Search Results (1,356)

Atmosphere visibility (VIS) is important for flight safety. Deriving VIS with high accuracy is a task in the field of atmospheric science. Although ground-based observation is ideal for VIS measurement, large-scale VIS observations must rely on satellite data. In this paper, we propose a new method to retrieve VIS with high accuracy using the aerosol products of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Himawari-8. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used as the simulation tool. In our method, we used the techniques of data integration and Kriging interpolation in the VIS retrieval for the first time. It was demonstrated that our results of VIS have high accuracy. Our method is useful in aerosol characterization and atmosphere monitoring. Full article

Drought is expected to intensify under climate change, posing significant risks to Mediterranean agroecosystems. This study provides long-term projections of drought and wetness conditions for three representative Mediterranean regions—Eastern Mancha (Spain), Sidi Bouzid Governorate (Tunisia), and the Beqaa Valley (Lebanon)—to support climate-resilient planning. Future monthly precipitation (2020–2050) was dynamically downscaled using the Weather Research and Forecasting (WRF) model under the RCP4.5 scenario, and the Standardized Precipitation Index (SPI12) was subsequently applied to quantify drought severity at annual and monthly scales. By integrating dynamically downscaled WRF projections with pixel-based SPI analysis across three spatially distinct Mediterranean regions, the study provides a novel, spatially explicit and comparative framework for assessing future drought and wetness extremes in support of climate-resilient planning. The results reveal spatial variability and moderate temporal fluctuations across the three regions, reflected in differing timings and intensities of their driest and wettest hydrological years. Spain is projected to experience its driest hydrological year in 2046–2047, Tunisia in 2030–2031, and Lebanon in 2047–2048. The wettest years are projected to occur in 2045–2046 for Spain and Tunisia, and in 2028–2029 for Lebanon. Although extreme drought events are not widely anticipated, localised severe dry periods emerge in many parts of the study areas. while in Lebanon, these conditions also extend into the winter and spring. These findings underscore the need for spatially targeted adaptation rather than uniform regional measures. Identifying both driest and wettest projected years enhances preparedness, informs water-resource optimisation, and supports agricultural land-use planning, especially in areas with favourable future climatic conditions. Integrating drought projections into multi-hazard planning (i.e., drought and floods) frameworks can further strengthen territorial resilience in regions facing increasing climate-related extremes. Full article

Wind gusts pose an increasing threat to sustainable development, damaging resilient infrastructure (SDG 9), disrupting clean energy systems (SDG 7), and endangering community safety (SDG 11). However, the reliability of early warning systems remains limited by systematic biases in Numerical Weather Prediction (NWP) models and insufficient uncertainty quantification, particularly in regions with sparse monitoring networks. To address these challenges in sustainable disaster risk reduction, this study proposes a physics-informed deep learning framework—the Physics-Informed Spatial Attention Network (PISA-Net). The model integrates high-resolution WRF-UPP forecasts as a physical prior within a Transformer-based architecture, enabling effective bias correction and spatial dependency learning under data-sparse conditions. A hybrid probabilistic learning objective is employed to simultaneously improve deterministic gust predictions and provide calibrated uncertainty estimates. Evaluated on 61 extratropical cyclone events in the northeastern United States, PISA-Net substantially outperforms baseline NWP and conventional deep learning models, reducing the mean absolute error and root mean square error to 1.75 m/s and 2.26 m/s, respectively. In addition, the resulting 95% prediction intervals are well calibrated and offer reliable risk-based guidance. By improving both the accuracy and credibility of wind gust forecasts, PISA-Net provides a practical decision-support tool for infrastructure maintenance, wind farm operations, and public safety planning. This work demonstrates the potential of physics-informed deep learning to strengthen sustainable early warning systems in data-sparse regions. Full article

