Search Results (140)

This study evaluated the wind speed simulation performance of the Weather Research and Forecasting (WRF) model at three resolutions in Hebei Province based on wind speed data from 2022. The results show that the simulation effectiveness of the WRF model for wind speeds at different heights varies significantly under different seasons and topographic conditions. In general, the model simulates the wind speed at the high level most accurately, followed by the mid level, and the simulation of low level wind speed shows the largest bias. Increasing the model resolution significantly improves the simulation of low-level wind speed, and the 5 km resolution performs best at most stations; while for the mid-level and high-level wind speeds, increasing the resolution does not significantly improve the simulation effect, and the high-resolution simulation has a greater bias at some stations. In terms of topographic features, wind speeds are generally better simulated in mountainous areas than in the plains during spring, summer, and autumn, while the opposite is true in winter. These findings provide scientific reference for WRF model optimal resolution selection and wind resource assessment. Full article

This study addresses the dynamic response control of deep-water jacket offshore platforms under typhoon and misaligned wave loads by proposing a Tuned Mass Damper (TMD)-based vibration suppression strategy. Typhoon loading is predicted using the Weather Research and Forecasting (WRF) model to simulate maximum wind speed and direction, a customized exponential wind profile fitted to WRF results, and a spectral model calibrated with field-measured data. Correspondingly, typhoon wave loading is calculated using stochastic wave theory with the Joint North Sea Wave Project (JONSWAP) spectrum. A rigorous Finite Element Model (FEM) incorporating soil–structure interaction (SSI) and water-pile interaction is implemented in the Opensees platform. The SSI is modeled using nonlinear Beam on Nonlinear Winkler Foundation (BNWF) elements (PySimple1, TzSimple1, QzSimple1). Numerical simulations demonstrate that the TMD effectively mitigates dynamic platform responses under aligned typhoon and wave conditions. Specifically, the maximum deck acceleration in the X-direction is reduced by 26.19% and 31.58% under these aligned loads, with a 17.7% peak attenuation in base shear. For misaligned conditions, the TMD exhibits pronounced control over displacements in both X- and Y-directions, achieving reductions of up to 29.4%. Sensitivity studies indicated that the TMD’s effectiveness is more significantly impacted by stiffness detuning than mass detuning. It should be emphasized that the effectiveness verification of linear TMD is limited to the load levels within the design limits; for the load conditions that trigger extreme structural nonlinearity, its performance remains to be studied. This research provides theoretical and practical references for multi-directional coupled vibration control of deep-water jacket platforms in extreme marine environments. Full article

Seasonal climate forecasts are an essential tool for providing early insight into weather-related impacts and supporting decision-making in sectors such as agriculture, energy, and disaster management. Accurate representation of atmospheric circulation at the seasonal scale is essential, especially in regions such as the Eastern Mediterranean, where complex synoptic patterns drive significant climate variability. The aim of this study is to perform a comparison of weather type classifications between ERA5 reanalysis and seasonal forecasts in order to assess the ability of seasonal data to capture the synoptic patterns over the Eastern Mediterranean. For this purpose, we introduce a regional seasonal forecasting framework based on the state-of-the-art Advanced Research WRF (WRF-ARW) model. A series of sensitivity experiments were also conducted to evaluate the robustness of the model’s performance under different configurations. Moreover, the ability of seasonal data to reproduce observed trends in weather types over the historical period is also examined. The classification results from both ERA5 and seasonal forecasts reveal a consistent dominance of anticyclonic weather types throughout most of the year, with a particularly strong signal during the summer months. Model evaluation indicates that seasonal forecasts achieve an accuracy of approximately 80% in predicting the daily synoptic condition (cyclonic or anticyclonic) up to three months in advance. These findings highlight the promising skill of seasonal datasets in capturing large-scale circulation features and their associated trends in the region. Full article

