Journal of Atmospheric and Solar-Terrestrial Physics最新文献

{"title":"Evaluating the performance of the rainfall forecast model based on GNSS ZTD and ERA5 CAPE for Hong Kong","authors":"Yang Liu ,&nbsp;Qiaoling Zhang ,&nbsp;Tenglong Wang ,&nbsp;Yibin Yao ,&nbsp;Mingxian Hu ,&nbsp;Haobo Li ,&nbsp;Bao Zhang ,&nbsp;Chaoqian Xu ,&nbsp;Qingzhi Zhao","doi":"10.1016/j.jastp.2025.106525","DOIUrl":"10.1016/j.jastp.2025.106525","url":null,"abstract":"<div><div>The Earth's troposphere delay provides valuable information for rainfall forecasting. Tropospheric delay products (e.g., Zenith Total Delay (ZTD)) derived from the Global Navigation Satellite System (GNSS) have become an indispensable approach for accessing tropospheric information. Several studies have established rainfall forecast models based on the GNSS-derived ZTD. However, the forecast predictors used in these models were all developed from the ZTD or its highly correlated parameters, resulting in high virtual forecast results. Therefore, a novel predictor, Convective Available Potential Energy (CAPE), was introduced to enhance the existing rainfall forecast model and is composed of the ZTD and CAPE rainfall (ZCR) model. The ZCR model includes six predictors: the ZTD value, ZTD variation, its derivation, CAPE value, CAPE variation, and its derivation. Hourly ZTD, CAPE, and rainfall data from three stations in Hong Kong with a time span of one year were collected to perform the experiment. The forecast accuracy of the ZCR model was evaluated using the corrected forecasted rate (CFR) and false alarm rate (FAR). The optimal thresholds of the predictors were determined using the percentile method, following the highest CFR and lowest FAR. The forecast accuracies of the ZTD and CAPE models, composed of their own three predictors, were comparable at the seasonal and monthly scales. Finally, we combined the ZTD and CAPE predictors into a ZCR model. Compared with the single ZTD or CAPE models, the combined ZCR model can significantly improve the accuracy of the FAR, with the CFR and FAR values of 93.53 % and 26.82 %, respectively. And the ZCR model also illustrates that the forecast accuracy on the monthly scale is higher than that on the seasonal scale.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106525"},"PeriodicalIF":1.8,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on PWV fusion based on ERA5 PWV and FY-4A PWV in Chinese mainland","authors":"Wei Du ,&nbsp;Yong Wang ,&nbsp;Xiao Liu ,&nbsp;Jing Huang ,&nbsp;Yanping Liu ,&nbsp;Xiangshun Meng","doi":"10.1016/j.jastp.2025.106526","DOIUrl":"10.1016/j.jastp.2025.106526","url":null,"abstract":"<div><div>High precision and high spatiotemporal resolution of precipitable water vapor (PWV) serve as fundamental support for meteorological research, severe weather forecasting and warning systems, as well as long-term climate change trend analysis. The FY-4A PWV has been widely utilized in PWV research due to its high observational accuracy and superior spatial resolution. However, there are many missing values in the FY-4A PWV due to the influence of weather and cloud cover. The ERA5 PWV has the advantages of high temporal resolution, complete data, rapid updates, and a multitude of parameters, but its spatial resolution is relatively low at 0.25° × 0.25°. Therefore, the focus of this study is to integrate the advantages of multiple datasets to obtain high-precision PWV data with superior spatiotemporal resolution and completeness. This study takes the Chinese Mainland as research area, the ERA5 PWV was interpolated to the same spatial resolution as the FY-4A PWV (4km × 4 km) using the cubic convolution interpolation, and the interpolated ERA5 PWV was used to fill in the missing values of FY-4A PWV. Based on their respective precision weights, the datasets were integrated through the fusion model to generate a complete and high-accuracy PWV product. The results show that more than 55 % of the interpolated ERA5 PWV has a correlation coefficient of more than 0.9 with the GNSS PWV, and the difference in the ERA5 PWV accuracy before and after interpolation is within 3 mm in each region. By establishing regional and seasonal data fusion model, the RMSE of the PWV after fusion can be relatively improved by 0.34 %–24.96 %,0.36 %–29.39 %,0.78 %–27.94 %,0.78 %–34.31 % for the four seasons, respectively. The fusion effect is better in spring and winter, and the fusion PWV error is basically within 4 mm. This study achieves the acquisition of high-precision PWV data with a spatial resolution of 4km × 4 km on an hourly basis in Chinese mainland. The fused PWV can provide an important database for further applications and research.