Search Results (1,065)

The growing penetration of distributed photovoltaic (PV) systems presents significant operational challenges for power grids, driven by the scarcity of historical data and the high spatiotemporal variability of PV generation. To address these challenges, we propose Generative Reconstruction and Adaptive Identification via Latents (GRAIL), a unified, end-to-end framework that integrates generative modeling with adaptive clustering to discover latent structures and representative scenarios in PV datasets. GRAIL operates through a closed-loop mechanism where clustering feedback guides a cluster-aware data generation process, and the resulting generative augmentation strengthens partitioning in the latent space. Evaluated on a real-world, multi-site PV dataset with a high missing data rate of $45.4\%$, GRAIL consistently outperforms both classical clustering algorithms and deep embedding-based methods. Specifically, GRAIL achieves a Silhouette Score of 0.969, a Calinski–Harabasz index exceeding $4.132\times {10}^6$, and a Davies–Bouldin index of $0.042$, demonstrating superior intra-cluster compactness and inter-cluster separation. The framework also yields a normalized entropy of $0.994$, which indicates highly balanced partitioning. These results underscore that coupling data generation with clustering is a powerful strategy for expressive and robust structure learning in data-sparse environments. Notably, GRAIL achieves significant performance gains over the strongest deep learning baseline that lacks a generative component, securing the highest composite score among all evaluated methods. The framework is also computationally efficient. Its alternating optimization converges rapidly, and clustering and reconstruction metrics stabilize within approximately six iterations. Beyond quantitative performance, GRAIL produces physically interpretable clusters that correspond to distinct weather-driven regimes and capture cross-site dependencies. These clusters serve as compact and robust state descriptors, valuable for downstream applications such as PV forecasting, dispatch optimization, and intelligent energy management in modern power systems. Full article

This study presents a machine learning framework for predicting traffic accident occurrence and congestion intensity using artificial neural networks (ANNs) trained on real-world traffic data collected from a central urban corridor in Egypt. The research aims to enhance proactive traffic management by providing reliable, data-driven forecasts derived from temporal and environmental road features. Sixty-seven traffic observations were recorded over three months, capturing variations across vehicle flow, speed, weather, holidays, and road conditions. Two predictive models were developed: a binary accident detection classifier and a multi-class congestion level estimation classifier. Both models employed Bayesian optimization for hyperparameter tuning and were evaluated under three validation strategies—5-fold cross-validation, 10-fold cross-validation, and resubstitution—combined with different train/test splits. The results demonstrated that the model using 10-fold cross-validation and a 75/25 split achieved the highest accuracy in accident prediction (93.8% on test data), with minimal variance between validation and testing phases. In contrast, resubstitution validation yielded artificially high training accuracy (up to 100%) but lower generalization performance, confirming overfitting risks. Congestion prediction showed similarly strong classification trends, with the optimized model effectively distinguishing between congestion levels under dynamic traffic conditions. These findings validate the use of ANN-based prediction in real-world traffic scenarios and highlight the critical role of validation design in developing robust forecasting models. The proposed approach holds promise for integrating intelligent transportation systems, enabling anticipatory interventions, and enhancing road safety. Full article

On September 7, 2024, a deep convection event was observed in Northern and Central Greece, and based on radar data analysis, three supercells were identified. One of these, the most intense with maximum radar reflectivity of 68 dBZ, had a lifetime of almost 7 h and covered a distance of more than 200 km, producing damaging winds and large hail along its track. The goal of this study was to analyze this case using radar data and to evaluate the predictability of such a high-impact event using a numerical weather prediction model. The Weather Research and Forecasting (ARW-WRF) model was used to perform an array of simulations, and using multiple initialization times, the influence of lead time was examined. Furthermore, the dependence of the results on the choice of parameterization scheme used in the model is assessed below. The model performed satisfactorily in predicting intense storm activity, without reaching the extreme values observed by the radar. Full article

