Journal of Atmospheric and Solar-Terrestrial Physics最新文献

{"title":"Spatio-temporal characteristics of rainfall and drought conditions are using the different drought indices with geospatial approaches in Karnataka state","authors":"","doi":"10.1016/j.jastp.2024.106372","DOIUrl":"10.1016/j.jastp.2024.106372","url":null,"abstract":"<div><div>Karnataka state drought conditions are assessed via drought indices. Over the past several decades, several indices of drought (DI) have been developed and presented, although some of them are region-specific and have problems regarding their applicability to other climatic circumstances. Additionally, choosing the best DI time step to illustrate the drought condition is difficult because of the DIs' various time steps. The study compares the Standardized Precipitation Index (SPI), Statistical Z-Score, China Z-Index (CZI), Rainfall Anomaly Index (RAI), and Rainfall Departure (RD) to determine which one is most appropriate for the districts of the Karnataka state that are prone to both dry and rainy conditions. This study has pointed out that the best drought indices are SPI and RD, and the least accountable drought indice is China Z index. Droughts were most common in 22 districts in 2009, which represent around 70.97% of the state's landmass. In comparison with the rest of the districts, the Ramanagara district noticed the worst drought conditions in 2003, with rainfall reaching 92.47 mm and SPI -3.68, RAI-6.04, MCZI -2.39, and Z score −2.62. Overall, the results of the study will aid in the organization and improvement of drought, flood, agriculture, and water resource management approaches in the state.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531336","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":"Identification of the Driving factors impacts of Land Surface Albedo over Iran: An analysis with the MODIS data","authors":"","doi":"10.1016/j.jastp.2024.106378","DOIUrl":"10.1016/j.jastp.2024.106378","url":null,"abstract":"<div><div>Albedo is a key parameter in climatic research and depends on environmental and climatic factors. Modeling these factors greatly contributes to understanding environmental variations. To this end, the data of Land Surface Albedo, Land Surface Temperature (LST), Vegetation, Snow, Elevation, Slope, and Aspect of the MODIS sensor from 1/1/2001 to 30/12/2021 with a 1000-m spatial resolution were used. After pre-processing, monthly, seasonal, and annual albedo modeling was performed using multiple linear regression (MLR) in the highlands of Iran. The results of monthly modeling revealed the salient direct role of snow on the albedo of Iran's highlands in all months, except for July, August, and September. In these months, due to the lack of snow coverage and the fruiting of agricultural lands and gardens, the inverse role of vegetation on albedo variations is determining. Seasonal examinations also showed that snow plays a significant role on the albedo of Iran's highlands in winter, spring, and fall; however, vegetation has a determining role in the summer. The annual results indicated that snow, vegetation, elevation, slope, LST, and aspect, respectively, are the factors affecting albedo in the highlands of Iran. Furthermore, the role of snow, LST, and aspect is positive, while the role of vegetation, elevation, and slope is negative on albedo.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446064","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 comprehensive analysis of factors affecting GNSS observation noise","authors":"","doi":"10.1016/j.jastp.2024.106371","DOIUrl":"10.1016/j.jastp.2024.106371","url":null,"abstract":"<div><div>Observation noise is one of the most significant error sources in the Global Navigation Satellite System (GNSS). It can be influenced by various factors. Analyzing these factors is crucial for developing a stochastic model for GNSS navigation and positioning. This process ensures that the statistical properties of the observational data are accurately characterized, leading to more reliable and precise positioning results. Previous research has predominantly focused on code type and PPP techniques, often limited by the inability to separately assess observation types across different frequency bands due to ionospheric delay. If based on short baseline, these studies were generally constrained by limited experimental data. This study provides a detailed analysis of the affecting factor on observation noise, including elevation, SNR (signal-to-noise ratio), different receiver and antenna type, different GNSS system, and different frequency bands etc. In addition, environmental effects on observation noise are investigated by comparison between short baseline and zero baseline.