Search Results (277)

The accuracy of numerical weather prediction largely depends on the quality of the initial conditions. Global Navigation Satellite System radio occultation (GNSS-RO) observations, with their high vertical resolution, play an important role in reducing initial condition errors. In this study, multiple simulations with different initialization times were conducted during the development of Typhoon BEBINCA using the WRF-GSI assimilation system to evaluate the impact of YunYao GNSS-RO observations on improving extreme weather simulation performance and to investigate the sensitivity of refractivity assimilation to different cloud microphysics parameterization schemes. The results show that assimilating YunYao GNSS-RO data significantly improves the consistency between the model initial fields and observations and enhances the analysis quality in the middle and upper troposphere. Compared with ERA5 reanalysis data, the assimilation experiments better reproduce the spatial and temporal evolution of key atmospheric variables, and the improvements persist from 36 h to 120 h forecast lead time. Statistical results from multiple initializations show that the maximum RMSE reductions exceed 0.2 K for temperature, 0.1 m s⁻¹ for wind speed, and geopotential height shows consistent improvements throughout the entire atmosphere. In addition, the assimilation experiments improve the simulation of Typhoon BEBINCA’s track and intensity. Statistical results from multiple initializations indicate that the 84 h track error is reduced by approximately 30 km on average, and the minimum central pressure bias is also reduced. Sensitivity experiments further show that the WSM6 microphysics scheme performs better in track forecasting, while the Thompson scheme is more suitable for intensity forecasting. Overall, YunYao GNSS-RO assimilation effectively improves typhoon forecast accuracy and demonstrates strong potential for operational applications. Full article

Post-processing of numerical weather prediction (NWP) models constitutes a pivotal link in enhancing forecast performance. Despite their recognition as cutting-edge point-based post-processing techniques, systematic comparative evaluations of ecPoint (ECWMF for point forecasts) and Ensemble Model Output Statistics (EMOS)—particularly assessments of their applicability outside Europe and to Chinese ensemble forecasting systems—remain sparse. In this study, we evaluate two advanced post-processing techniques—EMOS and the ecPoint—for calibrating ensemble precipitation forecasts. A comprehensive assessment of the performance of these ensemble post-processing methods is conducted using the CMA-GEPS (China Meteorological Administration’s Global Ensemble Forecasting System forecast over eastern China. The results demonstrate that both methods significantly mitigate systematic biases and improve the reliability and dispersion of ensemble forecasts. Notably, improvement in forecast accuracy is observed even under convective weather conditions and early-warning capability of extreme precipitation events is improved. Overall, while both methods show comparable performance, they exhibit distinct behaviours across different regions. The ecPoint method slightly outperforms EMOS in terms of Continuous Ranked Probability Score (CRPS) and provides improved resolution and early-warning capabilities at various precipitation thresholds. Full article

In the period 2 to 4 March 2026, two rainstorms with intense convective weather occurred within and in the vicinity of Hong Kong, China, in the early rain season of the year in southern China. This is rather uncommon because the atmosphere is still generally stable (with very low or even zero value of convective available potential energy), and upper tropospheric divergence does not yet exist in the region climatologically. The rain episode is documented in this paper from both observational and forecasting aspects. On the observational side, a low-level vortex is found on and near the surface based on Doppler velocity measurements from a newly installed C-band solid-state weather radar. Combining the three-dimensional wind field as retrieved from the weather data and the measurements from the other ground-based remote-sensing meteorological equipment, the intense convection is mainly triggered by middle to lower tropospheric waves, and the vertical circulation in the atmospheric boundary layer may be stretched vertically upward to form the low-level vortex. In the second rainstorm, features of elevated thunderstorms are also identified. On the forecasting side, a high-resolution, limited-area atmosphere–ocean–wave coupled model manages to capture the occurrence and the timing of the heavy rain. The sub-seasonal forecast by a global model also provides a useful indication of the occurrence of above-normal rainfall over southern China, with a rather special feature of a deep and stationary westerly trough located to the north of the Indochina Peninsula. The microscale cyclone could be successfully picked up by the real-time run of a high-resolution numerical weather prediction model with data assimilation. This paper also discusses the weather service aspect of this rather unusual rainstorm episode. Full article

