Journal of Atmospheric and Solar-Terrestrial Physics最新文献

{"title":"Long-term air quality forecasting in Korba, India (2025–2047): A hybrid model using 44-year satellite data","authors":"Abhimanyu Kumar Gond,&nbsp;Aarif Jamal,&nbsp;Tarun Verma","doi":"10.1016/j.jastp.2026.106730","DOIUrl":"10.1016/j.jastp.2026.106730","url":null,"abstract":"<div><div>This study employs a hybrid Temporal Convolutional Network (TCN) and Transformer model to forecast air quality trends in Korba, Chhattisgarh, a critically polluted industrial hub dominated by extensive coal mining (including Gevra, Dipka, and Kusmunda mines), multiple thermal power plants, aluminium smelters, and cement production, from 2025 to 2047. Utilizing 44 years of satellite-derived data (1980–2024), the model integrates meteorological variables, vegetation indices (NDVI and NDBI), and coal mining metrics. Under a moderate policy change scenario, it predicts gradually rising pollutant levels: PM₁₀ (32–84 μg/m³), PM₂.₅ (10–33 μg/m³), SO₂ (7.25–12 μg/m³), NO₂ (5.25–9 μg/m³), and AQI (45–110, Moderate per CPCB Delhi standards), with seasonal patterns showing reduced concentrations during monsoon due to rainfall washout and elevated levels in summer and winter owing to limited atmospheric dispersion. The model demonstrated strong performance (R² = 0.75–0.91; RMSE = 1.09–11.91), effectively capturing both short- and long-term trends driven by industrial emissions and environmental factors. A sensitivity analysis further revealed the model's robust response to ±10–20 % variations in key drivers, with the most decisive influence from coal production increases, which could be elevated by 20–30 % and add 10–15 AQI points, while reduced rainfall, higher temperatures, and lower NDVI amplified dust resuspension and secondary pollutant formation. Uncertainty analysis identified high-risk periods, including elevated PM₂.₅ variability in 2025–2026 and AQI in 2033–2038. Validation against ground-truth data from Urja Nagar, Rampur stations, and MODIS Satellite-derived AQI (From January–September 2025; R²: 0.72, 0.61, and 0.58) confirmed forecasted AQI (30–164, mostly Moderate), posing potential respiratory risks to vulnerable groups upon prolonged exposure. These projections highlight escalating public health threats, particularly respiratory and cardiovascular diseases, underscoring the need for urgent interventions, such as stricter emission controls, a transition to renewable energy sources, the adoption of an air quality health index, and enhanced reforestation for dust mitigation. This work offers a robust, data-driven baseline and scalable framework for sustainable air quality management in industrial regions, aligning with India's vision for balanced development by 2047.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106730"},"PeriodicalIF":1.9,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979506","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 meteorological drought indices for monitoring agricultural drought using SPEI: a remote sensing approach","authors":"Kanhu Charan Panda ,&nbsp;Pradosh Kumar Paramaguru ,&nbsp;Ram Mandir Singh ,&nbsp;Sudhir Kumar Singh","doi":"10.1016/j.jastp.2026.106726","DOIUrl":"10.1016/j.jastp.2026.106726","url":null,"abstract":"<div><div>Estimating agricultural drought is hindered by limited data availability. Conversely, assessing meteorological drought is very easy as it requires only meteorological data that are easy to obtain. Thus, the study aimed to find meteorological drought indices suitable for assessing agricultural drought in a tropical river basin using remote sensing satellite data. Seven meteorological drought indices, i.e., Standardised Precipitation Index (SPI), Deciles Index (DI), Percent of Normal Index (PNI), Rainfall Anomaly Index (RAI), China-Z Index (CZI), Modified China-Z Index (MCZI), and Z-score Index (ZSI) were compared with an efficient agricultural drought index, viz., Standardised Precipitation Evapotranspiration Index (SPEI) for the Subarnarekha River basin using the NASA POWER satellite data. It was noticed that the CZI had the highest correlation with the 3-month SPEI (R² = 0.88–0.94, followed by the RAI (R² = 0.85–0.90). The meteorological drought indices were divided into three groups based on the ability to replicate SPEI: ‘excellent’ for SPI, CZI, and MCZI; ‘good’ for ZSI and RAI; and ‘poor’ for PNI and DI. Relying solely on correlation coefficients to evaluate drought indices provided erroneous results. However, integrating correlation, RMSE, and statistical distribution enhanced the robustness of the assessment. This approach reclassified MCZI and SPI from “good” to “excellent”. SPI and CZI showed better alignment with SPEI distributions throughout both dry and wet periods, establishing them as the most reliable alternatives for evaluating agricultural drought. The findings emphasize the importance of comprehensive evaluation criteria that incorporate several statistical metrics for reliable drought assessment in tropical and data-scarce regions.