Pub Date : 2026-03-01Epub Date: 2026-02-17DOI: 10.1016/j.jastp.2026.106760
Pedro Da Costa Louro , Philippe Keckhut , Alain Hauchecorne , Robin Wing , Gerd Baumgarten , Michael Gerding , Thierry Leblanc , Bernd Kaifler , Natalie Kaifler , Wolfgang Steinbrecht , Ali Jalali , Robert J. Sica
<div><div>Nine Rayleigh scattering-based lidars, some of which are affiliated with the Network for the Detection of Atmospheric Composition Change (NDACC) and the Atmospheric Remote Sensing in Stratosphere and mesosphere (ARISE) for monitoring stratospheric ozone, have been routinely observing temperature profile variations in the middle atmosphere for many years with excellent vertical resolution (around one kilometer). These observatories are located at various points around the globe from north to south: ALOMAR in Norway, Kühlungsborn in Mecklenburg-Western Pomerania Germany, Hohenpeissenberg in Bavaria Germany, Haute-Provence in southern France, Purple Crow in the Canadian London Ontario, Table Mountain in California, Mauna Loa in Hawaii, Maido on Reunion Island, Coral in Tierra del Fuego Argentina . These unique datasets have made it possible to update the climatology and seasonal variations of middle atmosphere temperatures between 30 and 80 km at several latitudes with significantly long databases that could be compared with numerical models. The behavior is similar at all sites, with a marked annual variation in the stratosphere of up to 6K, little variation around the stratopause (<span><math><mo>≈</mo></math></span> 2K) and a continuously increasing seasonal variation in the mesosphere of up to 16 K for some sites. These seasonal cycles are halved in tropical sites. The QBO (Quasi-Biennial Oscillation) is clearly visible in the temperature series and causes variations that can exceed 1 K. The temporal extent of the series, spanning several 11-year solar cycles, made it possible to extract variations associated with changes in solar activity on the vertical temperature profile, showing signatures of up to 4 K. In summer at mid and low latitudes, the signature is weak and around 1 K, in line with the expected photochemical effects. In winter, the response at mid-latitudes confirms previous observations of a negative effect. At higher latitudes, even larger signatures are observed, confirming the effect of feedback in the atmospheric response. This analysis also allowed us to update the quantification of decadal trends in the middle atmosphere, which show differences depending on latitude. These fluctuations become significant when the series are longer than one solar cycle. Observations at mid and low latitudes confirm the significant detection of a cooling ranging from about 1 K/decade in the upper stratosphere and increasing in the mesosphere to several K/decade. At mid latitudes, the three sites show good agreement, but in tropical/subtropical regions, greater variability is observed. In particular, in the mesosphere, observations obtained over Reunion Island in the tropical region in the southern hemisphere show cooling of up to 5 K/decade in the mesosphere, significantly greater than at other sites. Polar lidar data show that in these regions the trend is towards warming, with values remaining around 2K/decade, mainly in the mesosphe
{"title":"Decadal variability in mid-atmosphere temperature derived from continuous lidar observations","authors":"Pedro Da Costa Louro , Philippe Keckhut , Alain Hauchecorne , Robin Wing , Gerd Baumgarten , Michael Gerding , Thierry Leblanc , Bernd Kaifler , Natalie Kaifler , Wolfgang Steinbrecht , Ali Jalali , Robert J. Sica","doi":"10.1016/j.jastp.2026.106760","DOIUrl":"10.1016/j.jastp.2026.106760","url":null,"abstract":"<div><div>Nine Rayleigh scattering-based lidars, some of which are affiliated with the Network for the Detection of Atmospheric Composition Change (NDACC) and the Atmospheric Remote Sensing in Stratosphere and mesosphere (ARISE) for monitoring stratospheric ozone, have been routinely observing temperature profile variations in the middle atmosphere for many years with excellent vertical resolution (around one kilometer). These observatories are located at various points around the globe from north to south: ALOMAR in Norway, Kühlungsborn in Mecklenburg-Western Pomerania Germany, Hohenpeissenberg in Bavaria Germany, Haute-Provence in southern France, Purple Crow in the Canadian London Ontario, Table Mountain in California, Mauna Loa in Hawaii, Maido on Reunion Island, Coral in Tierra del Fuego Argentina . These unique datasets have made it possible to update the climatology and seasonal variations of middle atmosphere temperatures between 30 and 80 km at several latitudes with significantly long databases that could be compared with numerical models. The behavior is similar at all sites, with a marked annual variation in the stratosphere of up to 6K, little variation around the stratopause (<span><math><mo>≈</mo></math></span> 2K) and a continuously increasing seasonal variation in the mesosphere of up to 16 K for some sites. These seasonal cycles are halved in tropical sites. The QBO (Quasi-Biennial Oscillation) is clearly visible in the temperature series and causes variations that can exceed 1 K. The temporal extent of the series, spanning several 11-year solar cycles, made it possible to extract variations associated with changes in solar activity on the vertical temperature profile, showing signatures of up to 4 K. In summer at mid and low latitudes, the signature is weak and around 1 K, in line with the expected photochemical effects. In winter, the response at mid-latitudes confirms previous observations of a negative effect. At higher latitudes, even larger