Accurate ultra-short-term photovoltaic (PV) power forecasting is essential for secure grid dispatch and renewable-rich system operation, yet it remains difficult because of rapid weather fluctuations and error accumulation in multi-step prediction. This paper proposes a decoupled physics-guided residual-correction framework, built on an attention-based sequence-to-sequence (Seq2Seq) architecture, for deterministic 4 h ahead rolling PV forecasting at 15 min resolution. In the first stage, a physical model maps numerical weather prediction (NWP) inputs to a deterministic baseline trajectory while preserving physical bounds. In the second stage, an Attention-Seq2Seq network learns the structured residual trajectory from historical sequences. The global attention mechanism allows the decoder to focus on the most informative historical states, helping reduce information loss and error accumulation over extended horizons. Experiments on a 22-month real-world PV dataset show that the proposed framework outperforms conventional linear and nonlinear benchmarks, reducing root mean square error (RMSE) and mean absolute error (MAE) by 23.79% and 39.17%, respectively, relative to the physical baseline. The framework also maintains robust instantaneous tracking under rapidly changing cloud conditions and preserves a 30–40% error reduction rate at Steps 12–16, supporting reliable intraday scheduling. Full article

Accurately forecasting the weighted mean temperature (Tm) is critical for converting the zenith wet delay (ZWD) into global navigation satellite system (GNSS)-based precipitable water vapor (PWV) for real-time sensing and forecasting applications. The forecast Vienna Mapping Function 1 (VMF1-FC) is a global forecast product developed by TU Wien based on numerical weather prediction models and can provide grid-wise Tm one day ahead. In this study, we evaluate the accuracy of VMF1-FC-forecasted Tm using observations from 319 global radiosonde (RS) sites during 2019–2021. The results indicate that VMF1-FC-forecasted Tm shows a relatively low RMSE but a relatively large bias (0.75 K) relative to the widely used Global Pressure and Temperature 3 (GPT3) model. To improve the accuracy of VMF1-FC-forecasted Tm, three refined models, XTm, LTm, and CTm, are developed using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), respectively, based on observations from 319 RS sites. The models use longitude, latitude, ellipsoidal height, floating day of year (fdoy), and VMF1-FC Tm as input features, and RS Tm as the target variable. Validation using RS data from 2022 that are not involved in model development shows that the refined models significantly reduce bias, with biases of 0 K, 0 K, and −0.03 K for XTm, LTm, and CTm, respectively. Benefiting from the effective reduction in bias, the root mean square error (RMSE) is correspondingly reduced. The RMSEs of XTm, LTm, and CTm are 1.45 K, 1.45 K, and 1.46 K, respectively, achieving improvements of 18.50%/64.93%, 18.44%/64.91%, and 18.11%/64.76% compared with the VMF1-FC and GPT3 models. In addition, three refined models demonstrate higher accuracy and improve stability across different latitude bands, ellipsoidal height ranges, and temporal scales. The refined models provide more accurate global-scale Tm and offer strong potential for GNSS meteorological applications, particularly real-time GNSS-based PWV sensing and weather forecasting. Full article

By 2024, global photovoltaic (PV) capacity exceeded 2000 GW, corresponding with a decline in levelized costs of approximately 90% since 2010. Artificial intelligence (AI) and machine learning (ML) are enabling novel approaches to solar energy system design and implementation. This survey offers a detailed evaluation of AI/ML methodologies utilized across the solar energy value chain, with a focus on solar irradiance forecasting, maximum power point tracking (MPPT), fault identification, and the expeditious discovery of system materials. The distinction between AI as the broader paradigm and ML as its data-driven subset is drawn and maintained throughout. The primary results cite forecasting improvements via deep learning architectures (LSTM, CNN, Transformer) of 10–40% over traditional methods, while hybrid numerical weather prediction and deep learning models achieve mean absolute error reductions of 15–25%. Reinforcement learning-based MPPT achieves tracking efficiencies in excess of 99% under partial shading, CNN-based fault classification reaches accuracies above 95%, and ML-based screening of materials accelerates perovskite optimization by a factor of 5–10×. Promising paradigms such as explainable AI, federated learning, digital twins, and physics-informed neural networks are evaluated alongside technical, economic, and regulatory constraints. This survey provides a consolidated reference and practical roadmap for the advancement of AI-driven solar energy technologies. Full article