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432232","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":"On the long-term stability of the association between foF2 and EUV solar proxies","authors":"","doi":"10.1016/j.jastp.2024.106363","DOIUrl":"10.1016/j.jastp.2024.106363","url":null,"abstract":"<div><div>Solar extreme ultraviolet (EUV) radiation is the main source of heating and ionization of the Earth's upper atmosphere, forcing most of this system's time variability, which in annual scales corresponds to the solar activity ∼11-year cycle. Due to the difficulties in obtaining solar EUV time series covering extended periods of time or during periods without measurements available, the use of solar EUV proxies became a solution. In the case of the ionosphere, and in particular the F2-layer critical frequency (foF2), in addition to the solar activity cycle variation, it may also exhibit the effect of long-term trend forcings, like the monotonous increasing greenhouse gas concentration since the industrial revolution. To accurately detect and measure this weak trend against the solar activity variability, it is crucial to account for the solar forced variation. Traditionally, it is modeled as a linear association between foF2 and a given solar EUV proxy. However, the stability of this association has become a controversial issue. It would be reasonable to assume, in turn, that if the ionospheric environment is undergoing a trend forced by a non-solar diver, like the greenhouse gas concentration increase, the relationship between foF2 and solar proxies may be affected, ceasing to be stable if this additional driver is not introduced in the modeled association. Using rolling regressions over the period 1960–2023 to analyze this stability, our results suggest that the issue may not only lie in the steady trend expected in foF2 from a non-solar source or the need to include terms in the simple linear regression commonly used, but also in the possible deviation of the different proxies from the 'true' EUV solar flux, which is the ultimate main driver of F2 region ionization, a deviation that has been intensifying over the last two decades. We assert that it is a deviation from the actual EUV behavior because the indices diverge from one another, something that should not occur if they all reflect the same solar EUV.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417525","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":"Speed and accuracy investigations of neural network algorithms for ionospheric modelling at an equatorial region","authors":"","doi":"10.1016/j.jastp.2024.106365","DOIUrl":"10.1016/j.jastp.2024.106365","url":null,"abstract":"<div><div>Neural networks are very efficient tools for modeling, including ionospheric modeling. The training algorithm is important for achieving the optimum performance of the trained network. This research is therefore meant to evaluate and compare the performances of ten neural network training algorithms based on their prediction accuracies, and the duration/times taken by each of the algorithms to establish the optimum result. The neural networks were trained using electron density measurements by Radio Occultation (RO) technique from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellites. Data for the period 2006 through 2021 was used. The networks were trained using about 2.9 million data points collected from the Kano region, Nigeria (5-degree rectangular region around geographic: 12.00° N, 8.59° E) after performing data quality control. The training algorithms considered in the work include: Bayesian Regularization (BR); Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFG); Conjugate Gradient with Powell/Beale (CGB); Fletcher-Reeves Conjugate Gradient (CGF); Gradient descent with momentum and adaptive learning rate (GDX); Levenberg Marquardt (LM); One Step Secant (OSS); Polak-Ribiére Conjugate Gradient (CGP); Resilient Backpropagation (RP); and Scaled Conjugate Gradient (SCG). The results showed that the BR and the LM algorithms gave the best performances in minimizing the errors of prediction (the mean RMSEs are respectively 112 and 114 <span><math><mrow><mo>×</mo><msup><mn>10</mn><mn>3</mn></msup></mrow></math></span> electrons/cm³), but the RP algorithm, which came third in terms of accuracy, was significantly faster than both the LM and BR algorithms. The worst-performing algorithm in terms of accuracy was the GDX algorithm, although it was the fastest algorithm. The BFG algorithm was the worst-performing algorithm in terms of a combination of speed and accuracy. The developed neural network model was validated using ionosonde electron density measurements obtained from Ilorin, Nigeria (geographic: 8.5° N, 4.5° E; geomagnetic: 1.8° S). A comparison of the neural network, the NeQuick, and the IRI model predictions relative to the ionosonde measurements indicate that the neural network model was the best-performing model; the NN model predictions minimized the mean absolute errors (MAEs) in ∼44% of 399 ionosonde profiles investigated, the IRI model did so in ∼32%, and the NeQuick did so in ∼24%. The MAEs of the NeQuick however exhibited the best (least) variance. In overall, the NN model gave the least (best) mean of the MAEs (∼73 <span>×</span> 10³ cm⁻³), compared to ∼82 × 10³ cm⁻³ given by both the NeQuick and the IRI models, further supporting the idea that neural networks are excellent for present-day ionospheric modeling.