Twin wildfires burned over 9500 ha in Seferihisar, İzmir, western Türkiye, on 29—30 June 2025 under extreme fire weather conditions. This study reconstructs the spatiotemporal progression of the fires and examines the drivers of contrasting behaviors and burn severity. Multi-source datasets—Sentinel-2 imagery, VIIRS/MODIS thermal detections, MTG images and thermal detections, aerial photos, and ground data—were integrated to delineate progression polygons and compute rate of spread (ROS), fuel consumption (FC), and fire-line intensity (FI). Kuyucak fire showed rapid early growth, burning 3554 ha in 2.5 h (mean ROS of 5.0 km h⁻¹; mean FI of 37,789 kW m⁻¹), driven by strong northeasterly winds of 40–50 km h⁻¹, steep terrain, dense Pinus brutia fuels, and very low dead fine-fuel moisture (<6%). Kavakdere fire advanced more slowly (mean ROS of 1.6 km h⁻¹) across open grassland and cropland, yielding lower FC and FI. Synoptic analysis revealed a strong pressure-gradient-induced northeasterly wind regime linked to a mid-tropospheric geopotential height dipole between Central Europe and the Eastern Mediterranean, while WRF simulations indicated a dry boundary layer and enhanced low-level winds during peak spread. Sentinel-2 dNBR burn severity mapping showed substantial spatial variability tied to fuel and topography contrasts. Findings demonstrate how twin ignitions under similar weather conditions can produce divergent outcomes, underscoring the need for terrain- and fuel-aware strategies during extreme Mediterranean fire outbreaks. Full article

To advance marine thunderstorm forecasting and enhance the operational utility of lightning data, this study developed a novel very low-frequency (VLF) lightning data assimilation scheme for the South China Sea region. The three-dimensional graupel mixing ratio field was successfully inverted from VLF lightning detection data through the application of an empirical formula linking lightning frequency to graupel mass, a database of graupel mixing ratio profiles, and a distance-weighted diffusion scheme. This reconstructed field was then subjected to horizontal diffusion and assimilated into the Weather Research and Forecasting (WRF) model using the Grid Nudging module within the WRF–Four-Dimensional Data Assimilation (WRF-FDDA) system. A quantitative evaluation of 37 nocturnal marine convective cases was conducted using Fengyun-4A(FY-4A) satellite observations. The results demonstrate that the proposed assimilation method significantly enhances short-term (0–6 h) forecast performance. Specifically, the Fractions Skill Score (FSS) derived from the Advanced Geosynchronous Radiation Imager (AGRI) data increased rapidly during the early forecast stage, exceeding a value of 0.9. Meanwhile, the Lightning Mapping Imager Event (LMIE) product evaluation showed a high probability of detection (POD) of 85% for lightning forecasts, with a false alarm ratio (FAR) of only 9%. These findings indicate that the assimilation approach improves the accuracy of capturing the spatial structure and evolution of convective systems. Although the degree of improvement diminished with longer lead times, the results confirm the value of VLF lightning data in initializing convective-scale processes and underscore its practical value in marine nowcasting applications. Full article

Aerosol dry deposition is an important sink for particulate matter and a source of uncertainty in air quality modeling. Using the Weather Research and Forecasting model coupled with CUACE (WRF-CUACE), we quantified how three aerosol dry deposition schemes and satellite-based leaf area index (LAI) information affected PM_2.5 dry removal and near-surface PM_2.5 over central and eastern China in January 2022. The schemes were abbreviated as Z01, E20, and PZ10, respectively. A fourth simulation (PZ10_MLAI) used PZ10 but replaced the baseline LAI dataset with a Moderate Resolution Imaging Spectroradiometer (MODIS) constrained LAI field. Hourly PM_2.5 was evaluated with the China National Environmental Monitoring Center network. The schemes produced pronounced, size-dependent differences in deposition velocities, with a pronounced spread in the 0 to 2.5 µm average and more than one order of magnitude spread in the accumulation mode diagnostic, leading to distinct regional mean PM_2.5 dry deposition fluxes. The mean PM_2.5 flux increased by 5.9% in E20 relative to Z01 and decreased by 54.4% in PZ10. The MODIS LAI adjustment changed the PZ10 mean flux by 0.42%. The flux contrasts yielded coherent PM_2.5 responses, with E20 reducing near-surface concentrations by about 10 to 30% and PZ10 increasing them by about 20 to 60%, reaching about 80 to 100% in parts of southern China. Domain mean correlations ranged from 0.61 to 0.65 and PZ10-based simulations exhibited near-zero mean bias. Although MODIS LAI effects were modest for this winter month, local PM_2.5 differences commonly remained within about 4% and approached 6 to 10%, indicating that satellite LAI constraints can be important for multi-year and decadal applications. Full article