Against the background of intensifying global climate change, extreme precipitation events have become increasingly frequent. Improving the accuracy of short-term precipitation nowcasting is therefore essential for disaster prevention and mitigation. Traditional numerical weather prediction (NWP) approaches are constrained by computational latency and errors arising from physical parameterizations, making it difficult to satisfy real-time forecasting requirements at high spatiotemporal resolution. Using the SEVIR dataset, this study conducts a systematic comparison of two Transformer-based deep learning models—Earthformer and LLMDiff—for short-term extreme precipitation nowcasting. Model performance is evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and Success Ratio (SUCR). Results indicate that, for 0–30 min lead times, Earthformer more efficiently captures both local and long-range spatiotemporal dependencies via its Cuboid Attention mechanism and shows a slight advantage for low-intensity precipitation. As the lead time extends to 60 min, LLMDiff demonstrates stronger longer-horizon skill due to its diffusion-based probabilistic modeling and a frozen large language model (LLM) module, which enhance the representation of uncertainty and longer-term evolution of precipitation systems. However, LLMDiff tends to produce a higher false-alarm rate. Overall, Earthformer is better suited for rapid early warning of light precipitation, whereas LLMDiff is more appropriate for high-accuracy nowcasting of heavy precipitation, offering useful insights for intelligent forecasting of extreme weather. Full article

Accurate prediction of extreme rainfall events during the Indian Summer Monsoon (ISM, June to September) is critical for disaster preparedness and mitigation. This study evaluates the performance of two operational numerical weather prediction models, a high-resolution version of Global Forecast System (GFS T1534) and the control member of the Met Office Global and Regional Ensemble Prediction System-Global (MOGREPS-G), in forecasting such events during the ISM from 2020 to 2023. The results demonstrate that, with respect to observations, both models tend to underestimate the mean and variability of rainfall; GFS-T1534 represents the mean and correlation better while MOGREPS-G represents the variability better over the Indian landmass. To assess the models’ performance for extreme rainfall prediction, we fix a rainfall threshold of 50 mm day⁻¹, and the skill scores are computed including Probability of Detection, False Alarm Rate, Bias score and F1 score. Together, these scores indicate that both models show potential in short-range forecasting of extreme rainfall events, particularly within 24 h, but their skills remain limited at longer lead times. Specifically, the model biases vary over different geographical locations, often showing contrasting features. This underscores the need for model-specific post-processing and calibration techniques if these forecasts are to be used effectively for operational decision-making. Full article

The scarcity of global wind field data limits the accuracy of numerical weather prediction. The currently operational spaceborne Doppler lidar (the European Space Agency’s Aeolus) measures only a single line-of-sight (LOS) wind component, which leads to discrepancies between the measured results and the real wind field. The systems of the United States and Japan have provided additional LOS wind measurements. Yet residual errors in correcting for the satellite’s own velocity can still degrade the accuracy of the retrieved wind vectors. To enhance the accuracy and timeliness of global wind observations, we propose a dual-spaceborne Doppler lidar wind measurement system. Two satellite orbits with different inclinations each provide a LOS wind; combining these components at each crossover yields the horizontal wind vector. Thereby, within 12 h, the crossovers blanket the globe, yielding a global horizontal wind-vector field. Orbital simulations show that inclinations summing to 180° produce the most uniform crossover-point distribution. As Satellite-1’s inclination (prograde orbit) increases, the latitudinal coverage of crossover points expands accordingly. The preferred configuration is when the two satellites have inclinations of 70° and 110°, respectively. Their ground tracks cover nearly all major global landmasses, with a symmetrical distribution of intersection points and a balanced grid resolution. As satellite technology further matures, this dual-spaceborne approach is expected to supplement global horizontal wind-field data. Full article