This study evaluated ten drought indices focusing on their ability to monitor drought events in Marathwada, a semi-arid region of India. High-resolution gridded monthly total precipitation data for 75 years (1950–2024) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to evaluate the drought indices. These indices were computed across six timescales: 1, 3, 4, 6, 9, and 12 months. A Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was employed to detect temporal volatility in precipitation, followed by a second-order geospatial autocorrelation eigenfunction eigendecomposition using Global Moran’s Index statistics to geolocate both aggregated and non-aggregated precipitation locations. The performance of drought indices was assessed using non-parametric Spearman’s correlation to identify the strength, direction, and similarity of regional-specific drought events. The temporal lag interdependence between meteorological and agricultural droughts was assessed using a non-parametric Spearman’s cross correlation function (SCCF). The findings revealed that the GARCH model with a skewed Student’s t distribution effectively captured conditional temporal volatility and asymptotic behavior in the precipitation series. The model’s sensitivity enabled the incorporation of temporal fluctuations related to droughts and extreme meteorological events. The Bhalme and Mooley Drought Index (BMDI-6) and Z-Score Index (ZSI-6) were the most applicable indices for drought monitoring. Spearman’s cross-correlation analysis revealed that meteorological droughts influenced agricultural droughts with a time lag of up to 4 months. Full article

Biomass burning in the Amazon region, especially during the dry season, generates aerosol dispersion events across the southern part of the continent, with impacts observable thousands of kilometers from the emission source. This study presents a long-range aerosol transport case from September 2024, in which smoke aerosols from forest fires in the central Amazon reached southeastern and southern Brazil, affecting the air quality in distant areas such as São Paulo and São Martinho. The event was simulated using the Weather Research and Forecasting model with Chemistry (WRF-Chem), configured with the MOZCART chemical mechanism, combined with MERRA-2 reanalysis data and by using the 3BEM biomass burning emission inventory. Satellite datasets from MODIS and MERRA-2 reanalysis were used to evaluate the model’s performance. The results indicate that the South American Low-Level Jet (SALLJ) played a key role in transporting carbonaceous aerosols over long distances. The model successfully captured the spatial and temporal evolution of the aerosol plume and its impacts, although it tended to underestimate aerosol optical depth (AOD) values compared with satellite observations. This study highlights the WRF-Chem’s capability to simulate extreme smoke transport events in South America and supports its potential application in forecasting and air quality assessments. Full article

To address the challenge of effective tracking of weather-induced power fluctuation trends in daytime PV power forecasting, this paper proposes a joint forecasting framework oriented to weather classification. For the weather classification module, a spectral clustering method incorporating multivariate feature fusion-based evaluation is introduced to address the limitation that conventional clustering models fail to effectively identify power fluctuations caused by dynamic weather variations. Simultaneously, to tackle non-stationary fluctuations and local abrupt changes in PV power forecasting, a non-stationary Transformer-BiLSTM model optimised using the Differentiated Creative Search (DCS) algorithm (DCS-NsT-BiLSTM)is proposed. This model enables the co-optimisation of global and local features under diverse weather patterns. The proposed method takes into consideration the climatic typology of PV power plants, thereby overcoming the insensitivity of traditional clustering models to high-dimensional non-stationary data. Furthermore, the approach utilises the novel intelligent optimisation algorithm DCS to update the key hyperparameters of the forecasting model, which in turn enhances the accuracy of day-ahead PV power generation forecasting. Applied to a photovoltaic power station in Jilin Province, China, this method reduced the mean root mean square error by 4.63% across various weather conditions, effectively validating the proposed methodology. Full article