This study investigates the impact of desert dust on precipitation patterns using multi-model simulations. Dust-based processes of formation/removal of ice nuclei (IN) and cloud condensation nuclei (CCN) are investigated by using both the online access model WRF-CHIMERE and the online integrated model WRF-Chem. Comparisons of model predictions with rainfall measurements (GRISO: Spatial Interpolation Generator from Rainfall Observations) over the Italian peninsula show the models’ ability to reproduce heavy orographic precipitation in alpine regions. To quantify the impact of the mineral dust transport concomitant to the atmospheric river (AR) on cloud formation, a sensitivity study is performed by using the WRF-CHIMERE model (i) by setting dust concentrations to zero and (ii) by modifying the settings of the Thompson Aerosol-Aware microphysics scheme. Statistical comparisons revealed that WRF-CHIMERE outperformed WRF-Chem. It achieved a correlation coefficient of up to 0.77, mean bias (MB) between +3.56 and +5.01 mm/day, and lower RMSE and MAE values (~32 mm and ~22 mm, respectively). Conversely, WRF-Chem displayed a substantial underestimation, with an MB of −25.22 mm/day and higher RMSE and MAE values. Our findings show that, despite general agreement in spatial precipitation patterns, both models significantly underestimated the peak daily rainfall in pre-alpine regions (e.g., 216 mm observed at Malga Valine vs. 130–140 mm simulated, corresponding to a 35–40% underestimation). Although important instantaneous changes in precipitation and temperature were modeled at a local scale, no significant total changes in precipitation or air temperature averaged over the entire domain were observed. These results underline the complexity of aerosol–cloud interactions and the need for improved parameterizations in coupled meteorological models. Full article

The primary contribution of this study lies in analyzing the dynamic drivers during two anomalous sea level rise events in the Bohai Sea through coupled numeric modeling using the Weather Research and Forecasting (WRF) model and the Finite-Volume Community Ocean Model (FVCOM) integrated with the Simulating Waves Nearshore (SWAN) module (hereafter referred to as FVCOM-SWAVE). WRF-derived wind speeds (0.05° grid resolution) were validated against Haiyang-2 (HY-2) scatterometer observations, yielding a root mean square error (RMSE) of 1.88 m/s and a correlation coefficient (Cor) of 0.85. Similarly, comparisons of significant wave height (SWH) simulated by FVCOM-SWAVE (0.05° triangular mesh) with HY-2 altimeter data showed an RMSE of 0.67 m and a Cor of 0.84. Four FVCOM sensitivity experiments were conducted to assess drivers of sea level rise, validated against tide gauge observations. The results identified tides as the primary driver of sea level rise, with wind stress and elevation forcing (e.g., storm surge) amplifying variability, while currents exhibited negligible influence. During the two events, i.e., 20–21 October and 25–26 August 2024, elevation forcing contributed to localized sea level rises of 0.6 m in the northern and southern Bohai Sea and 1.1 m in the southern Bohai Sea. A 1 m surge in the northern region correlated with intense Yellow Sea winds (20 m/s) and waves (5 m SWH), which drove water masses into the Bohai Sea. Stokes transport (wave-driven circulation) significantly amplified water levels during the 21 October and 26 August peak, underscoring critical wave–tide interactions. This study highlights the necessity of incorporating tides, wind, elevation forcing, and wave effects into coastal hydrodynamic models to improve predictions of extreme sea level rise events. In contrast, the role of imposed boundary current can be marginalized in such scenarios. Full article

This study explores the relationship between land use and land cover (LULC) changes and a significant cyclogenesis event that occurred in Alabama’s Black Belt region from 6 to 7 October 2021. Utilizing the Weather Research and Forecasting (WRF) model, two scenarios were analyzed: the WRF Control Run, which maintained unchanged LULC, and the WRF Sensitivity Experiment, which converted 56.5% of forested areas into cropland to assess the impact on storm dynamics. Quantitative comparisons of predicted rainfall from both simulations were conducted against observed data. The control run demonstrated a Root Mean Square Error (RMSE) of 1.64, indicating accurate rainfall predictions. In contrast, the modified scenario yielded an RMSE of 2.01, suggesting lower reliability. The Mean Bias (MB) values were 1.32 for the control run and 1.58 for the modified scenario, revealing notable discrepancies in accuracy. The coefficient of determination (R²) was 0.247 for the control run and 0.270 for the modified scenario. The Nash–Sutcliffe Efficiency (NSE) value was 0.1567 for the control run but dropped to −0.2257 following LULC modifications. Sensitivity analyses revealed a 60% increase in heat flux and a 36% rise in precipitation, underscoring the significant impact of LULC on meteorological outcomes. While this study concentrated on the Black Belt region, the methodologies employed could apply to various other areas, though caution is advised when generalizing these results to different climates and socio-economic contexts. Further research is necessary to enhance the model’s applicability across diverse environments. Full article

