Search Results (1,121)

Atmospheric temperature and humidity profiles are essential for numerical weather prediction and severe weather monitoring. To effectively utilize data from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4 satellite, this study proposes a retrieval method based on a radial basis function (RBF) neural network, which integrates numerical model background profiles with GIIRS simulated radiance errors to construct a mapping from these two inputs to background profile errors. A channel selection strategy is developed using correlations between background errors and radiance errors to identify channels sensitive to temperature and humidity variations at different pressure levels. Experiments are conducted using data from land stations in Zhejiang Province, China, from August to December 2024, including 829 clear-sky and 2109 cloudy profiles. Under clear-sky conditions, the method reduces temperature and humidity root mean square error (RMSE) by approximately 39% and 22.3% compared to background profiles. Under cloudy conditions, despite severe radiation interference, RMSE reductions of 38.5% for temperature and 15.3% for humidity are achieved, with notable improvements below 900 hPa and above 750 hPa for humidity. Compared with the multilayer perceptron (MLP) method, RBF shows superior performance under all test conditions, especially in cloudy-sky humidity retrieval. The proposed approach provides an effective, physically constrained framework for operational GIIRS data application in temperature and humidity retrieval. 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

Based on summer ground-based microwave radiometer (MWR) observations over North China, this study systematically evaluates the accuracy of temperature and humidity profile products derived from the Vertical Atmospheric Sounding System (VASS) onboard the FY-3E satellite. The VASS products are compared with the numerical weather prediction (NWP) background fields as well as the ERA5 and CMA-RA 1.5 (CRA) reanalysis datasets. The results show that both ERA5 and CRA exhibit stable and reliable performance in representing temperature and humidity fields under both clear and cloudy conditions over North China. The temperature root mean square error (RMSE) generally ranges from 1.6 K to 2.6 K at different height levels (from 0 km to 10 km), while the RMSE of absolute humidity is approximately 0.4–2.7 g/m³. These results further confirm the reliability of CRA under both clear and cloudy conditions in this region. In contrast, the errors of the VASS products show pronounced variations with height, station, and weather conditions. A clear systematic underestimation of temperature is found at 1–3 km, with a mean bias of about −3.44 K. Humidity is also significantly underestimated in the boundary layer, with a mean bias of approximately −5.91 g/m³. Both temperature and humidity errors decrease rapidly with increasing height. Clear inter-station differences are also identified. Temperature errors show boundary-layer overestimation in Beijing, while Xingtai and Dingzhou exhibit systematic underestimation throughout most of the profile, with mean biases reaching −4.1 K and −3.3 K, respectively. Boundary-layer humidity underestimation is more pronounced in Xingtai and Dingzhou (approximately −6.6 g/m³) than in Beijing (−4.0 g/m³). Weather-based analysis indicates that clouds have a significant impact on the accuracy of the VASS products. Under cloudy conditions, the near-surface temperature mean bias shifts from overestimation under clear skies to underestimation. The magnitude of humidity underestimation under cloudy conditions is approximately twice that under clear conditions. Further comparison shows that the error characteristics of the NWP background fields in the lower and middle troposphere are partly similar to those of the VASS products. This suggests that the current retrieval algorithm still has limited capability to correct background field biases under complex weather conditions. These results provide scientific support for the selection of application scenarios and the optimization of retrieval algorithms for FY-3E/VASS temperature and humidity profile products, and they also support the reliable use of domestic reanalysis datasets in regional studies. Full article

Increasingly frequent extreme rainfall commonly leads to water accumulation on the road surface, elevating vehicle tire hydroplaning to a major threat to driving safety. Existing research mainly focused on tire model optimization or predicting critical hydroplaning speed features based on empirical formulas and numerical simulations. However, there is a lack of systematic validation of the tire–water–pavement coupling interaction under realistic pavement conditions, with particular insufficient attention paid to pavement dynamic responses. In this study, numerical simulation and field full-scale accelerated loading methods were applied to investigate dynamic response characteristics of the tire–water–pavement coupling interaction system. Parametric analyses were first performed to investigate the influences of vehicle speed, vehicle load, water-film thickness, and tire lateral position on the mechanical behaviors of the fluid–structure interaction for a moving vehicle tire. Subsequently, field-measured dynamic responses’ features were used to validate the numerical model, which was then further applied to predict critical conditions of vehicle tire hydroplaning. Finally, the mechanisms of hydroplaning and corresponding mitigation measures were discussed. The study revealed that increasing vehicle speed and water-film thickness, as well as decreasing vehicle load, would reduce the pavement supporting force. The tire–pavement contact stress and strain decreased from the vehicle tire’s center position towards its shoulders. The predicted critical hydroplaning condition suggested that increasing vehicle load mitigated hydroplaning by reducing the proportion of water-induced hydrodynamic lifting force relative to the total vehicle load. When the water depth is relatively shallow, the hydroplaning risk increases rapidly with water depth, while the water’s adverse impact on tire–pavement contact force gradually diminishes as water depth continues to increase. It implies that a vehicle with a relatively low axle load driving on the pavement with a thin thickness of retained water in light rain will still face the hydroplaning risk, as the pavement’s supporting force may be substantially reduced in this weather. The findings provide theoretical foundations and experimentally supported insights on driving safety assessment and anti-skid design of water-covered pavement. Full article