This study quantitatively evaluates the health impacts of urban temperature changes and the potential health benefits of highly reflective roofs as an urban heat island (UHI) mitigation measure. First, empirically derived relationships between ambient temperature and health-related indicators were established for multiple diseases, including both fatal and non-fatal outcomes. Health impacts were assessed using disability-adjusted life years (DALYs), integrating years of life lost (YLLs) and years lived with disability (YLDs). Target diseases included heat- and cold-related mortality, heatstroke, infectious diseases, sleep disturbance, and fatigue. Next, a meteorological simulation was conducted using a Weather Research and Forecasting (WRF) model to estimate outdoor air temperature changes resulting from the implementation of highly reflective building roofs in Osaka Prefecture, Japan. Roof surface reflectance was increased from 0.15 to 0.65 within an urban canopy model, and temperature reductions were evaluated at a 2 km spatial resolution for a one-year period. The results indicate that highly reflective roofs reduced daytime air temperatures by approximately 1.2–1.8 °C, with greater effects observed in high-density urban areas. By integrating the simulated temperature reductions with the temperature–health relationships, annual health impacts were quantified. Although wintertime increases in cold-related health burdens were observed, the annual cumulative DALYs decreased by 1767, corresponding to approximately a 5% reduction in total temperature-related health burdens in Osaka Prefecture. These findings demonstrate that rooftop reflectivity enhancement can contribute to net health improvements while highlighting the importance of accounting for seasonal trade-offs in UHI mitigation strategies. Full article

In recent years, the increasing frequency of strong and super typhoons has been attributed to rising sea surface temperatures due to global warming. This study utilized the Weather Research and Forecasting (WRF) and Simulating WAves Nearshore (SWAN) models to analyze 30 years of wind and wave data for the Yangjiang sea area in China. The accuracy of the numerical simulations was validated using observed data from typhoons Ty201213, Ty201522, Ty201822, and Ty202118, along with wind and wave data from December 2024. This study utilized the P-III distribution to analyze design wind parameters. At a height of 10 m, the 3 s and 10 min mean wind speeds for the 100- and 50-year return periods were 62.21 m/s, 47.85 m/s, 57.99 m/s, and 44.61 m/s, respectively. At hub height (170 m), the corresponding values were 80.27 m/s, 61.75 m/s, 74.84 m/s, and 57.57 m/s. Furthermore, this study successfully applied a 2D-KDE approach to construct a joint probability model and derive environmental contours for extreme environmental assessments. The HS and TP at project point P for the 100- and 50-year return periods are 13.61 m and 15.91 s, as well as 12.39 m and 15.07 s, respectively. Full article

Urbanization and climate change have intensified the need for advanced methods to simulate building energy performance within realistic urban environmental contexts. This study presents a microclimate-informed framework for developing representative building energy prototypes that enable the estimation of energy use for buildings sharing similar microclimatic conditions and building-level characteristics. The framework is demonstrated using Des Moines, Iowa, as a case study. The framework combines high-resolution microclimate modeling with geospatial analysis to quantify the influence of urban form and vegetation on building energy use. Localized weather files were generated using the Weather Research and Forecasting (WRF) model to capture spatial variations in microclimate across the city. Detailed three-dimensional models of buildings and trees were developed from Light Detection and Ranging (LiDAR) point cloud data and integrated with building attributes, including construction materials and heating and cooling systems, to generate representative building typologies use them to build a similarity-based lookup table. Urban energy simulations were conducted using the Urban Modeling Interface (UMI). To demonstrate the effectiveness of the framework, simulations were conducted for two building prototypes according to the framework. Results show that monthly energy use intensity (EUI) of a representative cluster compared to randomly selected buildings differs by 10% to 19%, with both positive and negative deviations observed depending on building template and month. Thus, the proposed framework shows great promise to capture comparable energy performance trends across buildings with similar construction characteristics and urban context and minimize computational demands for doing so. While evapotranspiration effects are not explicitly modeled in the current framework, they are recognized as an important microclimatic process and will be incorporated in future work. This study demonstrates that the proposed framework provides a scalable and computationally efficient approach for urban-scale energy analysis and can support data driven decision making for climate-responsive urban planning. Full article

