. The amplitudes of these disturbances increased as the earthquake approached, suggesting a progressive ionospheric response to the buildup of tectonic stress. Spectral analysis using the Short-Time Fourier Transform revealed center frequencies between 3.63 mHz and 4.80 mHz — within the acoustic/infrasonic range — indicating that such waves, possibly generated by foreshocks, may be responsible for these PIDs. To rule out other sources of TEC anomalies, we examined the TEC data for the previous day along the same trajectories and found no similar disturbances. Furthermore, geomagnetic conditions were quiet during the period, as indicated by Kp and Dst indexes two days before and after the earthquake. These findings contribute to the understanding of seismo-ionospheric coupling and highlight the potential role of ionospheric monitoring as a complementary approach to conventional seismic methods in earthquake early warning systems.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106644"},"PeriodicalIF":1.9,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221899","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}
Remote sensing of atmospheric water vapor using Global Navigation Satellite System (GNSS) signals has become an important technique in meteorology, weather forecasting, and climate research. This study investigated regional atmospheric variability over Tashkent, Uzbekistan, by analyzing ten key atmospheric parameters from the ERA5 reanalysis and retrieving precipitable water vapor (PW) from GNSS-derived tropospheric delay data. The analysis covered the period from 12 to 22 February 2025 (day of year 43–53), using ground-based observations from the Tashkent (TASH) and Maidantal (MTAL) GNSS stations. The primary aim was to enhance the characterization of regional atmospheric dynamics and to evaluate the potential of GNSS-derived PW for improving precipitation forecasting when combined with reanalysis data. The results revealed a strong correlation between GNSS-derived and ERA5-derived PW values, indicating that GNSS tropospheric delay observations reliably capture short-term variations in atmospheric water vapor. These findings confirm the utility of integrating GNSS retrievals with reanalysis products for high-resolution monitoring of atmospheric processes in Central Asia.
{"title":"Estimation of ERA5 tropospheric parameters using GNSS data over Tashkent","authors":"H.E. Eshkuvatov , Sh.N. Mardonov , O.V. Xudoynazarov , Z.J. Ruziev , Sh.Sh. Numonjonov , J.R. Hoshimov , F.X. Asatullayev , I.M. Egamberdiev , M.A. Musurmonov","doi":"10.1016/j.jastp.2025.106648","DOIUrl":"10.1016/j.jastp.2025.106648","url":null,"abstract":"<div><div>Remote sensing of atmospheric water vapor using Global Navigation Satellite System (GNSS) signals has become an important technique in meteorology, weather forecasting, and climate research. This study investigated regional atmospheric variability over Tashkent, Uzbekistan, by analyzing ten key atmospheric parameters from the ERA5 reanalysis and retrieving precipitable water vapor (PW) from GNSS-derived tropospheric delay data. The analysis covered the period from 12 to 22 February 2025 (day of year 43–53), using ground-based observations from the Tashkent (TASH) and Maidantal (MTAL) GNSS stations. The primary aim was to enhance the characterization of regional atmospheric dynamics and to evaluate the potential of GNSS-derived PW for improving precipitation forecasting when combined with reanalysis data. The results revealed a strong correlation between GNSS-derived and ERA5-derived PW values, indicating that GNSS tropospheric delay observations reliably capture short-term variations in atmospheric water vapor. These findings confirm the utility of integrating GNSS retrievals with reanalysis products for high-resolution monitoring of atmospheric processes in Central Asia.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106648"},"PeriodicalIF":1.9,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221900","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}
In this paper, for the first time, we have attempted to identify the length and distribution of the wet and dry season of the 70 most populated cities in India based on the relative intra-annual distribution of rainfall observed during the last three decades (1991–2020). The wet season in the cities under investigation is rather continuous and usually lasts for 4–8 months. The distribution of the wet months however indicates significant variation, particularly for the cities located in the southern and northern periphery of the country. The cities located in the remaining areas even though have been receiving rainfall primarily during the Indian Summer Monsoon (ISM), the length of the wet season however varies among them. The present study also highlights that the average length of the wet season in India is not always four months; instead, it is a little over 5 months long in general. The length and distribution of the dry season also indicate that most cities receive a very negligible amount of rainfall for more than five months. The average amount of rainfall received by the cities during their respective dry season (4.1 %) is about 22 times lower than the average amount of rainfall received during their respective wet season (90.2 %). Noticeable variation in the amount of annual rainfall, length of dry and wet season, and amount of rainfall received in their respective wet and dry season among cities within each of the four homogeneous regions is also a significant finding of the present work.