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106726"},"PeriodicalIF":1.9,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940740","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":"Forecasting seasonal surface ozone trends using radial basis function networks and ground level monitoring data","authors":"Sharanya Suraboyina ,&nbsp;Gangagni Rao Anupoju ,&nbsp;Anand Polumati","doi":"10.1016/j.jastp.2026.106725","DOIUrl":"10.1016/j.jastp.2026.106725","url":null,"abstract":"<div><div>Accurate forecasting of tropospheric surface ozone (O₃) concentrations is increasingly vital due to rapid urbanization and industrial growth, which have intensified photochemical smog episodes and associated health and environmental risks. Traditional deterministic models often struggle to capture the complex nonlinear relationships among meteorological and precursor variables influencing O₃ formation. Addressing this limitation, the present study introduces a Radial Basis Function Network (RBFN) model for seasonal forecasting of surface O₃ concentrations using real-time ground monitoring data (2014–2017) from an urban air-quality station in Hyderabad, India (TIFR–NBF: Tata Institute of Fundamental Research–National Balloon Facility). The novelty of this work lies in applying RBFNs to capture seasonal and diurnal O₃ variations with high precision, using input parameters including NO, NO₂, NO_x, CO, VOCs, and NMHCs. The RBFN model was trained with 80 % of the dataset and validated using 20 %, and its performance was assessed through Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Willmott's index(WI)Comparative analysis with the nonlinear regression-based Response Surface Methodology (RSM) revealed that the RBFN achieved superior predictive accuracy with lower RMSE (0.0038 vs. 0.0185), MSE (0.000014 vs. 0.000342), MAE (0.0021 vs. 0.0094), and (WI = 0.9999 vs. 0.9812), The model effectively captured seasonal and diurnal O₃ peaks, particularly during summer and pre-monsoon periods. Overall, this study demonstrates the robustness, adaptability, and real-time forecasting potential of RBFNs, underscoring their application in early-warning systems, air-quality management, and policy-driven pollution mitigation strategies for rapidly urbanizing regions.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106725"},"PeriodicalIF":1.9,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979914","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 spatiotemporal analysis of variation in soil moisture over Iran","authors":"Mohammad Darand ,&nbsp;Ramtin Tashan","doi":"10.1016/j.jastp.2026.106727","DOIUrl":"10.1016/j.jastp.2026.106727","url":null,"abstract":"<div><div>Soil moisture, as an important hydrological component, plays a crucial role in land–atmosphere interactions. Understanding variations in soil moisture content is highly valuable for effective water resource management, agricultural activities, and climate adaptation. The objective of this study is to analyze the spatiotemporal variations of surface soil moisture (0–7 cm) across Iran during the period 1979–2024. To achieve this, daily gridded data with a spatial resolution of 0.1° from the ERA5-Land dataset provided by ECMWF were used. The results showed that the spatial distribution pattern of soil moisture content follows the spatial patterns of precipitation, rainy days, and temperature across Iran. The modified Mann–Kendall test and Sen's slope estimator were applied to detect trends and their magnitudes at a 95 % confidence level. The findings indicated that soil moisture content across Iran has shown a decreasing trend, with an average reduction of 0.0032 m³ m⁻³ per decade. Temporally, the greatest reduction in soil moisture occurred during the cold and rainy seasons. Spatially, the decrease in soil moisture volume during winter was significantly higher in the northeastern part of the country compared to other regions. In some areas of the Alborz and Zagros highlands, however, soil moisture content exhibited an increasing trend. The findings of this study suggest that changes in soil moisture can be a potential predictor of climate change and can be applied to the fields of water resource management, agriculture, flood management, and hydrology.