This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K_60x, and the neural network emulator, RRTMG-K_NN, within a high-resolution numerical weather prediction (NWP) model. The evaluation uses satellite-derived observations of Outgoing Longwave Radiation (OLR) and Outgoing Shortwave Radiation (OSR) from the Clouds and the Earth’s Radiant Energy System (CERES) over the Korean Peninsula during 2020, including an extreme case study of Typhoon Haishen. Results show that RRTMG-K reduces RMSEs by 4.8% for OLR and 17.5% for OSR relative to RRTMG, primarily due to substantial bias reduction (42.3% for OLR, 60.4% for OSR). The RRTMG-K_NN scheme achieves approximately 60-fold computational speedup while maintaining similar or slightly better accuracy than RRTMG-K; specifically, it reduces OLR errors by 1.2% and OSR errors by 1.6% compared to the infrequently applied RRTMG-K_60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K_60x) slightly increases errors, underscoring the trade-off between computational efficiency and accuracy. These findings demonstrate the value of integrating advanced satellite flux observations and machine learning techniques into the evaluation and optimization of radiation schemes, providing a robust framework for improving cloud–radiation interaction representation in NWP models. Full article

Forecasting national energy demand is challenging under climate variability and macroeconomic uncertainty. We assess whether hybrid Prophet–(Q)LSTM models that integrate climate and economic predictors improve long-horizon forecasts for The Netherlands. This study covers 2010–2024 and uses data from ENTSO-E (hourly load), KNMI and Copernicus/ERA5 (weather and climate indices), Statistics Netherlands (CBS), and the World Bank (macroeconomic and commodity series). We evaluate Prophet–LSTM and Prophet–QLSTM, each with and without stacking via XGBoost, under rolling-origin cross-validation; feature choice is guided by Bayesian optimisation. Stacking provides the largest and most consistent accuracy gains across horizons. The quantum-inspired variant performs on par with the classical ensemble while using a smaller recurrent core, indicating value as a complementary learner. Substantively, short-run variation is dominated by weather and calendar effects, whereas selected commodity and activity indicators stabilise longer-range baselines; combining both domains improves robustness to regime shifts. In sustainability terms, improved long-horizon accuracy supports renewable integration, resource adequacy, and lower curtailment by strengthening seasonal planning and demand-response scheduling. The pipeline demonstrates the feasibility of integrating quantum-inspired components into national planning workflows, using The Netherlands as a case study, while acknowledging simulator constraints and compute costs. Full article

Changes in the amount and distribution of rainfall highly impact agricultural production in predominantly rainfed farming systems in Africa. Reliable rainfall forecasts on a daily timescale are vital for in-season decision-making. This study evaluated the relative prediction abilities of the European Centre for Medium-Range Weather Forecasts Season 5.1 (ECMWFSv5.1) and the Climate Forecast System version 2 (CFSv2) gridded rainfall models across Africa and three sub-regions from 2012–2022. The results indicate that the performance of both models declines with increasing lead times and improves with aggregated or coarser temporal resolutions. ECMWFv5.1 consistently represented observed daily rainfall better than CFSv2 at all lead times, particularly in West Africa. On dekadal timescales, ECMWFv5.1 outperformed CFSv2 across all sub-regions. CFSv2 tended to overestimate low- and high-intensity rainfall events, whereas ECMWFv5.1 slightly underestimated low-intensity rainfall but accurately captured high-intensity events. While ECMWFv5.1 showed superior skill overall, model reliability was generally limited to West Africa; in contrast, both models performed poorly in East Africa. The high probability of detection (POD) indicates that the models are generally effective at identifying rainy days. However, their overall accuracy in forecasting rainfall across Africa varies depending on lead time, region, rainfall intensity, and elevation. While we did not apply bias-correction methods in this study, we recommend that such techniques be used in future work to improve the reliability of forecasts for operational and sectoral applications. This study therefore highlights both the strengths and the limitations of CFSv2 and ECMWFv5.1 for climate impact assessments, particularly in West Africa and low-elevation regions. Full article

Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and 7 days ahead) and mid-term (14 and 30 days ahead) forecasts of SM at surface (0–10 cm) and subsurface (10–40 and 40–100 cm) soil layers across the contiguous U.S. The model was trained with five-year period (2018–2022) datasets including Soil Moisture Active Passive (SMAP) level 3 ancillary covariables, North American Land Data Assimilation System phase 2 (NLDAS-2) SM product, shortwave infrared reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS), and terrain features (e.g., elevation, slope, curvature), as well as soil texture and bulk density maps from the Soil Landscape of the United States (SOLUS100) database. To develop and evaluate the model, the dataset was divided into three subsets: training (January 2018–January 2021), validation (2021), and testing (2022). The outputs were validated with observed in situ data from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) soil moisture networks. The results indicated that the accuracy of SM forecasts decreased with increasing lead time, particularly in the surface (0–10 cm) and subsurface (10–40 cm) layers, where strong fluctuations driven by rainfall variability and evapotranspiration fluxes introduced greater uncertainty. Across all soil layers and lead times, the model achieved a median unbiased root mean square error (ubRMSE) of 0.04 cm³ cm⁻³ with a Pearson correlation coefficient of 0.61. Further, the performance of the model was evaluated with respect to both land cover and soil texture databases. Forecast accuracy was highest in coarse-textured soils, followed by medium- and fine-textured soils, likely because the greater penetration depth of microwave observations improves SM retrieval in sandy soils. Among land cover types, performance was strongest in grasslands and savannas and weakest in dense forests and shrublands, where dense vegetation attenuates the microwave signal and reduces SM estimation accuracy. These results demonstrate that the ConvLSTM framework provides skillful short- and mid-term forecasts of surface and subsurface soil moisture, offering valuable support for large-scale drought and flood monitoring. Full article

The paper presents AgriMicro, a modern Farm Management Information System (FMIS) designed to help farmers monitor and optimize corn crops from sowing to harvest, by leveraging cloud technologies and machine learning algorithms. The platform is built on a modular architecture composed of multiple components implemented through microservices such as the weather and soil service, recommendation and alert engine, field service, and crop service—which continuously communicate to centralize field data and provide real-time insights. Through the ongoing exchange of data between these services, different information pieces about soil conditions, crop health, and agricultural operations are processed and analyzed, resulting in predictions of crop evolution and practical recommendations for future interventions (e.g., fertilization or irrigation). This integrated FMIS transforms collected data into concrete actions, supporting farmers and agricultural consultants in making informed decisions, improving field productivity, and ensuring more efficient resource use. Its microservice-based architecture provides scalability, modularity, and straightforward integration with other information systems. The objectives of this study are threefold. First, to specify and design a modular FMIS architecture based on microservices and cloud computing, ensuring scalability, interoperability and adaptability to different farm contexts. Second, to prototype and integrate initial components and Internet of Things (IoT)-based data collection with machine learning models, specifically Random Forest and XGBoost, to provide maize yield forecasting as a proof of concept. Model performance was evaluated using standard predictive accuracy metrics, including the coefficient of determination (R²) and the root mean square error (RMSE), confirming the reliability of the forecasting pipeline and validated against official harvest data (average maize yield) from the Romanian National Institute of Statistics (INS) for 2024. These results confirm the reliability of the forecasting pipeline under controlled conditions; however, in real-world practice, broader regional and inter-annual variability typically results in considerably higher errors, often on the order of 10–20%. Third, to present a Romania based case study which illustrates the end-to-end workflow and outlines an implementation roadmap toward full deployment. As this is a design-oriented study currently under development, several services remain at the planning or early prototyping stage, and comprehensive system level benchmarks are deferred to future work. Full article

Terminal Aerodrome Forecasts (TAFs) and long TAFs are issued by forecasters in the Hellenic National Meteorological Service for the upcoming 9 or 24 h respectively, for all airports to aid in flight planning. The most important predicted parameters are wind speed and direction, weather phenomena, and visibility. A verification procedure comparing TAFs to Meteorological Aerodrome Reports (METARs) can be helpful for improving the skills of forecasters. To this end, software was developed to read the raw format structure of TAFs and METARs and compare them. The wind speed and direction for each forecast hour are verified according to thresholds specified by the International Civil Aviation Organization (ICAO), and a summary is produced, showing correct, overestimated, and underestimated percentages. A weather phenomenon, such as rain (RA) or thunderstorm (TSRA), is usually given inside a probability time window in a TAF (e.g., PROB40 TEMPO RA). In such a case, the occurrence or absence of the phenomenon and its frequency inside the time window are considered when determining the forecaster’s skill (correct, false alarm, or miss), evaluated using categorical indices such as POD, ETS, and FAR over a number of TAFs. A similar procedure is carried out for visibility range intervals. In this study, verification was performed for a test period of January 2023 for 14 Greek airports. Results indicate generally good performance in predicting wind speed and direction, and also demonstrate the TAFs accuracy in detecting phenomena like rain, although with a notable tendency for false alarms. A systematic tendency to underestimate actual visibility, especially inside TEMPO statements is observed. Full article

