Acta Geophysica最新文献

{"title":"Spatiotemporal distribution characteristics and driving mechanisms of geoelectric field in lakeside and arid regions","authors":"Junsong Sun,&nbsp;Qing Ye,&nbsp;Yanping Niu,&nbsp;Yuanfu Zhang,&nbsp;Yu Li,&nbsp;Li Wang,&nbsp;Yuan Hu,&nbsp;Xiao Wang,&nbsp;Gaochuan Liu","doi":"10.1007/s11600-026-02051-8","DOIUrl":"10.1007/s11600-026-02051-8","url":null,"abstract":"<div><p>The geoelectric field is sensitive to both external electromagnetic disturbances and subsurface electrical structures. To investigate its spatial and temporal variations under contrasting environmental conditions, multi-electrode systems were conducted at Gaotai Station in the arid region of Gansu and Gaoyou Station near Gaoyou Lake in Jiangsu. Geoelectric field variations in eight azimuths were analyzed using time-series comparison, Welch power spectral density estimation, and omnidirectional spatial analysis. The results show that both stations respond strongly to geomagnetic disturbances, with substantially enhanced fluctuations during the 10–11 May 2024 geomagnetic storm. Under geomagnetically quiet conditions, Gaoyou exhibits larger diurnal variations than Gaotai, with amplitudes of approximately 50–100 mV/km and 5–20 mV/km, respectively. Spectral analysis identifies dominant periods near 8, 12, and 24 h, with 12 and 24 h components more prominent at Gaoyou and the 8 h component more evident at Gaotai. Clear differences among azimuthal channels are also observed, indicating spatial heterogeneity and directional dependence of the geoelectric field. These differences are likely related to the combined effects of external electromagnetic forcing, frequency-dependent induction, and regional electrical structures. The results demonstrate that multi-directional observations can effectively characterize small-scale variations in natural geoelectric fields and provide useful constraints for distinguishing regional environmental effects from externally driven electromagnetic signals.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132185","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":"Experimental study of scour downstream of a stilling basin under free and submerged inclined wall jets","authors":"Md Shaheer Ali,&nbsp;Zulfequar Ahmad","doi":"10.1007/s11600-026-02037-6","DOIUrl":"10.1007/s11600-026-02037-6","url":null,"abstract":"<div><p>Scour downstream of stilling basins caused by wall jets is a major concern for the safety and stability of hydraulic structures. While horizontal wall jets have been widely investigated, the scour behavior of inclined wall jets has received limited attention. This study experimentally examined the flow and scour characteristics of free and submerged inclined wall jets through laboratory experiments conducted under varying sluice openings, discharges, and tailwater depths. Flow measurements were measured using an Acoustic Doppler Velocimeter, and scour characteristics was monitored with an ultrasonic bed profiler. The results showed that free jets generated intense turbulence and severe scour, whereas high tailwater results in formation of a hydraulic jump on the stilling basin and energy dissipation. This caused reduction in the jet velocity by approximately 87%, maximum scour depth by up to 96%, and scour volume by nearly 90%. Self-similarity was obtained in the equilibrium scour profiles of both free and submerged jets, and regression-based equations for the prediction of equilibrium scour profiles were proposed. In addition, a nonlinear regression equation was developed for the computation of maximum scour depth incorporating the densimetric Froude number, relative tailwater depth, and relative stilling basin length with good accuracy to provide a practical tool for scour prediction and hydraulic design of hydraulic structures.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132186","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":"Experimental study of scour downstream of a stilling basin under free and submerged inclined wall jets","authors":"Md Shaheer Ali,&nbsp;Zulfequar Ahmad","doi":"10.1007/s11600-026-02037-6","DOIUrl":"10.1007/s11600-026-02037-6","url":null,"abstract":"<div><p>Scour downstream of stilling basins caused by wall jets is a major concern for the safety and stability of hydraulic structures. While horizontal wall jets have been widely investigated, the scour behavior of inclined wall jets has received limited attention. This study experimentally examined the flow and scour characteristics of free and submerged inclined wall jets through laboratory experiments conducted under varying sluice openings, discharges, and tailwater depths. Flow measurements were measured using an Acoustic Doppler Velocimeter, and scour characteristics was monitored with an ultrasonic bed profiler. The results showed that