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106727"},"PeriodicalIF":1.9,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940742","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 note on effects of fossil fuel reduction policies on atmospheric carbon dioxide buildup and global warming","authors":"Baris Baykant Alagoz ,&nbsp;Cemal Keles ,&nbsp;Burhan Baran","doi":"10.1016/j.jastp.2026.106724","DOIUrl":"10.1016/j.jastp.2026.106724","url":null,"abstract":"<div><div>Global warming is a near-future major problem that has serious consequences on life on Earth. Accumulation of CO₂ emission in the atmosphere becomes a dominating factor in the global warming process. This study introduces a global temperature uptrend model that considers effects of atmospheric CO₂ buildup because of combustion of natural gas, oil and coal fuels. The model is based on earth's energy balance modeling and estimates impacts of fossil fuel-based CO₂ emission on the average global temperature. To evaluate effects of fossil fuel reduction policies, this model is used to study yearly 3 %, 5 % and 7 % fossil fuel reduction scenarios on 2013–2022 global fuel consumption data. This backward projection indicated that the cumulative carbon dioxide emission of 10-year global fuel consumption has potential of about +0.023 °C/y global temperature rise between 2013 and 2022 years. Moreover, this model is also used for forward projections to discuss the effects of these 10-year-long fossil fuel reduction programs on future trends of global temperature anomaly.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106724"},"PeriodicalIF":1.9,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940744","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":"Multifractal, recurrence, and information–Theoretic characterization of coupled proton dynamics in the solar wind","authors":"A.O. Adelakun","doi":"10.1016/j.jastp.2025.106713","DOIUrl":"10.1016/j.jastp.2025.106713","url":null,"abstract":"<div><div>This study applies a unified nonlinear framework–combining multifractal detrended fluctuation analysis (MF–DFA), recurrence quantification analysis (RQA), and transfer entropy (TE)–to investigate the dynamical coupling between proton density (PD), temperature (PT), and speed (PS) in the solar wind. The results reveal a consistent hierarchy across all diagnostics: temperature-speed coupling dominates (<span><math><mrow><mi>PT − PS</mi><mo>&gt;</mo><mi>PD − PS</mi><mo>&gt;</mo><mi>PD − PT</mi></mrow></math></span>), governing the principal pathway of energy redistribution. The PT–PS pair exhibits the broadest joint singularity spectrum and the strongest multifractal asymmetry, while recurrence metrics show sustained synchronization and structured determinism. Transfer–entropy analysis confirms a thermal–compressive <span><math><mo>→</mo></math></span> kinetic directionality, with PD and PT acting as information sources (<span><math><mrow><mi>PD</mi><mo>=</mo><mo>+</mo><mn>0</mn><mo>.</mo><mn>869</mn></mrow></math></span>, <span><math><mrow><mi>PT</mi><mo>=</mo><mo>+</mo><mn>0</mn><mo>.</mo><mn>457</mn></mrow></math></span>) and PS acting as the receiver (<span><math><mrow><mi>PS</mi><mo>=</mo><mo>−</mo><mn>1</mn><mo>.</mo><mn>326</mn></mrow></math></span>). These findings depict the solar wind as a self-organized, multiscale system in which intermittent synchronization between thermal and kinetic processes regulates flow variability. The integrated multifractal–recurrence–entropy framework provides a new diagnostic tool for identifying regime transitions and improving space-weather predictability.