signatures are observed, confirming the effect of feedback in the atmospheric response. This analysis also allowed us to update the quantification of decadal trends in the middle atmosphere, which show differences depending on latitude. These fluctuations become significant when the series are longer than one solar cycle. Observations at mid and low latitudes confirm the significant detection of a cooling ranging from about 1 K/decade in the upper stratosphere and increasing in the mesosphere to several K/decade. At mid latitudes, the three sites show good agreement, but in tropical/subtropical regions, greater variability is observed. In particular, in the mesosphere, observations obtained over Reunion Island in the tropical region in the southern hemisphere show cooling of up to 5 K/decade in the mesosphere, significantly greater than at other sites. Polar lidar data show that in these regions the trend is towards warming, with values remaining around 2K/decade, mainly in the mesosphe","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106760"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420965","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 : 2026-03-01Epub Date: 2026-02-05DOI: 10.1016/j.jastp.2026.106746
Yongren Chen , Yueqing Li
The Southwest China vortex (SWCV) is an important weather system that affects warm-season rainfall in China, but forecasting its path of activity remains difficult. In this study, we chose six historical cases to analyze the thermodynamic differences between the less-moving pattern (LMP) and the moving pattern (MP) of SWCV activity, the principal results were as follows. (1) Regardless of whether the SWCV moved, an approximate “dipole pattern” of cold–warm advection was evident in the vortex circulation. Generally, dry–cold advection was found in the northeasterly airflow of the SWCV circulation, and wet–warm advection occurred in the southwesterly airflow. (2) In the LMP, the range of dry–cold advection was smaller than that of wet–warm advection in the horizontal direction. In the MP, the range of dry–cold advection at the rear of the SWCV along the direction of motion expanded and promoted movement of the SWCV. In the vertical direction, the front of the SWCV was always characterized by wet–warm advection, and the rear was characterized by dry–cold advection under eastward movement of the MP, but such a feature was not obvious in the LMP. To a certain extent, the approximate dipole pattern of cold–warm advection in the horizontal direction facilitates the maintenance of a complete cyclonic circulation, but it also supports SWCV movement when the dry–cold advection expands and becomes deeper in the vertical direction. Based on these findings, we tried to define a thermodynamic index for determining SWCV movement.
{"title":"Comparative analysis of thermal structure in the activities of Southwest China vortexes","authors":"Yongren Chen , Yueqing Li","doi":"10.1016/j.jastp.2026.106746","DOIUrl":"10.1016/j.jastp.2026.106746","url":null,"abstract":"<div><div>The Southwest China vortex (SWCV) is an important weather system that affects warm-season rainfall in China, but forecasting its path of activity remains difficult. In this study, we chose six historical cases to analyze the thermodynamic differences between the less-moving pattern (LMP) and the moving pattern (MP) of SWCV activity, the principal results were as follows. (1) Regardless of whether the SWCV moved, an approximate “dipole pattern” of cold–warm advection was evident in the vortex circulation. Generally, dry–cold advection was found in the northeasterly airflow of the SWCV circulation, and wet–warm advection occurred in the southwesterly airflow. (2) In the LMP, the range of dry–cold advection was smaller than that of wet–warm advection in the horizontal direction. In the MP, the range of dry–cold advection at the rear of the SWCV along the direction of motion expanded and promoted movement of the SWCV. In the vertical direction, the front of the SWCV was always characterized by wet–warm advection, and the rear was characterized by dry–cold advection under eastward movement of the MP, but such a feature was not obvious in the LMP. To a certain extent, the approximate dipole pattern of cold–warm advection in the horizontal direction facilitates the maintenance of a complete cyclonic circulation, but it also supports SWCV movement when the dry–cold advection expands and becomes deeper in the vertical direction. Based on these findings, we tried to define a thermodynamic index for determining SWCV movement.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106746"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420974","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 : 2026-03-01Epub Date: 2026-02-16DOI: 10.1016/j.jastp.2026.106762
Anand Shankar , Bikash Chandra Sahana
Reduced atmospheric visibility arising from emissions, air pollutants, and environmental factors such as humidity, temperature, and weather conditions necessitates accurate estimation of visibility and runway visual range (RVR) for safe and efficient aviation operations. This research introduces a hybrid deep learning system for the automatic estimate of Visibility and RVR using CCTV images. Ground-truth labels are carried out from co-located forward scatterometer (FSM) readings from more than 13,000 images acquired at one-minute intervals adjacent to Runway 25 at Patna Airport from December 25, 2023, to January 31, 2024. The dataset was divided into training, validation, and testing subsets, including daytime, night-time, and mixed low-visibility scenarios, to guarantee thorough model assessment across all illumination conditions.