free jets generated intense turbulence and severe scour, whereas high tailwater results in formation of a hydraulic jump on the stilling basin and energy dissipation. This caused reduction in the jet velocity by approximately 87%, maximum scour depth by up to 96%, and scour volume by nearly 90%. Self-similarity was obtained in the equilibrium scour profiles of both free and submerged jets, and regression-based equations for the prediction of equilibrium scour profiles were proposed. In addition, a nonlinear regression equation was developed for the computation of maximum scour depth incorporating the densimetric Froude number, relative tailwater depth, and relative stilling basin length with good accuracy to provide a practical tool for scour prediction and hydraulic design of hydraulic structures.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132411","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":"Spatiotemporal distribution characteristics and driving mechanisms of geoelectric field in lakeside and arid regions","authors":"Junsong Sun,&nbsp;Qing Ye,&nbsp;Yanping Niu,&nbsp;Yuanfu Zhang,&nbsp;Yu Li,&nbsp;Li Wang,&nbsp;Yuan Hu,&nbsp;Xiao Wang,&nbsp;Gaochuan Liu","doi":"10.1007/s11600-026-02051-8","DOIUrl":"10.1007/s11600-026-02051-8","url":null,"abstract":"<div><p>The geoelectric field is sensitive to both external electromagnetic disturbances and subsurface electrical structures. To investigate its spatial and temporal variations under contrasting environmental conditions, multi-electrode systems were conducted at Gaotai Station in the arid region of Gansu and Gaoyou Station near Gaoyou Lake in Jiangsu. Geoelectric field variations in eight azimuths were analyzed using time-series comparison, Welch power spectral density estimation, and omnidirectional spatial analysis. The results show that both stations respond strongly to geomagnetic disturbances, with substantially enhanced fluctuations during the 10–11 May 2024 geomagnetic storm. Under geomagnetically quiet conditions, Gaoyou exhibits larger diurnal variations than Gaotai, with amplitudes of approximately 50–100 mV/km and 5–20 mV/km, respectively. Spectral analysis identifies dominant periods near 8, 12, and 24 h, with 12 and 24 h components more prominent at Gaoyou and the 8 h component more evident at Gaotai. Clear differences among azimuthal channels are also observed, indicating spatial heterogeneity and directional dependence of the geoelectric field. These differences are likely related to the combined effects of external electromagnetic forcing, frequency-dependent induction, and regional electrical structures. The results demonstrate that multi-directional observations can effectively characterize small-scale variations in natural geoelectric fields and provide useful constraints for distinguishing regional environmental effects from externally driven electromagnetic signals.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132388","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":"Comparative machine-learning-based regional flood frequency analysis for flood quantile estimation","authors":"Zainab Hussain,&nbsp;Jianyi Lin,&nbsp;Mohammed M. A. Almazah,&nbsp;Ijaz Hussain,&nbsp;Shreefa O. Hilali,&nbsp;A. Y. Al-Rezami","doi":"10.1007/s11600-026-02027-8","DOIUrl":"10.1007/s11600-026-02027-8","url":null,"abstract":"<div><p>Reliable flood quantile estimation is essential for Regional Flood Frequency Analysis (RFFA), especially in data-scarce and flood-prone mountainous regions. A flood quantile represents the magnitude of a design flood associated with a specified return period; in this study, Q20 and Q80 denote the 20-year and 80-year flood quantiles, respectively. This study develops a machine-learning-based Regional Flood Frequency Analysis (RFFA) framework. Daily meteorological records from 28 stations spanning 1981–2022 were used, sourced from the NASA POWER database. Three predictors were selected: mean annual temperature, relative humidity, and L1. Here, L1 represents the first L-moment of the annual maximum precipitation series and describes the station-level average magnitude of extreme precipitation. Four models were compared: Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and Stacking Ensemble (SE). The Stacking Ensemble model was developed as a two-layer framework in which DT, RF, and SVR were used as base learners to capture threshold-based, ensemble-tree, and kernel-based non-linear relationships, while Lasso regression was used as the regularised meta-learner to combine their predictions and reduce the risk of overfitting. The models were trained and tested using a 70/30 split, with five-fold cross-validation applied during model tuning. Model performance was assessed using MAPE, MAE, RMSE, RRMSE, RMSNE, Bias, R², NSE, and KGE, and further evaluated using ANOVA, paired t-tests, and Taylor diagrams. The results showed that SVR produced the most consistent overall performance. For Q20, SVR achieved MAPE = 3.72%, RMSE = 2.86, NSE = 0.991 and KGE = 0.964. For Q80, it achieved RMSE = 9.21, NSE = 0.969 and KGE = 0.959. ANOVA and Taylor diagrams confirmed that SVR showed stronger overall agreement with observed flood quantiles, while SE was competitive for Q80 but not consistently superior. These findings provide researchers with a validated comparative framework for ML-based RFFA and offer policymakers practical evidence for selecting suitable flood-quantile models to support flood-risk assessment, infrastructure design, and disaster-preparedness planning in data-limited regions.