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106526"},"PeriodicalIF":1.8,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atmospheric electric field variations and its correlation with air quality index as observed from two low-latitude Indian stations","authors":"Rakhijul Alam Faruque ,&nbsp;Sujay Pal ,&nbsp;Gahul Amin ,&nbsp;Sushanta Kumar Mondal","doi":"10.1016/j.jastp.2025.106505","DOIUrl":"10.1016/j.jastp.2025.106505","url":null,"abstract":"<div><div>Atmospheric electric field (AEF) variations and their relationship with air quality indicators like the Air Quality Index (AQI) are vital in understanding both local AEF and global electric circuit (GEC) behaviors. The present study investigates the diurnal and seasonal variations of AEF at two different locations in West Bengal, India: Bagula (BGL) and Purulia (PRL). The fair weather AEF variations from these sites are compared with the global standard Carnegie curve and with other global sites. Compared to the urban site, which exhibits notable departures from the Carnegie curve, the fair-weather mean diurnal variation at the rural site displays distinct GEC signals. The AEF variations are also analyzed with respect to the AQI, and particulate matter in those two sites to evaluate the impact of local air pollution on atmospheric electricity. Moderate to strong correlations between AEF and AQI across different seasons are obtained. The seasonal AEF variation and AQI have a strong link for the winter season (r = 0.75), the post-monsoon season (r = 0.59) at BGL, and the post-monsoon season (r = 0.63) at PRL. The monsoon season experienced the lowest correlation in both sites. The AEF exhibits a strongly negative association with the AQI on a daily scale during periods of extreme pollution in both sites, suggesting the formation of a negative space charge layer that modifies AEF. Results indicate the possibility of estimating AQI values from the AEF measurements during high episodes of pollution.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106505"},"PeriodicalIF":1.8,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation and visualizations of solar car motion along the road","authors":"Emin Gardashov ,&nbsp;Murad Eminov ,&nbsp;Gökhan Kara ,&nbsp;Rauf Gardashov","doi":"10.1016/j.jastp.2025.106524","DOIUrl":"10.1016/j.jastp.2025.106524","url":null,"abstract":"<div><div>A mathematical model for the simulation of the motion of a solar car along the arbitrary uneven road has been developed. This model consists of two parts. The first is the “mathematical model of solar radiation falling onto the panel of the solar car” that determines the amount of solar energy falling onto the panel at any instant of time and at any point of the road. The second is the “mathematical model of the motion of the solar car” that determines the law of motion by solving the differential equation of motion while taking into account all the main forces acting on the car; namely: driving, gravity, reaction, and resistance. The model-simulated data has been used to create ArcGIS-based 3D visualization of car motion along the chosen roads.</div><div>The model developed here can be used to evaluate the performance of chosen type of solar car on uneven road and can serve for the solution of various problems such as: i) optimal choice of car motion starting time to minimize travelling time; ii) determination of the optimal speed that should be maintained along the uneven road for hybrid (PV + accumulator) cars; iii) determination of the instants of time when car will reach the mountain passes where a huge amount of solar radiation or sufficiently charged accumulators, are needed; iv) to determine the proper instants of time for triggering the accumulator and its charging, etc. At the same time, the model allows to estimate the amount of solar energy harvested by PV panel during the motion on the chosen highway. This energy depends mainly on the geographical location, the season, the weather conditions, the topography of the road (trajectory) and car speed.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106524"},"PeriodicalIF":1.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A physical parameterization for cloudy-sky downward longwave radiation: Validation for tropical and subtropical regions in Brazil","authors":"José Marcelo Lopes Júnior ,&nbsp;Juan Carlos Ceballos ,&nbsp;Simone Marilene Sievert da Costa ,&nbsp;Francisco Luiz Leitão de Mesquita ,&nbsp;Hallan Souza de Jesus ,&nbsp;André Rodrigues Gonçalves","doi":"10.1016/j.jastp.2025.106512","DOIUrl":"10.1016/j.jastp.2025.106512","url":null,"abstract":"<div><div>Downward longwave radiation is a crucial component of the surface radiation balance and is essential for a better understanding of the greenhouse effect. It is strongly influenced by atmospheric temperature, water vapor content, and cloudiness. This study proposes a new model to estimate downward longwave radiation under cloudy sky conditions, suitable for the application of remote sensing data. The Santa Barbara DISORT Atmospheric Radiative Transfer code was employed to develop parameterizations for cloud and atmospheric processes. The simulations indicated that the emissivity of a stratiform cloud when the cloud is thicker than 300 m (ε = 0.985) approaches that of a blackbody. It is shown that atmospheric emissivity and transmittance can be parameterized as functions of precipitable water content below cloud base. The model was validated using observational data from six different sites in Brazil. The parameterizations of primary model variables (atmospheric layer emissivity, atmospheric transmittance, and precipitable water below the cloud base) generally showed good agreement with radiosonde data. The parameterization of precipitable water in the sub-cloud atmosphere yielded good results, with a maximum RMSE of 0.44 g cm⁻². The developed model presents an average overestimation of only 14 W m⁻², corresponding to a percentage error of 3.5 %, and an overall average RMSE of 18.3 W m⁻² (4.6 %). It shows satisfactory performance and is suitable for application in tropical and subtropical regions, as the underlying physics of this method is consistent worldwide.