Two hybrid architectures were proposed and systematically evaluated: (i) a CNN-RNN model incorporating Fast Fourier Transform (FFT) pre-processing for frequency-domain feature extraction and (ii) a CNN-LSTM model merging convolutional layers for spatial feature extraction with LSTM networks for temporal dependency analysis. Both architectures utilize fully connected layers to associate learned spatiotemporal features with visibility and RVR outputs. In three distinct test datasets, the CNN-LSTM architecture attained R2 values of 0.86, 0.83, and 0.84 for visibility estimation and 0.91, 0.84, and 0.87 for RVR estimation, surpassing the FFT-based CNN-RNN model with consistently reduced mean absolute error (MAE) and root mean squared error (RMSE). Scenario-specific investigation verifies strong performance under daytime, night-time, and mixed lighting circumstances.
The findings indicate the framework's functional feasibility for aviation weather services, offering an automated, scalable, and economical substitute for conventional scatterometer-based systems. The methodology demonstrates significant potential for visibility estimation across several applications, including road transport and marine navigation, where precise real-time visibility evaluation under fluctuating environmental circumstances is essential for safety and operational efficiency.
{"title":"A hybrid deep learning framework for image-based visibility and runway visual range estimation for aviation services","authors":"Anand Shankar , Bikash Chandra Sahana","doi":"10.1016/j.jastp.2026.106762","DOIUrl":"10.1016/j.jastp.2026.106762","url":null,"abstract":"<div><div>Reduced atmospheric visibility arising from emissions, air pollutants, and environmental factors such as humidity, temperature, and weather conditions necessitates accurate estimation of visibility and runway visual range (RVR) for safe and efficient aviation operations. This research introduces a hybrid deep learning system for the automatic estimate of Visibility and RVR using CCTV images. Ground-truth labels are carried out from co-located forward scatterometer (FSM) readings from more than 13,000 images acquired at one-minute intervals adjacent to Runway 25 at Patna Airport from December 25, 2023, to January 31, 2024. The dataset was divided into training, validation, and testing subsets, including daytime, night-time, and mixed low-visibility scenarios, to guarantee thorough model assessment across all illumination conditions.</div><div>Two hybrid architectures were proposed and systematically evaluated: (i) a CNN-RNN model incorporating Fast Fourier Transform (FFT) pre-processing for frequency-domain feature extraction and (ii) a CNN-LSTM model merging convolutional layers for spatial feature extraction with LSTM networks for temporal dependency analysis. Both architectures utilize fully connected layers to associate learned spatiotemporal features with visibility and RVR outputs. In three distinct test datasets, the CNN-LSTM architecture attained R<sup>2</sup> values of 0.86, 0.83, and 0.84 for visibility estimation and 0.91, 0.84, and 0.87 for RVR estimation, surpassing the FFT-based CNN-RNN model with consistently reduced mean absolute error (MAE) and root mean squared error (RMSE). Scenario-specific investigation verifies strong performance under daytime, night-time, and mixed lighting circumstances.</div><div>The findings indicate the framework's functional feasibility for aviation weather services, offering an automated, scalable, and economical substitute for conventional scatterometer-based systems. The methodology demonstrates significant potential for visibility estimation across several applications, including road transport and marine navigation, where precise real-time visibility evaluation under fluctuating environmental circumstances is essential for safety and operational efficiency.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106762"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422311","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 : 2026-03-01Epub Date: 2026-02-17DOI: 10.1016/j.jastp.2026.106764
Vishnu Rajendra Kumar, Richard L. Collins, Jintai Li, Rahsha Kerven, Jennifer Alspach, Denise Thorsen
This study presents observations of noctilucent clouds (NLCs) and associated mesospheric conditions at Poker Flat Research Range (PFRR), Chatanika, Alaska (65° N, 147° W). NLCs were measured using the Rayleigh lidar system from 2005 to 2022, with a total of 28 events recorded between late July and early August. The clouds were characterized in terms of brightness and duration using the peak and integrated backscatter coefficients from lidar data. Local wind patterns were analyzed using meteor radar observations, while Aura MLS data provided temperature and water vapor profiles during the NLC events. The results show that NLC occurrence at Chatanika is strongly linked to temperatures relative to the frost point and indicate that the brightest NLCs occurred during periods when MLS collocations indicated marginally subsaturated background conditions at standard levels. Cloud-favorable conditions exist several hundred kilometers north of Chatanika, and meteor radar winds support this interpretation, suggesting that southward transport played a major role in delivering NLCs to the site.