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132184","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":"Comparative machine-learning-based regional flood frequency analysis for flood quantile estimation","authors":"Zainab Hussain,&nbsp;Jianyi Lin,&nbsp;Mohammed M. A. Almazah,&nbsp;Ijaz Hussain,&nbsp;Shreefa O. Hilali,&nbsp;A. Y. Al-Rezami","doi":"10.1007/s11600-026-02027-8","DOIUrl":"10.1007/s11600-026-02027-8","url":null,"abstract":"<div><p>Reliable flood quantile estimation is essential for Regional Flood Frequency Analysis (RFFA), especially in data-scarce and flood-prone mountainous regions. A flood quantile represents the magnitude of a design flood associated with a specified return period; in this study, Q20 and Q80 denote the 20-year and 80-year flood quantiles, respectively. This study develops a machine-learning-based Regional Flood Frequency Analysis (RFFA) framework. Daily meteorological records from 28 stations spanning 1981–2022 were used, sourced from the NASA POWER database. Three predictors were selected: mean annual temperature, relative humidity, and L1. Here, L1 represents the first L-moment of the annual maximum precipitation series and describes the station-level average magnitude of extreme precipitation. Four models were compared: Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and Stacking Ensemble (SE). The Stacking Ensemble model was developed as a two-layer framework in which DT, RF, and SVR were used as base learners to capture threshold-based, ensemble-tree, and kernel-based non-linear relationships, while Lasso regression was used as the regularised meta-learner to combine their predictions and reduce the risk of overfitting. The models were trained and tested using a 70/30 split, with five-fold cross-validation applied during model tuning. Model performance was assessed using MAPE, MAE, RMSE, RRMSE, RMSNE, Bias, R², NSE, and KGE, and further evaluated using ANOVA, paired t-tests, and Taylor diagrams. The results showed that SVR produced the most consistent overall performance. For Q20, SVR achieved MAPE = 3.72%, RMSE = 2.86, NSE = 0.991 and KGE = 0.964. For Q80, it achieved RMSE = 9.21, NSE = 0.969 and KGE = 0.959. ANOVA and Taylor diagrams confirmed that SVR showed stronger overall agreement with observed flood quantiles, while SE was competitive for Q80 but not consistently superior. These findings provide researchers with a validated comparative framework for ML-based RFFA and offer policymakers practical evidence for selecting suitable flood-quantile models to support flood-risk assessment, infrastructure design, and disaster-preparedness planning in data-limited regions.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132387","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":"Geostatistical characterization of higher-order statistics in simulated data of precipitation under global climate models","authors":"Muhammad Abdullah Arshad,&nbsp;Zulfiqar Ali","doi":"10.1007/s11600-026-02049-2","DOIUrl":"10.1007/s11600-026-02049-2","url":null,"abstract":"<div><p>Global warming and climate change have increased the frequency and severity of hydroclimatic extremes, with drought remaining one of the most persistent threats to environmental sustainability and water security. Since precipitation largely controls the onset, persistence, and intensity of drought conditions, improving its statistical characterization is essential for understanding hydroclimatic variability and evaluating climate model performance. Although Global Climate Models (GCMs) are widely used to simulate precipitation under changing climatic conditions, their evaluation has predominantly relied on lower-order statistical measures, whereas higher-order moments that characterize the distribution of precipitation, particularly kurtosis, have received comparatively little attention. This study investigates the spatial variability of precipitation kurtosis derived from 22 CMIP6 GCMs over the Tibetan Plateau using monthly precipitation records from 1961 to 2014 at 95 locations. An observation-based precipitation dataset was incorporated as a