Typhoon track prediction is an important research direction in weather forecasting. Although deep learning methods have achieved some progress in this field, challenges remain, including insufficient fusion of meteorological features, limited capability in modeling temporal and spatial evolution, and high computational cost of some models. To address these issues, this paper proposes a dual-path, multi-modal typhoon track prediction model that incorporates a gated axial Transformer to enhance the modeling of deep structural features in the meteorological environment. Numerical experimental results show that the proposed model achieves higher prediction accuracy than comparative methods in typhoon track prediction tasks across multiple time scales, demonstrating the effectiveness of the approach. Full article

This modeling study investigates the impact of the 2023 Canadian wildfire aerosols (primarily black carbon and organic aerosol) on weather forecasts, concluding that incorporating real-time aerosol forcing improves model performance over using climatology. Experiments without real-time data severely underestimated aerosol optical depth (AOD), an error mitigated by including the forcing or using the coupled atmosphere–chemistry model. The aerosols exerted a strong direct radiative effect, reducing surface downward shortwave (SW) flux and generating corresponding surface cooling over the wildfire region. Furthermore, including aerosol–cloud interactions amplified this cooling and led to an increase in the overall cloud fraction and precipitation, illustrating complex indirect effects. While these physical improvements enhanced the representation of the atmosphere, the positive impact on overall medium-range forecasting performance (5–10 days) was modest, suggesting that the benefits of accurately representing wildfire feedback on the coupled Earth system are achieved through relatively slow processes, such as radiation feedback. Full article

Hailstorms frequently develop in Yun-Gui Plateau, Western China, which bring about significant economic damage. Due to the high terrain, these storms are typically shallow, rapidly evolving, and challenging to forecast. An X-band phased-array radar (XPAR) network is set up at Weining in Yun-Gui Plateau to study these storms. To explore these XPAR data for numerical prediction of hailstorms in this region, we implement the Weather Research and Forecast (WRF) model and Hydrometeor and Latent Heat Nudging (HLHN) method to assimilate the data and conduct prediction experiments. The XPAR data was evaluated along with the operational Severe Weather Automatic Nowcast (SWAN) system radar mosaic data. Furthermore, a humidity adjustment scheme is used to overcome inconsistency of the humidity field and related prediction errors. The model results show that in comparison to the SWAN data, assimilating XPAR data in 1-min intervals significantly reduces the model error, and improves the representation of rapid hail cloud evolution. Additionally, adjusting the model humidity based on vertically integrated liquid (VIL) derived from the radar data can effectively correct model analyses of humidity and temperatures, suppressing spurious convection, thus improving the hailstorm forecast. Overall, we recommend joint assimilation of the high spatiotemporal resolution XPAR data along with SWAN radar data with the improved WRF-HLHN for hailstorm prediction over the study region, and the algorithm can be promptly adapted to forecasting hailstorms in other regions. Full article

To advance our understanding of atmospheric processes and climate dynamics, improved knowledge of outgoing long-wave radiation (OLR) spectral emission is essential. The FORUM mission, selected for the ninth cycle of the European Space Agency’s Earth Explorer programme, is specifically designed to address the long-standing observational gap in the far-infrared (FIR) spectral region. When combined with measurements from the IASI-NG instrument, FORUM will provide complete spectral coverage of Earth’s OLR emission (spanning 100 to 2760 cm⁻¹ wavenumber, or 3.62 to 100 μm wavelength), thereby enabling robust climate model validation and enhanced understanding of climate change processes. While IASI-NG’s primary mission is to support numerical weather prediction, FORUM is designed to measure key climate variables, which also enable the retrieval of atmospheric parameters in the troposphere and lower stratosphere. In this study, we assess the information content of FORUM and IASI-NG measurements for atmospheric profiling through a simulation-based approach. Synthetic retrieval products are generated using a linearized formulation of the retrieval transfer function, allowing an efficient and physically consistent evaluation of the sensitivity of the two instruments to atmospheric temperature and water vapor profiles. The analysis reveals a non-negligible sensitivity of FORUM to atmospheric temperature extending into the stratosphere, resulting in significant information content at altitudes higher than previously reported. This finding highlights the potential of far-infrared observations to contribute to atmospheric temperature profiling beyond the lower troposphere. The complementary capabilities of FORUM and IASI-NG suggest that their combined use can enhance the characterization of the atmospheric thermal structure. These results represent a first step toward evaluating the potential role of FORUM Level-2 products in future numerical weather prediction applications. 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