本研究介绍了阿拉斯加州查塔尼卡（65°N, 147°W）的Poker Flat Research Range （PFRR）的夜光云（nlc）和相关中间层条件的观测结果。从2005年到2022年，使用瑞利激光雷达系统测量了NLCs，在7月底到8月初共记录了28次事件。利用激光雷达数据的峰值散射系数和综合后向散射系数对云的亮度和持续时间进行了表征。利用流星雷达观测分析了当地的风型，而Aura MLS数据提供了NLC事件期间的温度和水蒸气剖面。结果表明，在Chatanika的NLC发生与相对于霜点的温度密切相关，并且表明最亮的NLC发生在MLS搭配表明在标准水平下的边缘亚饱和背景条件的时期。在Chatanika以北几百公里处存在有利的云层条件，流星雷达风支持了这一解释，表明向南的运输在将nlc运送到该地点方面发挥了主要作用。
{"title":"An extended investigation of noctilucent cloud dynamics at Chatanika, Alaska using lidar, satellite, and radar observations","authors":"Vishnu Rajendra Kumar, Richard L. Collins, Jintai Li, Rahsha Kerven, Jennifer Alspach, Denise Thorsen","doi":"10.1016/j.jastp.2026.106764","DOIUrl":"10.1016/j.jastp.2026.106764","url":null,"abstract":"<div><div>This study presents observations of noctilucent clouds (NLCs) and associated mesospheric conditions at Poker Flat Research Range (PFRR), Chatanika, Alaska (65° N, 147° W). NLCs were measured using the Rayleigh lidar system from 2005 to 2022, with a total of 28 events recorded between late July and early August. The clouds were characterized in terms of brightness and duration using the peak and integrated backscatter coefficients from lidar data. Local wind patterns were analyzed using meteor radar observations, while Aura MLS data provided temperature and water vapor profiles during the NLC events. The results show that NLC occurrence at Chatanika is strongly linked to temperatures relative to the frost point and indicate that the brightest NLCs occurred during periods when MLS collocations indicated marginally subsaturated background conditions at standard levels. Cloud-favorable conditions exist several hundred kilometers north of Chatanika, and meteor radar winds support this interpretation, suggesting that southward transport played a major role in delivering NLCs to the site.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106764"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420966","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 : 2026-03-01Epub Date: 2026-02-01DOI: 10.1016/j.jastp.2026.106741
Pengwei Gao, Jinye Cui
Precipitable water vapor (PWV) is an important parameter in studying hydrological cycles on a regional or global scale and monitoring local weather changes. Here, PWV and zenith total delay (ZTD) datasets were derived from Global Navigation Satellite System (GNSS) measurements at 430 stations in the contiguous United States from 2013 through 2020. These GNSS-derived ZTD and PWV datasets were assessed based on radiosonde (RS) data at 48 RS-GNSS collocated sites from 2013 through 2020 and ERA5 reanalysis products at 430 sites covering the same periods. Comparisons show that the GNSS-derived PWV dataset obtains overall accuracies in terms of root-mean-square (RMS) errors of 2.3 mm and 1.8 mm with respect to the RS measurements and ERA5 reanalysis products, respectively. Moreover, the GNSS-derived ZTD dataset achieves overall RMS errors of 1.4 cm and 1.0 cm in contrast to the RS data and ERA5 atmosphere products, respectively. In general, the quality of the GNSS-derived PWV dataset in the regions of interest mainly depends on the accuracy of ZTD estimations obtained from GNSS observations. Furthermore, mean values and seasonal variations of PWV time series derived from GNSS observations are in good agreement with those extracted from the ERA5 reanalysis products across the contiguous United States. The mean values of PWV time series in the southeastern regions vary from 20 mm to 45 mm and they are larger than those in the western regions, which is due to the topography distribution over the contiguous United States.