reference for evaluating the spatial characteristics of GCM-derived kurtosis. Spatial dependence was characterized using the robust Cressie–Hawkins variogram estimator, and six theoretical variogram models were evaluated through leave-one-out cross-validation (LOOCV) before being incorporated into Simple, Ordinary, and Universal Kriging for spatial interpolation. The kriging approaches were comparatively evaluated LOOCV. The results reveal substantial inter-model differences in the spatial dependence of precipitation kurtosis, indicating contrasting representations of precipitation distributional characteristics among the selected GCMs. Among the evaluated kriging approaches, Ordinary Kriging generally provided comparatively lower prediction errors for the assessed GCMs. The optimized variogram models successfully captured the underlying spatial structure and produced coherent interpolation surfaces with spatially varying interpolation uncertainty. These findings provide an additional statistical perspective for climate model evaluation and may support future investigations of precipitation-based drought assessment, climate risk analysis, and ensemble weighting strategies.\u0000</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133170","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":"Evaluation of the performance of the Weather Research and Forecasting model in simulating surface solar radiation over Penang Island, Malaysia, through comparing with reanalysis and in situ data","authors":"Ninu Krishnan Modon Valappil,&nbsp;Liu Jiaoying,&nbsp;Abigail Birago Adomako,&nbsp;Ehsan Jolous Jamshidi,&nbsp;Yusri Yusup","doi":"10.1007/s11600-026-02035-8","DOIUrl":"10.1007/s11600-026-02035-8","url":null,"abstract":"<div><p>The accurate assessment of surface solar radiation (SSR) is crucial for various applications, including solar energy planning, climate modeling, and environmental management, especially in tropical coastal areas where cloud variability impacts radiation budgets. The present research investigates SSR from four distinct sources: high-resolution Weather Research and Forecasting (WRF) model simulations, ground-based measurements (Muka Head station—MKH and Bayan Lepas station—BLE), and two global reanalysis products (ERA5 and NASA POWER) on Penang Island, Malaysia, throughout the year 2022. Hourly SSR data were analyzed using statistical methods, correlation analysis, and error metrics, which include Mean Bias Error (MBE), Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). All datasets effectively captured the diurnal cycle of SSR, which is characterized by a swift increase in the morning, a notable peak at midday, and a gradual decline in the evening. Strong correlations (exceeding 0.85) among the WRF model, reanalysis products, and ground observations validate their capacity to represent temporal variations. Nevertheless, differences in magnitude were observed, with the WRF model consistently yielding higher SSR estimates than ground-based measurements and reanalysis datasets, indicating a systematic positive bias, particularly during inter-transitional months, when cloud dynamics are intricate. ERA5 and NASA POWER generally provided intermediate SSR estimates, with NASA POWER aligning most closely with ground-based measurements. Monthly analysis indicated a double-peaked SSR pattern, with peaks occurring during the transitional phases of wind (February–March and September–October). Site-specific factors, such as coastal topography and urban heat effects, affected dataset performance, revealing larger biases at the more complex MKH station compared to the flatter, urbanized BLE station. In summary, although the WRF model provides significant high-resolution spatial insights, the ERA5 and NASA POWER datasets deliver dependable large-scale representations. The integration of these datasets through methods such as bias correction or hybrid modeling has the potential to improve SSR estimation for the assessment of energy resources and climate-related applications in Southeast Asia.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133169","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":"Geostatistical characterization of higher-order statistics in simulated data of precipitation under global climate models","authors":"Muhammad Abdullah Arshad,&nbsp;Zulfiqar Ali","doi":"10.1007/s11600-026-02049-2","DOIUrl":"10.1007/s11600-026-02049-2","url":null,"abstract":"<div><p>Global warming and climate change have increased the frequency and severity of hydroclimatic extremes, with drought remaining one of the most persistent threats to environmental sustainability and water security. Since precipitation largely controls the onset, persistence, and intensity of drought conditions, improving its statistical characterization is essential for understanding