Wind energy has the potential to become an important source of energy for remote Arctic regions. However, there are risks associated with the exposure of coastal wind parks to extremely strong winds caused by storms and polar lows. Extreme winds can either enhance or reduce wind energy production. The outcomes largely depend on the coastal landscape surrounding the wind park. To address these questions, we conducted a series of simulations using the Weather Research and Forecasting (WRF) model. This study focuses on one of the strongest wind events along the western Norwegian coast—the landfall of the storm “Ylva” (24–27 November 2017). The study employs terrain-resolving downscaling by zooming in on the area of the Kvitfjell–Raudfjell wind park, Norway. The terrain-resolving WRF simulations reveal stronger winds at turbine hub height (80 m to 100 m above the ground level) in the coastal area. However, it was previously overlooked that the landfall of an Atlantic storm, which approaches this area from the southwest, brings the strongest winds from southeast directions, i.e., from the land. This creates geographically extensive and vertically deep wind-sheltered areas along the coast. Wind speeds at hub height in these sheltered areas are reduced, while they remain extreme over wind-channeling sea fjords. The novelty and applied value of this study is that it reveals an overlooked opportunity for optimal wind park siting. The coastal wind parks can take advantage of both sustained westerly winds during normal weather conditions and wind sheltering during extreme storm conditions. We found that the Kvitfjell–Raudfjell location is nearly optimal with respect to the extreme winds of “Ylva.” Full article

Large-eddy simulation (LES) within a weather research and forecasting (WRF) model coupled with an active scalar transport equation was used to simulate Atmospheric Boundary Layer conditions during the Mosquito fire, the largest wildland fire in California during September 2022. The simulations were conducted with realistic boundary conditions derived from the National Oceanic and Atmospheric Administration (NOAA) High Resolution Rapid Refresh (HRRR) model, with the aim of better understanding the two-way coupling between the ABL and plume dynamics. The terrain was extremely inhomogeneous, and the topography varied significantly within the numerical domain. Initially, LES of the smoke-free ABL was conducted on nested domains, and detailed ABL data were gathered from 8 to 9 September 2022. LES simulations were validated using four Automated Surface Observing System (ASOS) stations and NOAA meteorological (MET) observations, as well as NOAA met Twin Otter measurements, and the desired accuracy was established. The smoke plume was then released into the ABL at noon on 9 September 2022, and the plume simulations were conducted for a period of one hour following the release. During this period, the ABL transitioned from convective to buoyancy-shear-driven regimes. Late-night and early-morning conditions are influenced by the complex topography and low-level jet, whereas buoyancy and shear control the ABL dynamics during the morning and afternoon hours. The plume vertical transport is influenced by the ABL depth and the size of the vertical turbulence structures during that time, whereas the wind conditions and turbulent kinetic energy within the ABL dictate the horizontal transport scales of the plume. In addition, the results demonstrate that the plume modifies the microclimate along its path. Full article

This study evaluates a time-lagged ensemble averaging strategy to improve the accuracy and robustness of short-range streamflow point forecasts when hydrological simulations are driven by deterministic numerical weather prediction (NWP) forcing. We implemented WRF-Hydro in standalone mode for the Geumho River basin, South Korea, using Local Data Assimilation and Prediction System (LDAPS) forecasts initialized every 6 h with lead times up to 48 h. Time-lagged ensembles were constructed by averaging overlapping WRF-Hydro predictions from successive LDAPS initializations. Across two contrasting flood-producing storms, ensemble-mean forecasts consistently reduced lead-time-dependent skill degradation relative to single-initialization forecasts; the event-wise median Nash–Sutcliffe efficiency at the downstream gauge improved from 0.39 to 0.81 at 48 h (Event 2020) and from 0.48 to 0.85 at 24 h (Event 2022), while RMSE decreased by up to 48%. The most effective ensemble window varied with storm evolution and forecast horizon, indicating additional gains from adaptive time-lag selection. Overall, time-lagged ensemble averaging provides a practical, low-cost post-processing approach to enhance operational short-range streamflow prediction with NWP forcings. Full article