{"title":"Comparisons of precipitable water vapor and zenith total delay derived from GNSS measurements, radiosondes, and ERA5 reanalysis data in the contiguous United States","authors":"Pengwei Gao, Jinye Cui","doi":"10.1016/j.jastp.2026.106741","DOIUrl":"10.1016/j.jastp.2026.106741","url":null,"abstract":"<div><div>Precipitable water vapor (PWV) is an important parameter in studying hydrological cycles on a regional or global scale and monitoring local weather changes. Here, PWV and zenith total delay (ZTD) datasets were derived from Global Navigation Satellite System (GNSS) measurements at 430 stations in the contiguous United States from 2013 through 2020. These GNSS-derived ZTD and PWV datasets were assessed based on radiosonde (RS) data at 48 RS-GNSS collocated sites from 2013 through 2020 and ERA5 reanalysis products at 430 sites covering the same periods. Comparisons show that the GNSS-derived PWV dataset obtains overall accuracies in terms of root-mean-square (RMS) errors of 2.3 mm and 1.8 mm with respect to the RS measurements and ERA5 reanalysis products, respectively. Moreover, the GNSS-derived ZTD dataset achieves overall RMS errors of 1.4 cm and 1.0 cm in contrast to the RS data and ERA5 atmosphere products, respectively. In general, the quality of the GNSS-derived PWV dataset in the regions of interest mainly depends on the accuracy of ZTD estimations obtained from GNSS observations. Furthermore, mean values and seasonal variations of PWV time series derived from GNSS observations are in good agreement with those extracted from the ERA5 reanalysis products across the contiguous United States. The mean values of PWV time series in the southeastern regions vary from 20 mm to 45 mm and they are larger than those in the western regions, which is due to the topography distribution over the contiguous United States.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106741"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421985","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 : 2026-03-01Epub Date: 2026-02-14DOI: 10.1016/j.jastp.2026.106757
Alireza Mahmoudian , Mahsa Baghbani , Joe McInerney
The mesosphere and lower thermosphere (MLT), which extends from altitudes of 60 to 120 km, serve as the boundary between Earth’s atmosphere and outer space. This region is challenging to study directly, as it is very challenging to instruments mounted on satellites or balloons. Ground-based tools like radars and lidars offer only limited observations, primarily focused on the neutral atmosphere using electromagnetic waves. This paper delves into how solar storm activity penetrates the MLT region. It examines the neutral wind patterns and background temperature responses during three geomagnetic storms in 2015 and 2003. The research employs numerical simulations utilizing the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X). We analyze the zonal and meridional winds along with temperature changes in the MLT region under both quiet and active geomagnetic conditions. To validate the simulation outcomes, we incorporate neutral wind data gathered from Wuhan meteor radar observations and temperature measurements from the TIMED/SABER satellite. Our findings indicate a distinct signature of the neutral wind’s response to geomagnetic activity. The WACCM-X model prediction of meridional and zonal winds in the MLT region is consistent with the meteor radar observations. We also determine the penetration depth and the percentage of background temperature alteration resulting from active geomagnetic conditions. The alignment of the WACCM-X results with the observational data is encouraging. A data assimilation technique using the WACCM-X model, combined with radar and satellite observations, is proposed to determine the MLT response to active geomagnetic conditions.
{"title":"Investigating MLT response to active geomagnetic conditions using WACCM-X simulations, Wuhan meteor radar and SABER observations","authors":"Alireza Mahmoudian , Mahsa Baghbani , Joe McInerney","doi":"10.1016/j.jastp.2026.106757","DOIUrl":"10.1016/j.jastp.2026.106757","url":null,"abstract":"<div><div>The mesosphere and lower thermosphere (MLT), which extends from altitudes of 60 to 120 km, serve as the boundary between Earth’s atmosphere and outer space. This region is challenging to study directly, as it is very challenging to instruments mounted on satellites or balloons. Ground-based tools like radars and lidars offer only limited observations, primarily focused on the neutral atmosphere using electromagnetic waves. This paper delves into how solar storm activity penetrates the MLT region. It examines the neutral wind patterns and background temperature responses during three geomagnetic storms in 2015 and 2003. The research employs numerical simulations utilizing the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X). We analyze the zonal and meridional winds along with temperature changes in the MLT region under both quiet and active geomagnetic conditions. To validate the simulation outcomes, we incorporate neutral wind data gathered from Wuhan meteor radar observations and temperature measurements from the TIMED/SABER satellite. Our findings indicate a distinct signature of the neutral wind’s response to geomagnetic activity. The WACCM-X model prediction of meridional and zonal winds in the MLT region is consistent with the meteor radar observations. We also determine the penetration depth and the percentage of background temperature alteration resulting from active geomagnetic conditions. The alignment of the WACCM-X results with the observational data is encouraging. A data assimilation technique using the WACCM-X model, combined with radar and satellite observations, is proposed to determine the MLT response to active geomagnetic conditions.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106757"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420969","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}
The study investigates the first excited electronic state of hydroxyl, , in Earth’s upper atmosphere ( ). A radiative transfer and fluid model is employed to evaluate the effects of soft X-rays ( nm), EUV ( nm; nm) solar radiation, and energetic electrons on formation. The main production mechanisms are photolysis and solar pumping of , with additional contributions from reactions involving hydrogen and oxygen atoms. The model shows that excitation depends on the solar zenith angle, reaching its maximum intensity at solar noon, and increases with higher solar activity. Model results are validated using OSIRIS satellite observations of limb radiance.