hydroclimatic variability and evaluating climate model performance. Although Global Climate Models (GCMs) are widely used to simulate precipitation under changing climatic conditions, their evaluation has predominantly relied on lower-order statistical measures, whereas higher-order moments that characterize the distribution of precipitation, particularly kurtosis, have received comparatively little attention. This study investigates the spatial variability of precipitation kurtosis derived from 22 CMIP6 GCMs over the Tibetan Plateau using monthly precipitation records from 1961 to 2014 at 95 locations. An observation-based precipitation dataset was incorporated as a reference for evaluating the spatial characteristics of GCM-derived kurtosis. Spatial dependence was characterized using the robust Cressie–Hawkins variogram estimator, and six theoretical variogram models were evaluated through leave-one-out cross-validation (LOOCV) before being incorporated into Simple, Ordinary, and Universal Kriging for spatial interpolation. The kriging approaches were comparatively evaluated LOOCV. The results reveal substantial inter-model differences in the spatial dependence of precipitation kurtosis, indicating contrasting representations of precipitation distributional characteristics among the selected GCMs. Among the evaluated kriging approaches, Ordinary Kriging generally provided comparatively lower prediction errors for the assessed GCMs. The optimized variogram models successfully captured the underlying spatial structure and produced coherent interpolation surfaces with spatially varying interpolation uncertainty. These findings provide an additional statistical perspective for climate model evaluation and may support future investigations of precipitation-based drought assessment, climate risk analysis, and ensemble weighting strategies.\u0000</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133227","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":"Evaluation of the performance of the Weather Research and Forecasting model in simulating surface solar radiation over Penang Island, Malaysia, through comparing with reanalysis and in situ data","authors":"Ninu Krishnan Modon Valappil,&nbsp;Liu Jiaoying,&nbsp;Abigail Birago Adomako,&nbsp;Ehsan Jolous Jamshidi,&nbsp;Yusri Yusup","doi":"10.1007/s11600-026-02035-8","DOIUrl":"10.1007/s11600-026-02035-8","url":null,"abstract":"<div><p>The accurate assessment of surface solar radiation (SSR) is crucial for various applications, including solar energy planning, climate modeling, and environmental management, especially in tropical coastal areas where cloud variability impacts radiation budgets. The present research investigates SSR from four distinct sources: high-resolution Weather Research and Forecasting (WRF) model simulations, ground-based measurements (Muka Head station—MKH and Bayan Lepas station—BLE), and two global reanalysis products (ERA5 and NASA POWER) on Penang Island, Malaysia, throughout the year 2022. Hourly SSR data were analyzed using statistical methods, correlation analysis, and error metrics, which include Mean Bias Error (MBE), Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). All datasets effectively captured the diurnal cycle of SSR, which is characterized by a swift increase in the morning, a notable peak at midday, and a gradual decline in the evening. Strong correlations (exceeding 0.85) among the WRF model, reanalysis products, and ground observations validate their capacity to represent temporal variations. Nevertheless, differences in magnitude were observed, with the WRF model consistently yielding higher SSR estimates than ground-based measurements and reanalysis datasets, indicating a systematic positive bias, particularly during inter-transitional months, when cloud dynamics are intricate. ERA5 and NASA POWER generally provided intermediate SSR estimates, with NASA POWER aligning most closely with ground-based measurements. Monthly analysis indicated a double-peaked SSR pattern, with peaks occurring during the transitional phases of wind (February–March and September–October). Site-specific factors, such as coastal topography and urban heat effects, affected dataset performance, revealing larger biases at the more complex MKH station compared to the flatter, urbanized BLE station. In summary, although the WRF model provides significant high-resolution spatial insights, the ERA5 and NASA POWER datasets deliver dependable large-scale representations. The integration of these datasets through methods such as bias correction or hybrid modeling has the potential to improve SSR estimation for the assessment of energy resources and climate-related applications in Southeast Asia.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133226","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}