Accurate field-scale meteorological information is required for precision agriculture, but operational numerical weather prediction products remain spatially coarse and cannot resolve local microclimate variability. This study proposes a data fusion superresolution workflow that combines global GFS predictors (0.25°), regional station observations from Southern Moravia (Czech Republic), and static physiographic descriptors (elevation and terrain gradients) to predict the 2 m air temperature 24 h ahead and to generate spatially continuous high-resolution temperature fields. Several model families (LightGBM, TabPFN, Transformer, and Bayesian neural fields) are evaluated under spatiotemporal splits designed to test generalization to unseen time periods and unseen stations; spatial mapping is implemented via a KNN interpolation layer in the physiographic feature space. All learned configurations reduce the mean absolute error relative to raw GFS across splits. In the most operationally relevant regime (unseen stations and unseen future period), TabPFN-KNN achieves the lowest MAE (1.26 °C), corresponding to an ≈24% reduction versus GFS (1.66 °C). The results support the feasibility of an operational, sensor-infrastructure-compatible pipeline for high-resolution temperature superresolution in agricultural landscapes. Full article

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover information to account for surface-related modulation of precipitation, and seasonal cyclic signals to capture regime-dependent variability. These predictors complement dynamic precipitation inputs and enable the model to effectively capture nonlinear spatiotemporal patterns, resulting in improved performance relative to individual radar, IMERG, and LDAPS products. Evaluation against Automated Synoptic Observing System (ASOS) observations yielded a correlation coefficient of 0.935 and a mean absolute error of 3.304 mm day⁻¹ in a Leave-One-Year-Out (LOYO) validation for 2024. Regional analyses further indicate substantial performance gains in complex mountainous areas, including the Yeongdong–Yeongseo region, where the proposed framework markedly reduces estimation errors under challenging winter conditions. Overall, the results demonstrate the potential of the proposed fusion framework to provide robust, high-resolution precipitation estimates in regions characterized by strong topographic and seasonal heterogeneity, supporting applications related to hazard analysis and hydrometeorological assessment. Full article

Recent projections by the Intergovernmental Panel on Climate Change indicate that global warming will turn permanent and further intensify the severity and frequency of extreme weather events (heat waves, rain, and intense droughts), with coastal regions being the most vulnerable to extreme events. Therefore, the risk of natural disasters and the associated regional impacts on water, food, energy, social, and health security represents one of the world’s greatest challenges of this century. However, conventional methodologies for monitoring these regions during extreme events are usually not available to managers and decision-makers with the necessary urgency. The aim of this study was to present a framework concept for assessing extreme flood event impacts in coastal zones using a suite of field data combined with numerical (hydrological, meteorological, and hydrodynamic) and computational (flooding) models in a virtual environment that provides a replica of a natural environment—the Patos Lagoon Digital Twin. The study case was the extreme flood event that occurred in the southernmost region of Brazil in May 2024, considered the largest flooding event in 125 years of data. The hydrodynamic model calculated the water levels around Rio Grande City (MAE ± 0.18 m). These results fed the flooding model, which projected the water over the digital elevation model of the city and produced predictions of flooding conditions on every street (ranging from a few centimeters up to 1.5 m) days before the flooding happened. The results were further customized to attend specific demands from the security forces and municipal civil defense, who evaluated the best alternatives for evacuation strategies and infrastructure safety during the May 2024 extreme flood event. Flood Safety Maps were also generated for all the terminals in the Port of Rio Grande, indicating that the terminals were 0.05 to 2.5 m above the flood level. Overall, this study contributes to a better understanding of the strengths of digital twin models in simulating the impacts of extreme flood events in coastal areas and provides valuable insights into the potential impacts of future climate change in coastal regions, particularly in southern Brazil. This knowledge is crucial for developing targeted strategies to increase regional resilience and sustainability, ensuring that adaptation measures are effectively tailored to anticipated climate impacts. Full article