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106512"},"PeriodicalIF":1.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing temperature and water vapor in the atmospheric column over South America: a synopsis of identified trends using ERA5 reanalysis","authors":"Anna Carolina Bazzanela,&nbsp;Wanderson Luiz-Silva,&nbsp;Juan Neres,&nbsp;José Ricardo França,&nbsp;Lucas Menezes,&nbsp;Fabricio Polifke","doi":"10.1016/j.jastp.2025.106514","DOIUrl":"10.1016/j.jastp.2025.106514","url":null,"abstract":"<div><div>Climate change is already a reality globally. Several studies have quantified how these alterations affect specific energy budget components, such as radiative fluxes, heat storage, and energy transfer processes, correlating with verified modifications in several climate variables. Given the crucial role of thermodynamic processes in the hydrological cycle, this paper aims to provide a comprehensive overview of the climatology of air temperature and water vapor throughout the atmospheric column across South America. Moreover, detected trends in these variables are analyzed in the present climate using reanalysis data, focusing on the regions defined by the IPCC's Sixth Assessment Report (AR6). The results reveal an increasing air temperature trend at all levels of the atmosphere over the continent. The highest rates of air temperature rise are in the central portion of South America, while at high levels, the rate of warming is more significant in the northern part of the continent. There is an increasing trend in water vapor throughout the atmospheric column, particularly in the northern sector of South America. The identified decreasing trends of water vapor or specific humidity in the southeastern region do not exhibit a statistically significant signal. Such observed changes cause alterations in global and regional circulation patterns, mainly associated with extreme events.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106514"},"PeriodicalIF":1.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of atmospheric aerosols on air quality of three most polluted cities of Uttar Pradesh, India","authors":"Khushaboo Singh ,&nbsp;Jaswant Singh ,&nbsp;Suresh Kumar","doi":"10.1016/j.jastp.2025.106516","DOIUrl":"10.1016/j.jastp.2025.106516","url":null,"abstract":"<div><div>In recent decades, atmospheric aerosol/particulate matter has emerged as a central theme of increasing levels of air pollution around the world, especially developing nations like India. As per World Air Quality Report 2022, 14 of the 50 most polluted cities around the globe are from Uttar Pradesh, India. The availability of data on air quality in the most polluted cities of Uttar Pradesh is not much clear. Therefore, in the present study the data about air pollutants and meteorological parameters from Lucknow, Varanasi and Prayagraj for the year 2023 have been taken from the portal of the Central Pollution Control Board and analyzed. The outcomes show that monthly and annual concentrations of PM₁₀ &amp; PM_2.5 were found to be higher in Lucknow and lowest concentrations in Varanasi during the study period. Among the all-selected cities, the highest AQI value has been noticed in Poor category (&gt;250) in Lucknow and lowest for Good class (&lt;50) in Varanasi. The poor category of AQI has well known for prompting serious health effects on humans and proficient of causing serious respiratory, lung and heart illnesses. The highest wind speed, vertical wind speed, temperature and lowest humidity are conductive to dispersion of atmospheric pollutants in Varanasi and the lowest wind speed and temperature and higher humidity are not conductive to vertical and horizontal diffusion of air pollutants, resulting in higher concentrations of atmospheric aerosols/particulate pollution was observed in Lucknow. Consequential, this study revealed that meteorological parameters have a tremendous role in the accumulation and dispersion of atmospheric pollutants. In addition, this study also suggests that due to long-term restrictions on anthropogenic activities in 2020 and the implementation of the National Clean Air Programme in 2019 may have also led to significant improvement in air quality in Varanasi and Prayagraj. However, due to the increasing pace of anthropogenic activities (urbanization, construction activities, industrialization and vehicular sources) has significantly deteriorated the air quality in Lucknow city. The overall outcome of quantifying interactions between meteorological conditions and aerosol/PM concentration will play an important role for future researchers to understand the variability of aerosol/PM and seek ways to minimize air pollution in all the selected cities.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106516"},"PeriodicalIF":1.