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417524","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":"Aerosol type classification and its temporal distribution in Kanpur using ground-based remote sensing","authors":"","doi":"10.1016/j.jastp.2024.106366","DOIUrl":"10.1016/j.jastp.2024.106366","url":null,"abstract":"<div><div>Based on AERONET version 3 level 2 inversion products, we classify aerosol types and investigate their temporal distribution in the atmosphere using particle linear depolarization ratio (PLDR) and single scattering albedo (SSA) at the wavelength of 1020 nm over Kanpur. It is for the first time over the North Indian region the work has been emphasized. The remarkable findings over Kanpur station indicate that SSA and coarse-mode particles in the atmosphere increased with increasing PLDR at 440, 675, 870, and 1020 nm wavelengths. It is observed in the 2-dimensional histogram that the rate of occurrence of aerosols is high when the fine mode fraction (FMF) is high and the dust ratio (R_{<strong>d</strong>}) is low. The atmosphere of Kanpur is partially influenced by dust-dominated mixture (DDM), pollution-dominated mixture (PDM), and pure dust (PD) with 53% whereas, the rest of the dust-free pollution aerosols are 47%. The annual mean occurrence rate for different aerosol types is 5% for Strongly Absorbing (SA), 20% for Moderately Absorbing (MA), 19% for Weakly Absorbing (WA), 3% for Non-Absorbing (NA), 27% for DDM, 22% for PDM, and 4% for PD, ranging from January 2001 to January 2022. There is a variation in the distribution of various types of pollution particles, which is influenced by the changing seasons. The rate of occurrence of dust-free pollution aerosols is 47%, mostly observed throughout the post-monsoon and winter seasons. The PLDR values in the atmosphere of Kanpur are almost balanced equally because it is affected by both (dust and dust-free) pollution aerosols and the changes can be seen due to the frequent occurrence of dust storms and anthropogenic activities.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442763","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":"Techno-economic analysis of solar, wind and biomass hybrid renewable energy systems in Bhorha village, India","authors":"","doi":"10.1016/j.jastp.2024.106362","DOIUrl":"10.1016/j.jastp.2024.106362","url":null,"abstract":"<div><div>The present study investigates the potential use of Hybrid Renewable Energy Systems (Solar photovoltaic, wind, biomass, and diesel), both with and without the inclusion of battery/supercapacitor storage in the Bhorha village, Bihar, India. A comprehensive assessment of different possible system configurations is conducted using hybrid optimization model for electric renewable (HOMER) software to determine the most economically viable and efficient system for the designated place. The current analysis is focused on six distinct cases in the village community, in view of sufficing the daily energy requirement of 615.625 kWh and a peak demand of 86.47 kW, pertaining to major factors viz. system efficiency, financial viability, and ecological consequences. The primary aim of the research is to elucidate the comparative analysis of energy generation for different proposed designs of hybrid renewable energy systems. Detailed techno-commercial assessments are also carried out to examine the energy production, consumption, and financial impacts of each HRES configuration. The research outcome of this study obtained from HOMER software reveals that the optimized hybrid system comprises 86.7 kW solar photovoltaic, 30 kW wind turbine, 5 kW biogas generator, a 50 kW diesel generator, 280 kWh battery bank with nominal capacity, and 38.8 kW converter to sustain the for energy needs of the nominated place. This system has a minimum cost of energy of 0.309 $/kWh with a net present cost of $854894 along with operating cost 51847 $/year and net carbon dioxide emission of 56728 kg/yr. The research offers useful insights for designers, scholars, and policymakers on the existing design constraints and policies of biomass-based hybrid systems for a safe, sustainable and independent green future for the generations to come.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417522","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":"Long-term geomagnetic activities and stratospheric winter temperature","authors":"","doi":"10.1016/j.jastp.2024.106361","DOIUrl":"10.1016/j.jastp.2024.106361","url":null,"abstract":"<div><div>In this research, impact of long term solar forcing on stratospheric winter temperature is checked. The 11- year sunspot activity and geomagnetic indices (AE, Kp, Dst) are used as an indicator for solar forcing, as geomagnetic activity indices show good correlation with solar variability. To understand the impact of solar forcing through high latitude, stratospheric winter (November to March) time North Polar Region (60N–90N) temperature anomalies are considered. The findings showed that temperature changes in the stratosphere are significantly correlated with solar activity, as evidenced by a significant positive correlation between the 11-year moving mean of stratospheric (10 hPa) temperature anomalies and sunspot number. Approximately from 1970 to 2000, the North Polar Region saw positive anomalous stratospheric winter temperatures. During the same time, the geomagnetic activity also showed a substantial increase. The year-to-year correlation between stratospheric pole temperature and geomagnetic activity is significant (about 0.5). The Empirical Mode Decomposition analysis reveals a highly significant correlation (around 0.9) between the long-term component of stratospheric winter temperature (IMF-4) and the long-term component of geomagnetic activity (IMF-3 and IMF-4). One of the reasons for the increase in lower stratospheric temperature is an increase in ozone concentration during the same period when geomagnetic activity is higher. Empirical orthogonal function (EOF) and correlation analysis of stratospheric winter temperature with large-scale circulation patterns are also carried out. The spatial correlation is checked for stratospheric winter temperature at North Pole and lower atmospheric levels (250 hPa and 850 hPa) followed by pre-monsoon and monsoon season. This study includes statistical analysis, however, also highlights the necessity of in-depth dynamical analysis to improve our understanding of how solar activity impacts Earth's atmospheric layers, which may be helpful in predicting the weather and climate.