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106713"},"PeriodicalIF":1.9,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897868","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":"Geospatial time series forecasting of air quality across Delhi using ARIMA and LSTM models","authors":"Faizan Tahir Bahadur ,&nbsp;Shagoofta Rasool Shah ,&nbsp;Rama Rao Nidamanuri","doi":"10.1016/j.jastp.2026.106723","DOIUrl":"10.1016/j.jastp.2026.106723","url":null,"abstract":"<div><div>This study compared three forecasting approaches: Classical time-series, ARIMA variants, and Artificial Neural Network (ANN)-based Long Short-Term Memory (LSTM) models for PM2.5 prediction at two Delhi stations (Bawana and Okhla). All models produced strong forecasts and revealed consistently high predictive performance, with accuracy metrics and error values within acceptable ranges, R² values above 0.82 and RMSE between 15 and 30 μg/m³. Okhla generally outperformed Bawana, achieving the highest Classical model R² (0.957) and lowest RMSE (15.70 μg/m³). ARIMA delivered reliable results (Okhla: R² ≈ 0.913, RMSE ≈ 22.49 μg/m³), but performance declined at Bawana (R² ≈ 0.826, RMSE ≈ 29.18 μg/m³), highlighting its sensitivity to station-specific parameters and stationarity assumptions. LSTM architectures outperformed both classical and ARIMA models. ConvLSTM achieved the best overall accuracy (Okhla: R² = 0.891, RMSE = 20.10 μg/m³; Bawana: R² = 0.865, RMSE = 23.74 μg/m³), closely followed by Stacked LSTM (Okhla: R² = 0.889, RMSE = 20.78 μg/m³) and Bi-directional LSTM (Okhla: R² = 0.901, RMSE = 23.31 μg/m³). These models demonstrated stable convergence, minimal overfitting, and robustness against noise. While classical and ARIMA methods remain interpretable and computationally efficient, they struggle with complex nonlinear dependencies and require substantial reconfiguration for new sites. LSTM models, by contrast, adapt easily to other monitoring locations with minor hyperparameter tuning (e.g., learning rate, sequence length, layer depth), making them more scalable for city-wide deployment. In conclusion, although all approaches are viable for PM2.5 forecasting, LSTM-based models, particularly ConvLSTM and Stacked LSTM provide the highest accuracy, robustness, and adaptability, offering a reliable framework for operational air quality management in Delhi. From an applied perspective, the improved short-term predictability offered by LSTM-based models can support early-warning systems, targeted emission-control strategies, and data-driven decision-making for urban air-quality management.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106723"},"PeriodicalIF":1.9,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940741","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":"Integrating LGBM machine learning methods with SHAP model to explain the impact of different environmental factors on precipitation in China","authors":"Yong Liu ,&nbsp;Qingzu Luan ,&nbsp;Pengguo Zhao","doi":"10.1016/j.jastp.2025.106715","DOIUrl":"10.1016/j.jastp.2025.106715","url":null,"abstract":"<div><div>Precipitation processes are influenced by a combination of environmental variables, and it is crucial to identify the main contributing factors to rainfall. China has vast geographical diversity and complex terrain, with significant differences in precipitation formation mechanisms across climatic regions. Although machine learning models are efficient, they lack interpretability. Therefore, this study utilizes ground-based meteorological observation data, satellite remote sensing data, and atmospheric reanalysis data in conjunction with the explainable artificial intelligence (XAI) tool SHAP model combined with the Light Gradient Boosting Machine (LGBM) machine learning framework to investigate the impact of various environmental variables on precipitation in China and different climate regions, quantifying their contributions to precipitation. The results reveal that relative humidity (RH), Convective Available Potential Energy (CAPE), K index (KI), and ice water path (IWP) are the most critical factors influencing precipitation within China, ranking among the top four in terms of average SHAP values and significantly higher than other factors. Due to regional variations across different climate zones in China, land surface temperature (LST), wind direction (WD), evaporation (E), liquid water path (LWP), surface pressure (SP), cloud cover (CFC), aerosol optical depth (AOD), and relative humidity (RH) exert the most pronounced positive effects on precipitation at the national scale, while ice water path (IWP), K index (KI), cloud top height (CTH), and Convective Available Potential Energy (CAPE) demonstrate more significant negative impacts—stemming from the varying influences of each variable across different climate regions. Significant regional variations exist in precipitation drivers. CAPE shows stronger influence on precipitation in North Subtropical Humid climatic regions, while RH dominates in Marginal Tropical Humid and Mid-temperate Semi-humid zones. CTH is more pronounced in Plateau Temperate Semi-arid areas, IWP stands out in Mid-temperate Semi-arid climatic regions, KI predominates in Mid-temperate Arid areas, and LST plays a more significant role in Warm Temperate Semi-humid climatic regions.