{"title":"Daytime ultraviolet-driven OH variability in the earth’s upper atmosphere: Effects of solar zenith angle and F10.7 solar flux","authors":"Abdelaaziz Bouziane , Mohammed Amin Ferdi , Cirta Tanssaout , Mourad Djebli","doi":"10.1016/j.jastp.2026.106744","DOIUrl":"10.1016/j.jastp.2026.106744","url":null,"abstract":"<div><div>The study investigates the first excited electronic state of hydroxyl, <span><math><mrow><mtext>OH</mtext><mrow><mo>(</mo><mi>A</mi><mo>)</mo></mrow></mrow></math></span>, in Earth’s upper atmosphere (<span><math><mrow><mn>80</mn><mtext>–</mtext><mn>130</mn></mrow></math></span> <span><math><mtext>km</mtext></math></span>). A radiative transfer and fluid model is employed to evaluate the effects of soft X-rays (<span><math><mrow><mn>1</mn><mtext>–</mtext><mn>10</mn></mrow></math></span> nm), EUV (<span><math><mrow><mn>10</mn><mtext>–</mtext><mn>175</mn></mrow></math></span> nm; <span><math><mrow><mn>306</mn><mtext>–</mtext><mn>311</mn></mrow></math></span> nm) solar radiation, and energetic electrons on <span><math><mrow><mtext>OH</mtext><mrow><mo>(</mo><mi>A</mi><mo>)</mo></mrow></mrow></math></span> formation. The main production mechanisms are <span><math><mrow><msub><mrow><mtext>H</mtext></mrow><mrow><mn>2</mn></mrow></msub><mtext>O</mtext></mrow></math></span> photolysis and solar pumping of <span><math><mrow><mtext>OH</mtext><mrow><mo>(</mo><mi>X</mi><mo>)</mo></mrow></mrow></math></span>, with additional contributions from reactions involving hydrogen and oxygen atoms. The model shows that <span><math><mrow><mtext>OH</mtext><mrow><mo>(</mo><mi>A</mi><mo>)</mo></mrow></mrow></math></span> excitation depends on the solar zenith angle, reaching its maximum intensity at solar noon, and increases with higher solar activity. Model results are validated using OSIRIS satellite observations of limb radiance.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106744"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421984","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 : 2026-03-01Epub Date: 2026-01-30DOI: 10.1016/j.jastp.2026.106742
Manabraj Manna , Dhirendra Kumar Singh , Murtaza Hasan , Santosh Sambhaji Mali , Deb Kumar Das , Himani Bisht , Jitendra Rajput , Rupesh Kumar , Anjani Kumar Yadav
The purpose of this research was to delineate the areas susceptible to flooding in the Middle Indo-Gangetic Plain by employing Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP). Fourteen parameters, namely rainfall, elevation, slope, flow accumulation, drainage density, distance from rivers, stream power index, topographic wetness index, land use/land cover, soil texture, hydrogeology, Normalized Difference Vegetation Index (NDVI), topographic roughness index, and profile curvature, were selected for the detailed analysis. Weights were assigned to these parameters using AHP. Flood vulnerable areas were delineated, and a flood vulnerability map was generated using overlay analysis in GIS. The map’s accuracy was confirmed using the area under the curve receiver operating characteristic (AUC-ROC) technique with 1000 random validation points derived from historical flood data. The results indicated that 3.94 % of the study area is very highly vulnerable to floods, primarily in the eastern and northwestern parts near the Ganges River and its tributaries. Approximately 6.68 % is classified as highly vulnerable, while 56.02 % falls within the moderate vulnerability category. The remaining 33.36 % of the area shows low to very low flood vulnerability. The overall accuracy of the vulnerability map is 78.9 %, indicating a good predictive capability. This study demonstrates the effectiveness of integrating RS, GIS and AHP for flood vulnerability assessment in complex hydrological regions such as the Middle Indo-Gangetic Plain. A vulnerability map can help in developing flood management strategies, land use planning, agricultural activities, and disaster preparedness in this densely populated and flood-prone region.