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging machine learning for accurate PM2.5 concentration prediction in selected Nigerian locations","authors":"O.A. Falaiye ,&nbsp;O.F. Odubanjo ,&nbsp;M. Sanni","doi":"10.1016/j.jastp.2025.106515","DOIUrl":"10.1016/j.jastp.2025.106515","url":null,"abstract":"<div><div>Air pollution, particularly from fine particulate matter (PM_2.5), poses significant environmental and health threats. Accurately predicting PM_2.5 concentrations can greatly assist policymakers in developing effective mitigation strategies. This research evaluates the performance of four popular machine learning models—Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and Multiple Linear Regression (MLR)—in predicting PM_2.5 concentrations across several Nigerian cities: Abuja, Anyigba, Benin City, and Osogbo. The study utilized hourly PM_2.5 data from the Center for Atmospheric Research (CAR) Nigeria's Purple Air Real-Time Air Quality Sensors Network and meteorological data from the HelioClim website of solar radiation and meteorological data services. Evaluation metrics included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) to assess model performance. The results indicate that mean PM_2.5 concentrations varied by location, with Benin City recording the highest levels of 46.19 μg/m³ and Anyigba the lowest at 14.36 μg/m³; higher levels were observed in the dry season across all locations. MAE values ranged from 2.25 μg/m³ (RF in Anyigba) to 12.43 μg/m³ (MLR in Benin City). The RF model consistently outperformed the others, achieving the highest R² values (up to 0.89 in Anyigba) and the lowest RMSE (3.55 μg/m³ in Anyigba). In contrast, the GB model demonstrated moderate performance with R² values around 0.68, while the SVM model exhibited the lowest overall performance. T<strong>emperature has the highest average importance percentage across the selected locations, making it the best predictor.</strong> These findings underscore the effectiveness of the RF model for PM_2.5 prediction and suggest that future research should explore the incorporation of additional gaseous pollutants, such as O₃, NO₂, and SO₂, to enhance predictive capabilities.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106515"},"PeriodicalIF":1.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the performance of global precipitation products for hydrological modeling in the data-scarce morphologically complex central Himalayan region","authors":"Sneha Sandilya ,&nbsp;Sunayana Singh ,&nbsp;Sonu Kumar ,&nbsp;Jitendra Rajput","doi":"10.1016/j.jastp.2025.106513","DOIUrl":"10.1016/j.jastp.2025.106513","url":null,"abstract":"<div><div>Developing a hydrological model is challenging in ungauged river basins. Hydrological modeling requires historical precipitation estimates. Global precipitation products (GPPs) have equipped hydrologists with a significant resource for hydrological applications such as flood modeling and forecasting. This study compares the quality and hydrological utility of different GPPs, TRMM 3B42, TRMM 3B42RT, APHRODITE, and SM2RAIN in the Gandak river basin (Nepal and India). The study reveals that among the four GPPs, the SM2RAIN had the least average Root Mean Squared Error (RMSE) of 5.83 mm/day, and TRMM 3B42RT had the highest RMSE of 11 mm/day. When it comes to R-squared, SM2RAIN had the highest value (0.37), and TRMM 3B42 showed the lowest at 0.07. Similarly, Bias for SM2RAIN showed the most negligible average bias, which was −5.34 %, and TRMM 3B42 indicated the highest bias of 28.71 %. POD for SM2RAIN was the highest at 0.83 and the lowest for TRMM RT (0.67). Similarly, when a hydrological model using MIKE 11 NAM model was developed and calibrated with gauge data, TRMM 3B42 (daily), TRMM 3B42RT, and SM2RAIN, the NSE were calculated to be 0.74, 0.62, −0.25 and 0.74 respectively for the calibration period and 0.67, 0.51, 0.42 and 0.5, respectively for the validation period. The TRMM 3B42 RT data sets were not found to be suitable for hydrological modeling in the Gandak river basin as mostly the NSE value was found to be negative. The study reveals that the best-suited product among the four GPPs for hydrological simulations in the central Himalayan region is SM2RAIN, followed by the TRMM 3B42.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106513"},"PeriodicalIF":1.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solar terminator related variations in geomagnetic field","authors":"Alexey Andreyev ,&nbsp;Vyacheslav Somsikov ,&nbsp;Vitaliy Kapytin ,&nbsp;Yekaterina Chsherbulova","doi":"10.1016/j.jastp.2025.106517","DOIUrl":"10.1016/j.jastp.2025.106517","url":null,"abstract":"<div><div>The solar terminator is a global, regular source of disturbances in various parameters of the Earth's atmosphere. The main feature of the solar terminator is its stability and precise regularity. To estimate the regular effects from the solar terminator in geomagnetic data, an analysis of averaged variations in the geomagnetic field components was performed based on long magnetic field time-series over the past 24 years from a large number of geomagnetic observatories. It was demonstrated that in addition to quasi-semidiurnal oscillations, Sq variations of the geomagnetic field contain relatively weak regular oscillations with periods of less than an hour, precisely tied to the moment of sunrise and sunset (passage of the solar terminator), as well as the passage of the terminator through the magnetically conjugate point.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"271 ","pages":"Article 106517"},"PeriodicalIF":1.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848709","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}