The Earth system model ICON (ICOsahedral Nonhydrostatic general circulation) is a flexible framework that can be configured and tuned for various applications such as weather forecasting, simulations of aerosols and trace gases, and climate modelling. The numerical weather prediction component ICON is used in limited area mode (ICON-LAM) in Romania to obtain realistic weather simulations that support operational forecasting activities. The sensitivity of ICON-LAM is preliminarily evaluated for the geographical area of Romania. Numerical simulations using two parameterization schemes for radiation processes, two convection settings and different values for the laminar resistance of heat transfer from the surface to the air are evaluated against a control run employed for operational forecasts at the National Meteorological Administration. The validation is performed focusing on the precipitation field and surface continuous parameters. All configurations were integrated for a short period in summer when forecasted precipitation was strongly overestimated. Further on, selected configurations were evaluated for winter cases. The experiment with the shallow convection only, the ecRad radiation parameterization, and the laminar heat value 10 emerged as the best fit for Romania. This configuration (considered optimal) was evaluated alongside the operational control run for August 2022. Overall results indicate the selected optimal configuration generally outperforms the control run both with regard to precipitation and in forecasting surface parameters. This experiment has been adapted and implemented in operational workflow. 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

Accurate runoff forecasting is essential for effective water resource management, hydropower operation, and flood risk mitigation. In this study, daily inflow runoff in the Xin’an River Basin, eastern China, was simulated using four ensemble learning models: Gradient Boosting Decision Tree (GBDT), XGBoost, CatBoost, and Stacking. Among them, the CatBoost model achieved the best performance, with a correlation coefficient (CC) exceeding 0.97, Nash–Sutcliffe efficiency (NSE) above 0.95, and reduced RMSE and MAE compared with the currently operational hydrological model. To extend the forecast lead times, two hydro–meteorological coupled models were developed by integrating the CatBoost model with a single numerical weather prediction model (EC) and a dynamically weighted multi-model ensemble precipitation forecast system (OCF). The coupled models were evaluated for lead times up to 240 h. The forecast skill value was highest within 96 h, with CC values above 0.80 and NSE around 0.50. The OCF-coupled model demonstrated improved reliability for lead times of 48–96 h, whereas the EC-driven forecasts performed better within the first 48 h. Case studies during the 2021–2022 flood seasons confirmed that the coupled framework accurately reproduced flood evolution and peak discharge dynamics, demonstrating its practical value for medium-range runoff forecasting in humid river basins. Full article

This paper presents a re-analysis of the track and the intensity of tropical cyclone Senyar, an unprecedented tropical cyclone that formed over the Strait of Malacca south of 5 degrees North, moving eastwards towards the South China Sea. This cyclone brought about heavy rainfall, severe flooding and landslides to southern Thailand, Malaysia and Indonesia, and this re-analysis helps document such a special and disastrous storm. Some key meteorological observations are presented to support the re-analysis, including weather radar imageries and surface weather observations. Forecasting aspects of Senyar by medium-range models and a sub-seasonal model are also presented. It turns out that both the numerical weather prediction model and the artificial intelligence model manages to resolve the warm core structure of the cyclone, but the sub-seasonal forecast fails to capture the occurrence of this very rare storm even with a forecast time of one week ahead. The formation of Senyar is found to be related to the terrain of Malay Peninsula and Sumatra, as revealed by a number of numerical simulations using a mesoscale meteorological model with different modifications of the terrain. This may be related to the lee low downstream of the terrain of Malay Peninsula under the prevailing northeasterly flow. Full article

Clouds are a key factor affecting solar radiation, and their dynamic variations directly cause uncertainty and fluctuations in photovoltaic (PV) power output. To improve PV power prediction accuracy, this paper proposes an enhanced short-term photovoltaic power forecasting approach based on a hybrid neural network architecture using features extracted from satellite cloud images. First, a dual-layer image fusion method is developed for satellite cloud images from different wavelengths and spectral bands, effectively improving fusion accuracy. Second, texture descriptors derived from the Gray-Level Co-occurrence Matrix and multiscale information obtained via the wavelet transform are employed for feature extraction from fused images. Combined with a residual network (ResNet), an optical flow method, as well as an LSTM-based temporal modeling module, multidimensional features of the predicted cloud images are obtained. An improved Bayesian optimization (IBO) algorithm is then employed to derive the optimal fused features, thereby improving the matching between cloud image features and PV power. Third, an enhanced hybrid architecture integrating a convolutional neural network and long short-term memory units with a multi-head self-attention mechanism is developed. Numerical weather prediction (NWP) meteorological features are incorporated, and a tilted irradiance model is introduced to calculate the solar irradiance received by PV modules for use in near-term photovoltaic power forecasting. Finally, measurements collected at a photovoltaic power plant located in Hebei Province are used to validate the proposed method. The results show that, relative to the SA-CNN-MSA-LSTM and BO-CNN-LSTM models, the developed approach lowers the RMSE to an extent of 22.56% and 4.32%, while decreasing the MAE by 24.84% and 5.91%, respectively. Overall, the proposed model accurately captures the characteristics of predicted cloud images and effectively improves PV power prediction accuracy. 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