{"title":"Flood vulnerability mapping using remote sensing (RS), geographic information system (GIS), and analytical hierarchy process (AHP): A case study of the middle Indo-Gangetic Plain","authors":"Manabraj Manna , Dhirendra Kumar Singh , Murtaza Hasan , Santosh Sambhaji Mali , Deb Kumar Das , Himani Bisht , Jitendra Rajput , Rupesh Kumar , Anjani Kumar Yadav","doi":"10.1016/j.jastp.2026.106742","DOIUrl":"10.1016/j.jastp.2026.106742","url":null,"abstract":"<div><div>The purpose of this research was to delineate the areas susceptible to flooding in the Middle Indo-Gangetic Plain by employing Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP). Fourteen parameters, namely rainfall, elevation, slope, flow accumulation, drainage density, distance from rivers, stream power index, topographic wetness index, land use/land cover, soil texture, hydrogeology, Normalized Difference Vegetation Index (NDVI), topographic roughness index, and profile curvature, were selected for the detailed analysis. Weights were assigned to these parameters using AHP. Flood vulnerable areas were delineated, and a flood vulnerability map was generated using overlay analysis in GIS. The map’s accuracy was confirmed using the area under the curve receiver operating characteristic (AUC-ROC) technique with 1000 random validation points derived from historical flood data. The results indicated that 3.94 % of the study area is very highly vulnerable to floods, primarily in the eastern and northwestern parts near the Ganges River and its tributaries. Approximately 6.68 % is classified as highly vulnerable, while 56.02 % falls within the moderate vulnerability category. The remaining 33.36 % of the area shows low to very low flood vulnerability. The overall accuracy of the vulnerability map is 78.9 %, indicating a good predictive capability. This study demonstrates the effectiveness of integrating RS, GIS and AHP for flood vulnerability assessment in complex hydrological regions such as the Middle Indo-Gangetic Plain. A vulnerability map can help in developing flood management strategies, land use planning, agricultural activities, and disaster preparedness in this densely populated and flood-prone region.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106742"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421988","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 : 2026-03-01Epub Date: 2026-02-10DOI: 10.1016/j.jastp.2026.106759
Meenakshi S, S. Sridharan
On 20-21 November 2024, extreme precipitation, the first of its kind to occur in the past 125 years, resulted in severe flooding across the southern coastal region of Tamil Nadu in particular over Rameswaram. The dynamic and thermodynamic conditions in the atmosphere that precede this extreme precipitation event are examined using reanalysis datasets of ERA5, brightness temperature, and Sea Surface Temperature. The analysis reveals deep convective clouds, a large amount of precipitable water, elevated specific humidity, and wind convergence in the lower troposphere at Rameswaram. The positive sea surface temperature (SST) anomaly of 2°C in the Bay of Bengal could lead to significant evaporation. Intense moisture is transported from the Bay of Bengal by easterly winds, and the westward-propagating tropical easterly waves facilitate the advection of positive PV anomalies towards Rameswaram. The presence of a cyclonic circulation progressing towards the southwest coast of the Indian Peninsula is evident in the moisture flux divergence. Convection was supported and sustained by enhanced upward vertical velocity between 850 and 200 hPa. Moreover, the positive potential vorticity (PV) tower extending from 850 hPa to 200 hPa, points to the development of dynamic instability. Vertical and horizontal PV dipoles, signatures of deep convection and instability, are also identified over Rameswaram during heavy rain. The time-height evolution of the divergence, vertical velocity (ω), and PV anomaly, along with the mid-level vortex, suggests that the 'top-down' dynamics can be responsible for the torrential downpour over Rameswaram.