This study presents an integrated decision support system (DSS) designed to optimize real-time reservoir operation during typhoons by balancing flood control and water supply. The system combines ensemble quantitative precipitation forecasts (QPF) from WRF/MM5 models, a physically based rainfall–runoff model (KW-GIUH), and a three-stage optimization algorithm for reservoir release decisions. Eighteen ensemble rainfall members are processed to generate 6 h inflow forecasts, which serve as inputs for determining adaptive outflow strategies that consider both storage requirements and downstream flood risks. The DSS was tested using historical typhoon events—Talim, Saola, Trami, and Kong-rey—at the Tseng-Wen Reservoir in Taiwan. Results show that the KW-GIUH model effectively reproduces hydrograph characteristics, with a coefficient of efficiency around 0.80, while the optimization algorithm successfully maintains reservoir levels near target storage, even under imperfect rainfall forecasts. The mean deviation of reservoir water levels from the recorded to the target values is less than 0.18 m. The system enhances operational flexibility by adjusting release rates according to the proposed outflow index and flood-stage classification. During major storms, the DSS effectively allocates storage space for incoming floods while maximizing water retention during recession periods. Overall, the integrated framework demonstrates strong potential to support real-time reservoir management during extreme weather conditions, thereby improving both flood mitigation and water-supply reliability. Full article

Accurate forecasting of heatwaves is critical for ensuring the safe operation of electricity grids. Focusing on the complex terrain of Sichuan, China, this study investigates the optimization of spectral nudging parameters within the Weather Research and Forecasting (WRF) model to improve predictions of heatwave events. To overcome the subjectivity inherent in the traditional selection of the spectral nudging cutoff wavenumber, we propose an objective method based on power-spectrum energy diagnostics of the background field. This method determines an optimal domain-specific cutoff wavenumber. A series of sensitivity experiments were designed for a significant heatwave event that affected the Sichuan electricity grid in August 2019. These experiments evaluated the impact of different spectral nudging configurations, which considered varying domain sizes and forecast lead times, on correcting large-scale circulation drift and enhancing near-surface air temperature forecasts. The results demonstrate the following: (1) For a smaller domain or a longer forecast lead time, spectral nudging effectively compensates for circulation drift induced by weakening lateral boundary constraints, significantly improving the forecast of heatwave intensity and spatial extent, representing a compensatory effect. (2) For a larger domain that already adequately resolves large-scale circulation evolution, spectral nudging can over-constrain the model’s internal dynamical processes, thereby degrading forecast performance, an outcome termed the over-constraint effect. (3) The proposed energy-threshold method provides an objective, physics-based strategy for identifying dominant large-scale waves and optimizing the spectral nudging cutoff wavenumber. This work offers practical insights for the operational application of spectral nudging over complex terrain to advance extreme temperature forecasting. Full article

Flooding episodes caused by a heavy rainfall event have become more frequent, especially during the rainfall season in Botswana, which poses some socio-economic and environmental risks. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating a heavy rainfall event that occurred on 26 December 2023 in Mahalapye District, Botswana. This event is one among many that have negatively impacted the lives and infrastructures in Botswana. The WRF model was configured using the tropical-suite physics schemes, i.e., (Rapid Radiative Transfer Model, Yonsei University planetary boundary layer scheme, Unified Noah land surface model, New Tiedtke, Weather Research and Forecasting Single-Moment six-class) on a two-way nested domain (9 km and 3 km grid spacing) and was initialized with the GFS dataset. Gauged station data was used for verification alongside synoptic charts generated using ECMWF ERA5 dataset. The results show that the WRF model simulation using the tropical-suite physics schemes is able to reproduce the spatial and temporal patterns of the observed rainfall but with some notable biases. Performance metrics, including RMSE, correlation coefficient, and KGE, showed moderate to good agreement, highlighting the model’s sensitivity to physical parameterization and resolution. The results of this study conclude that the WRF model demonstrates promising potential in forecasting extreme rainfall events in Botswana, but more sensitivity tests to different parameterization schemes are needed in order to integrate the model into the early warning systems to enhance disaster preparedness and response. Full article