{"title":"Identification and distribution of wet and dry season in the 70 most populated cities in India","authors":"Sumanta Dandapath , Abhijit Patil , Dhanashri Suresh Shinde , Praveen Kumar Pathak","doi":"10.1016/j.jastp.2025.106649","DOIUrl":"10.1016/j.jastp.2025.106649","url":null,"abstract":"<div><div>In this paper, for the first time, we have attempted to identify the length and distribution of the wet and dry season of the 70 most populated cities in India based on the relative intra-annual distribution of rainfall observed during the last three decades (1991–2020). The wet season in the cities under investigation is rather continuous and usually lasts for 4–8 months. The distribution of the wet months however indicates significant variation, particularly for the cities located in the southern and northern periphery of the country. The cities located in the remaining areas even though have been receiving rainfall primarily during the Indian Summer Monsoon (ISM), the length of the wet season however varies among them. The present study also highlights that the average length of the wet season in India is not always four months; instead, it is a little over 5 months long in general. The length and distribution of the dry season also indicate that most cities receive a very negligible amount of rainfall for more than five months. The average amount of rainfall received by the cities during their respective dry season (4.1 %) is about 22 times lower than the average amount of rainfall received during their respective wet season (90.2 %). Noticeable variation in the amount of annual rainfall, length of dry and wet season, and amount of rainfall received in their respective wet and dry season among cities within each of the four homogeneous regions is also a significant finding of the present work.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106649"},"PeriodicalIF":1.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221941","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 : 2025-09-24DOI: 10.1016/j.jastp.2025.106645
Abdullrahman Maghrabi, Abdulah Al-Dosari, Mohammed Altlasi, Abdulah Alsherhri, Maohammed Almutairi
This study investigates the temporal variability of temperature inversions below 5000 m in Dammam, Saudi Arabia(26.4°N, 50.1°E), over a 38-year period from 1985 to 2023, using radiosonde data to analyze six critical variables: base height (Hi), maximum height (Hf), temperature at the base (Ti), temperature at the maximum height (Tf), height difference (DH), and temperature difference (DT). Considering the temperature inversions occurred below 5000 m and with DT greater than 1 C, a total of 13744 temperature inversion events were recognized and investigated. Analysis revealed predominant low-level inversions (76.07 % below 1000 m) with strong nocturnal occurrence (∼70 %). High-level inversions showed seasonal variations in Hi (1900 ± 100 m in winter, 2500 ± 100 m in summer) and Ti (5.98 ± 2.0 °C in January, 22.30 ± 2.0 °C in June), with Hf consistently 200–300 m higher and Tf 2–3 °C warmer. DH ranged annually between 170 ± 50 m and 220 ± 50 m, with nocturnal low-level inversions showing the largest stable vertical extent (296 ± 23 m). Inversion frequencies increased over time, particularly at higher altitudes, with robust trends confirmed by Mann-Kendall and regression analyses (91.7 % concordance). These patterns, driven by radiative cooling, sea-land breezes, and urban heat island effects, suggest significant implications for air quality and urban planning in Dammam's coastal desert environment.
{"title":"Dynamics of atmospheric temperature inversions in Dammam, Saudi Arabia: Long-term characterization and trends","authors":"Abdullrahman Maghrabi, Abdulah Al-Dosari, Mohammed Altlasi, Abdulah Alsherhri, Maohammed Almutairi","doi":"10.1016/j.jastp.2025.106645","DOIUrl":"10.1016/j.jastp.2025.106645","url":null,"abstract":"<div><div>This study investigates the temporal variability of temperature inversions below 5000 m in Dammam, Saudi Arabia(26.4°N, 50.1°E), over a 38-year period from 1985 to 2023, using radiosonde data to analyze six critical variables: base height (H<sub>i</sub>), maximum height (H<sub>f</sub>), temperature at the base (T<sub>i</sub>), temperature at the maximum height (T<sub>f</sub>), height difference (DH), and temperature difference (DT). Considering the temperature inversions occurred below 5000 m and with DT greater than 1 C, a total of 13744 temperature inversion events were recognized and investigated. Analysis revealed predominant low-level inversions (76.07 % below 1000 m) with strong nocturnal occurrence (∼70 %). High-level inversions showed seasonal variations in H<sub>i</sub> (1900 ± 100 m in winter, 2500 ± 100 m in summer) and T<sub>i</sub> (5.98 ± 2.0 °C in January, 22.30 ± 2.0 °C in June), with H<sub>f</sub> consistently 200–300 m higher and Tf 2–3 °C warmer. DH ranged annually between 170 ± 50 m and 220 ± 50 m, with nocturnal low-level inversions showing the largest stable vertical extent (296 ± 23 m). Inversion frequencies increased over time, particularly at higher altitudes, with robust trends confirmed by Mann-Kendall and regression analyses (91.7 % concordance). These patterns, driven by radiative cooling, sea-land breezes, and urban heat island effects, suggest significant implications for air quality and urban planning in Dammam's coastal desert environment.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106645"},"PeriodicalIF":1.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156582","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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