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106715"},"PeriodicalIF":1.9,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897867","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":"M3F-U-Net: A hybrid deep learning model for precipitation forecast correction","authors":"Tong Zhang ,&nbsp;Xing Wang ,&nbsp;Hao Li ,&nbsp;Qiliang Wu ,&nbsp;Dan Geng","doi":"10.1016/j.jastp.2025.106714","DOIUrl":"10.1016/j.jastp.2025.106714","url":null,"abstract":"<div><div>The accuracy of precipitation forecasting exerts significant impacts on socio-economic activities. However, numerical weather prediction (NWP) models often exhibit systematic biases due to the chaotic nature of atmospheric dynamics and localized topographic effects. This study proposes M³F-U-Net, a multi-stream multi-level multi-physics Fusion U-Net designed to decouple and reconstruct atmospheric dynamic, thermodynamic, moisture transport, and topographic forcing processes. A dynamic weighted fusion module is employed to adaptively adjust the importance of physical features, while a residual learning framework is incorporated to achieve precipitation forecast correction. Experiments based on ECMWF model forecasts and ERA5 reanalysis data demonstrate that the model reduces the Mean Squared Error (MSE) and Mean Absolute Error (MAE) by 20.04 % and 14.42 %, respectively. Notably, the Equitable Threat Score (ETS) improves by 6.19 % overall, driven by a significant reduction in false alarms. Subgroup analysis confirms the model's robustness across diverse terrains, achieving an 8.56 % ETS improvement even in complex mountainous regions. Furthermore, the model effectively mitigates nonlinear error accumulation in long-term forecasts (up to 72 h). This research establishes a physically consistent deep learning framework for mitigating systematic biases in NWP-based precipitation forecasts, providing an innovative technical paradigm for numerical model post-processing.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106714"},"PeriodicalIF":1.9,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940739","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":"Enhanced ground-based GNSS tomography for accurate water vapor retrieval","authors":"Xia Pengfei ,&nbsp;Zhang Weikang ,&nbsp;Shu Liang ,&nbsp;Guo Min","doi":"10.1016/j.jastp.2025.106709","DOIUrl":"10.1016/j.jastp.2025.106709","url":null,"abstract":"<div><div>Ground-based GNSS tomography-derived three-dimensional water vapor products exhibit significant potential to enhance weather forecast accuracy. However, their accuracy is constrained by key technical challenges such as the vertical constraint function, the vertical division of the tomographic voxel, and the determination of the voxel's top height. This paper aims to optimize GNSS water vapor tomography by constructing a vertical factor model for water vapor density based on ERA5 reanalysis data and proposing a new method for determining the top height of the tomographic voxel. Furthermore, in accordance with the characteristics of water vapor distribution, the vertical level of the tomographic voxel is divided into two regions for non-uniform and uniform partitioning. Using observation data from ten GNSS stations in Wuhan for trial calculations, and by statistically analyzing the inversion results for the entire year of 2023, it is found that the optimized tomography technique not only enhances the utilization rate of GNSS rays but also improves the accuracy of GNSS three-dimensional water vapor density by up to 30.2 % in spring, with an annual average improvement of 24.2 %, enhancing severe weather prediction and numerical modeling.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"278 ","pages":"Article 106709"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879943","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}