{"title":"Governing factors of the unprecedented extreme rainfall over Rameswaram Island","authors":"Meenakshi S, S. Sridharan","doi":"10.1016/j.jastp.2026.106759","DOIUrl":"10.1016/j.jastp.2026.106759","url":null,"abstract":"<div><div>On 20-21 November 2024, extreme precipitation, the first of its kind to occur in the past 125 years, resulted in severe flooding across the southern coastal region of Tamil Nadu in particular over Rameswaram. The dynamic and thermodynamic conditions in the atmosphere that precede this extreme precipitation event are examined using reanalysis datasets of ERA5, brightness temperature, and Sea Surface Temperature. The analysis reveals deep convective clouds, a large amount of precipitable water, elevated specific humidity, and wind convergence in the lower troposphere at Rameswaram. The positive sea surface temperature (SST) anomaly of 2°C in the Bay of Bengal could lead to significant evaporation. Intense moisture is transported from the Bay of Bengal by easterly winds, and the westward-propagating tropical easterly waves facilitate the advection of positive PV anomalies towards Rameswaram. The presence of a cyclonic circulation progressing towards the southwest coast of the Indian Peninsula is evident in the moisture flux divergence. Convection was supported and sustained by enhanced upward vertical velocity between 850 and 200 hPa. Moreover, the positive potential vorticity (PV) tower extending from 850 hPa to 200 hPa, points to the development of dynamic instability. Vertical and horizontal PV dipoles, signatures of deep convection and instability, are also identified over Rameswaram during heavy rain. The time-height evolution of the divergence, vertical velocity (<strong><em>ω</em></strong>), and PV anomaly, along with the mid-level vortex, suggests that the 'top-down' dynamics can be responsible for the torrential downpour over Rameswaram.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106759"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420967","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 : 2026-03-01Epub Date: 2026-01-27DOI: 10.1016/j.jastp.2026.106739
Yanhua Chen , Zhichao Tian , Wenbo Chen , Yi Yang , Caihong Li , Katinka Wolter
The rapid growth of wind energy makes accurate wind speed forecasting vital for power system reliability and efficiency. However, the nonlinear, non-stationary, and dynamic nature of wind speed, coupled with atmospheric disturbances, makes precise forecasting challenging. To address these challenges, this paper proposes a multi-stage hybrid framework integrating signal decomposition, filtering, deep learning, and intelligent optimization. First, a preprocessing method based on Variational Mode Decomposition (VMD) and Savitzky-Golay (SG) filtering is developed to decompose raw wind speed sequences into Intrinsic Mode Functions (IMFs) and adaptively denoise them, effectively reducing non-stationarity. Next, the refined data is processed by a Convolutional Long Short-Term Memory (ConvLSTM) network to capture local spatio-temporal dependencies, followed by a Transformer encoder-decoder to model long-range dependencies for multi-step forecasting. Finally, a novel GPSOGA algorithm, integrating the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) with a Global Elite Opposition-Based Learning strategy (GEOLS), is proposed to optimize hyperparameters, ensuring efficient parameter tuning and enhanced performance. Experimental results on real-world wind farm datasets demonstrate that the proposed framework consistently outperforms comparative models. Specifically, it improves forecasting accuracy by 37.1 %–56.6 % compared to individual CNN, LSTM, ConvLSTM, and Transformer models. These results confirm the framework's superior accuracy and robustness, validating its practical potential for wind energy management.
{"title":"A hybrid wind speed forecasting framework with decomposition denoising and intelligent optimization algorithms","authors":"Yanhua Chen , Zhichao Tian , Wenbo Chen , Yi Yang , Caihong Li , Katinka Wolter","doi":"10.1016/j.jastp.2026.106739","DOIUrl":"10.1016/j.jastp.2026.106739","url":null,"abstract":"<div><div>The rapid growth of wind energy makes accurate wind speed forecasting vital for power system reliability and efficiency. However, the nonlinear, non-stationary, and dynamic nature of wind speed, coupled with atmospheric disturbances, makes precise forecasting challenging. To address these challenges, this paper proposes a multi-stage hybrid framework integrating signal decomposition, filtering, deep learning, and intelligent optimization. First, a preprocessing method based on Variational Mode Decomposition (VMD) and Savitzky-Golay (SG) filtering is developed to decompose raw wind speed sequences into Intrinsic Mode Functions (IMFs) and adaptively denoise them, effectively reducing non-stationarity. Next, the refined data is processed by a Convolutional Long Short-Term Memory (ConvLSTM) network to capture local spatio-temporal dependencies, followed by a Transformer encoder-decoder to model long-range dependencies for multi-step forecasting. Finally, a novel GPSOGA algorithm, integrating the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) with a Global Elite Opposition-Based Learning strategy (GEOLS), is proposed to optimize hyperparameters, ensuring efficient parameter tuning and enhanced performance. Experimental results on real-world wind farm datasets demonstrate that the proposed framework consistently outperforms comparative models. Specifically, it improves forecasting accuracy by 37.1 %–56.6 % compared to individual CNN, LSTM, ConvLSTM, and Transformer models. These results confirm the framework's superior accuracy and robustness, validating its practical potential for wind energy management.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"280 ","pages":"Article 106739"},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420973","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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