High-precision temperature prediction is crucial for dealing with extreme weather events under the background of global warming. However, due to the limitations of computing resources, numerical weather prediction models are difficult to directly provide high spatio-temporal resolution data that meets the specific application requirements of a certain region. This problem is particularly prominent in areas with complex terrain. The use of remote sensing data, especially high-resolution terrain data, provides key information for understanding and simulating the interaction between land and atmosphere in complex terrain, making the integration of remote sensing and NWP outputs to achieve high-precision meteorological element downscaling a core challenge. Aiming at the challenge of temperature scaling in complex terrain areas of Southwest China, this paper proposes a novel deep learning model—Terrain Adaptive U-Net Transformer (TAUT). This model takes the encoder–decoder structure of U-Net as the skeleton, deeply integrates the global attention mechanism of Swin Transformer and the local spatiotemporal feature extraction ability of three-dimensional convolution, and innovatively introduces the multi-branch terrain adaptive module (MBTA). The adaptive integration of terrain remote sensing data with various meteorological data, such as temperature fields and wind fields, has been achieved. Eventually, in the complex terrain area of Southwest China, a spatio-temporal high-resolution downscaling of 2 m temperature was realized (from 0.1° in space to 0.01°, and from 3 h intervals to 1 h intervals in time). The experimental results show that within the 48 h downscaling window period, the TAUT model outperforms the comparison models such as bilinear interpolation, SRCNN, U-Net, and EDVR in all evaluation metrics (MAE, RMSE, COR, ACC, PSNR, SSIM). The systematic ablation experiment verified the independent contributions and synergistic effects of the Swin Transformer module, the 3D convolution module, and the MBTA module in improving the performance of each model. In addition, the regional terrain verification shows that this model demonstrates good adaptability and stability under different terrain types (mountains, plateaus, basins). Especially in cases of high-temperature extreme weather, it can more precisely restore the temperature distribution details and spatial textures affected by the terrain, verifying the significant impact of terrain remote sensing data on the accuracy of temperature downscaling. The core contribution of this study lies in the successful construction of a hybrid architecture that can jointly leverage the local feature extraction advantages of CNN and the global context modeling capabilities of Transformer, and effectively integrate key terrain remote sensing data through dedicated modules. The TAUT model offers an effective deep learning solution for precise temperature prediction in complex terrain areas and also provides a referential framework for the integration of remote sensing data and numerical model data in deep learning models. Full article

Short-term load forecasting (STLF) underpins the efficient and secure operation of power systems. This study develops and evaluates a hybrid architecture that couples deep neural networks (DNNs) with support vector regression (SVR) for national-scale day-ahead STLF using Croatian load data from 2006 to 2022. The approach employs an hour-specific framework of 24 hybrid models: each DNN learns a compact nonlinear representation for a given hour, while an SVR trained on the penultimate layer activations performs the final regression. Gradient-boosting-based feature selection yields compact, informative inputs shared across all model variants. To overcome limitations of historical local measurements, the framework integrates global numerical weather prediction data from the TIGGE archive with load and local meteorological observations in an operationally realistic setup. In the held-out test year 2022, the proposed hybrid consistently reduced forecasting error relative to standalone DNN-, LSTM- and Transformer-based baselines, while preserving a reproducible pipeline. Beyond using SVR as an alternative output layer, the contributions are as follows: addressing a 17-year STLF task, proposing an hour-specific hybrid DNN–SVR framework, providing a systematic comparison with deep learning baselines under a unified protocol, and integrating global weather forecasts into a practical day-ahead STLF solution for a real power system. Full article

Acrylonitrile Styrene Acrylate (ASA) is widely used in outdoor structural applications due to its favorable mechanical stability and weather resistance; however, its temperature-dependent plastic behavior remains insufficiently characterized for accurate numerical simulation. This study presents a non-standard method of calibrating the temperature-dependent Johnson–Cook (J-C) plasticity model for ASA in the practical operating temperature range below the glass transition temperature. Uniaxial tensile tests at constant strain rate 0.01 s⁻¹ were performed at −10 °C, +23 °C, and +65 °C to characterize the effect of temperature on the material’s plastic response. The J-C parameters $A$, $B$, and $n$ were identified for each temperature separately and globally using least-squares optimization implemented in MATLAB R2024b, showing good agreement with the experimental stress–strain curves. The calibrated parameters were subsequently implemented in Abaqus 2024 and validated through finite element simulations of the tensile tests. Numerical predictions demonstrated a very high correlation with the experimental data across all temperatures, confirming that the J-C model accurately captures the hardening behavior of ASA. The presented parameter set and calibration methodology provide a reliable basis for future simulation-driven design, forming analysis, and structural assessment of ASA components subjected to variable thermal conditions. Full article