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417523","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":"Analysis of LST, NDVI, and UHI patterns for urban climate using Landsat-9 satellite data in Delhi","authors":"","doi":"10.1016/j.jastp.2024.106359","DOIUrl":"10.1016/j.jastp.2024.106359","url":null,"abstract":"<div><div>The present study is based on remote sensing techniques focusing on Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to investigate their influence on land use and land cover dynamics, and the assessment of the Urban Heat Island (UHI) effect in Delhi, India. The objective of this study is to calculate LST, NDVI, and UHI values to understand the changes in LULC patterns, urbanization, and temperature increase within the city. Unlike previous studies conducted with Landsat-8, the present study employs Landsat-9 data, ensuring a higher level of authenticity in the results. Landsat-9, equipped with state-of-the-art sensors and instrumentation, provides superior data quality, enhanced image resolution, and advanced capabilities for precise monitoring and analysis. The methodology encompasses six steps for LST retrieval, enabling the calculation of UHI values and intensity. Ground data from 32 meteorological stations validate the LST results. Pearson correlation coefficients between LST and NDVI exhibit correlations ranging from −0.58 to −0.68 for three dates. On Dec 8, 2023, there is a weak negative correlation of −0.004. The analysis of changing land cover with variation in NDVI and LST unveils a diverse landscape, primarily characterised by green cover (47.34%), followed by built-up area (44.57%), barren land (7.57%), and water (0.52%). The study identifies the minimum value of UHI intensity for Delhi to be 8.13 °C on 26-Feb 2023 and the maximum value of UHI was estimated 10.29 °C on 2-June 2023. The study of Urban Heat Island (UHI) patterns revealed distinctive seasonal trends. The urban areas exhibited relatively cooler temperatures compared to surrounding rural regions on Dec 8, 2023. The conclusion drawn from this comprehensive analysis is that rapid urbanization in Delhi has significantly contributed to the increase in LST and UHI values. This rise can largely be attributed to the extensive use of concrete in construction activities, which exacerbates the UHI effect. Moreover, this analysis signifies the dynamic nature of UHI and emphasizes the urgency for strategic urban planning and climate-sensitive design approaches. Implementing such measures can create more sustainable and resilient urban environments.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417203","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":"Unveiling the impact of cosmic rays and solar activities on climate through optimized boost algorithms","authors":"","doi":"10.1016/j.jastp.2024.106360","DOIUrl":"10.1016/j.jastp.2024.106360","url":null,"abstract":"<div><div>This investigation explores the enhancement of climate anomaly predictions by incorporating Solar Sunspot Number (SSN) and Cosmic Ray (CR) data into climate models. Leveraging XGBoost and CatBoost regression methodologies enhanced by Atom Search Optimization (ASO) and Nuclear Reaction Optimization (NRO) for predictive analysis. Utilizing a dataset spanning from 1965 to 2020, comprising 672 data points per climate parameter, the study delves into the dynamics between CR flux, SSN variability, and climate parameters. The models aimed to forecast variations in total precipitation anomaly (TPA), total cloud cover anomaly (TCCA), and sea surface temperature anomaly (SSTA) based on decadal solar cycle activities and CR data. Our findings reveal the significant impact of integrating SSN and CR data into environmental prediction models for TCCA, TPA, and SSTA, employing CatBoost and XGBoost machine learning (ML) algorithms. Performance evaluation, centered on root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and Nash-Sutcliffe efficiency (NSE), illuminated the efficacy of ASO and NRO in model optimization, particularly under scenarios with and without SSN/CR data inclusion. The analytical outcomes underscore the enhanced prediction accuracy for TCCA, TPA, and SSTA when incorporating SSN and CR data, with ASO generally outperforming NRO in optimizing model parameters. Our regression models, optimized using ASO and NRO, showed a marked improvement in SSTA forecasts, with an increase in the R² value from 0.73 to 0.76 when SSN/CR data were not included. The CatBoost was superior the XGBoost models with results of four error metrics. These results underscore the critical role of solar activity data and optimized algorithms in enhancing the accuracy and reliability of climate modeling. This study underscores the utility of advanced ML techniques and the importance of strategic variable selection in environmental modeling, offering new insights into the complex interactions between solar activity, CR, and climate dynamics.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416990","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}