Emergency department (ED) overcrowding remains a major challenge for hospitals, resulting in worse outcomes, longer waits, elevated hospital operating costs, and greater strain on staff. Boarding count, the number of patients who have been admitted to an inpatient unit but are still in the ED waiting for transfer, is a key patient flow metric that affects overall ED operations. This study presents a deep learning-based approach to forecasting ED boarding counts using only operational and contextual features—derived from hourly ED tracking, inpatient census, weather, holiday, and local event data—without patient-level clinical information. Different deep learning algorithms were tested, including convolutional and transformer-based time-series models, and the best-performing model, Time Series Transformer Plus (TSTPlus), achieved strong performance at the 6-h prediction horizon, with a mean absolute error of 4.30 and an R² score of 0.79. After identifying TSTPlus as the best-performing model, its performance was further evaluated at additional horizons of 8, 10, and 12 h. The model was also evaluated under extreme operational conditions, demonstrating robust and accurate forecasts. These findings highlight the potential of the proposed forecasting approach to support proactive operational planning and reduce ED overcrowding. Full article

Accurate ground deformation monitoring with interferometric synthetic aperture radar (InSAR) is often hindered by tropospheric delays caused by atmospheric pressure, temperature, and water vapor variations. While models such as ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) provide first-order corrections, they often leave residual errors dominated by small-scale turbulent effects. To address this, we present a novel variational autoencoder with clustering (VAE-clustering) approach that performs unsupervised separation of atmospheric and deformation signals, followed by noise component removal via density-based clustering. The method is integrated into the MintPy pipeline for automated velocity and displacement time-series retrieval. We evaluate our approach on Sentinel-1 interferograms from three case studies: (1) land subsidence in Mashhad, Iran (2015–2022), (2) land subsidence in Tehran, Iran (2018–2021), and (3) postseismic deformation after the 2021 Acapulco earthquake. Across all cases, the method reduced the velocity standard deviation by approximately 70% compared to the ERA5 corrections, leading to more reliable displacement estimates. These results demonstrate that VAE-clustering can effectively mitigate residual tropospheric noise, improving the accuracy of large-scale InSAR time-series analyses for geohazard monitoring and related applications. Full article

Tropospheric delay is recognized as one of the main error sources affecting Global Navigation Satellite System (GNSS) positioning accuracy. Previous studies have only employed artificial intelligence-based weather models with low temporal resolution for comprehensive assessments. Therefore, this study proposes an ensemble forecasting approach based on multiple initial conditions from the Pangu-Weather model to obtain hourly resolution tropospheric delays. The ZTD data from 250 Crustal Movement Observation Network of China (CMONOC) GNSS stations across China in 2020 are used to validate the accuracy of the Pangu-Weather model. The findings show that the Pangu-Weather model exhibits strong performance under both forecast lead times compared to the traditional Global Forecast System (GFS) product, particularly in southern China. However, the Pangu-Weather model provides slightly inferior forecast accuracy compared to the GFS product in dry, low-humidity regions at stations located between 2 and 4 km in altitude, and for forecast lead times of less than 9 h. Nevertheless, a lower error accumulation trend is exhibited by the Pangu-Weather model, as its RMSE is larger than that of the Global Pressure and Temperature 3 (GPT3) empirical model after 240 h (10 days), demonstrating more stable accuracy over longer forecast periods. In summary, the Pangu-Weather model shows significant advantages in Chinese regions with complex climates and terrains, and it is of great potential in GNSS real-time positioning and meteorological monitoring. Full article