Accurately simulating near-surface wind speeds is indispensable for wind energy development, particularly under extreme weather conditions. This study utilizes the Weather Research and Forecasting (WRF) model with a 6 km resolution to evaluate 80 m wind speed simulations over Europe, using the ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis version 5 (ERA5) as initial and lateral boundary conditions. Two cases were analyzed: a normal case with relatively weak winds, and an extreme case with intense cyclonic activity over 7 days, focusing on offshore wind farm regions and validated against Forschungsplattformen in Nord- und Ostsee (FINO) observational data. Sensitivity experiments were conducted by modifying key physical parameterizations associated with wind simulation to assess their impact on accuracy. Results reveal that while the model realistically captured temporal wind speed variations, errors were significantly amplified in extreme cases, with overestimation in weak wind regimes and underestimation in strong winds (approximately 1–3 m/s). The Asymmetrical Convective Model 2 (ACM2) planetary boundary layer (PBL) scheme demonstrated superior performance in extreme cases, while there were no significant differences among experiments under normal cases. These findings emphasize the critical role of physical parameterizations and the need for improved modeling approaches under extreme wind conditions. This research contributes to developing reliable wind speed simulations, supporting the operational stability of wind energy systems. Full article

On 27 February 2023, the municipality of Itajubá in southeastern Brazil experienced a short-duration yet high-intensity rainfall event, causing significant socio-economic impacts. Hence, this study evaluates the performance of the Weather Research and Forecasting (WRF) model in simulating this extreme event through a set of sensitivity numerical experiments. The control simulation followed the operational configuration used daily by the Center for Weather and Climate Forecasting Studies of Minas Gerais (CEPreMG). Additional experiments tested the use of different microphysics schemes (WSM3, WSM6, WDM6), initial and boundary conditions (GFS, GDAS, ERA5), and surface datasets (sea surface temperature and soil moisture from ERA5 and GDAS). The model’s performance was evaluated by comparing the simulated variables with those from various datasets. We primarily focused on the representation of the spatial precipitation pattern, statistical metrics (bias, Pearson correlation, and Kling–Gupta Efficiency), and atmospheric instability indices (CAPE, K, and TT). The results showed that none of the simulations accurately captured the amount and spatial distribution of precipitation over the region, likely due to the complex topography and convective nature of the studied event. However, the WSM3 microphysics scheme and the use of ERA5 SST data provided slightly better representation of instability indices, although these configurations still underperformed in simulating the rainfall intensity. All simulations overestimated the instability indices compared to ERA5, although ERA5 itself may underestimate the convective environments. Despite some performance limitations, the sensitivity experiments provided valuable insights into the model’s behavior under different configurations for southeastern Brazil—particularly in a convective environment within mountainous terrain. However, further evaluation across multiple events is recommended. Full article

This study investigates the effectiveness of the large eddy simulation version of the Weather Research and Forecasting model (WRF-LES) in reproducing the atmospheric conditions observed during a Perdigão field experiment. When comparing the results of the WRF-LES with observations, using LES settings can accurately represent both large-scale events and the specific characteristics of atmospheric circulation at a small scale. Six sensitivity experiments are performed to evaluate the impact of different planetary boundary layer (PBL) schemes, including the MYNN, YSU, and Shin and Hong (SH) PBL models, as well as large eddy simulation (LES) with Smagorinsky (SMAG), a 1.5-order turbulence kinetic energy closure (TKE) model, and nonlinear backscatter and anisotropy (NBA) subgrid-scale (SGS) stress models. Two case studies are selected to be representative of flow conditions. In the northeastern flow, the MYNN NBA simulation yields the best result at a height of 100 m with an underestimation of 3.4%, despite SH generally producing better results than PBL schemes. In the southwestern flow, the MYNN TKE simulation at station Mast 29 is the best result, with an underestimation of 1.2%. The choice of SGS models over complex terrain affects wind field features in the boundary layer more than above the boundary layer. The NBA model generally produces better results in complex terrain when compared to other SGS models. In general, the WRF-LES can model the observed flow with high-resolution topographic maps in complex terrain with different SGS models for both flow regimes. Full article

Tropical cyclones (TCs) form over warm ocean surfaces and are driven by complex air–sea interactions, posing significant challenges to their forecasting. Accurate parameterization of physical processes is crucial for enhancing the precision of TC predictions. In this study, we employed the Weather Research and Forecasting model coupled with the Simulating Waves Nearshore (WRF-SWAN) model to forecast Typhoon Doksuri (2023), which exhibited a secondary intensification process in the South China Sea (SCS). We also investigated its sensitivity to various atmospheric physical parameterization schemes (PPS). The findings indicate that improvements in microphysical and cumulus convection parameterizations have significantly enhanced the prediction accuracy of Typhoon Doksuri’s trajectory and intensity. The simulation of sea surface heat flux is primarily influenced by the microphysical scheme, while the cumulus convection scheme substantially affects the representation of the typhoon core’s size and shape. Variations in the wind field induce differences in wave height, potentially reaching up to 2–3 m at any given moment. This study provides valuable insights into the effective selection of physical parameterizations for improving typhoon forecasts. Full article

This study evaluates the sensitivity of the Weather Research and Forecasting (WRF-ARW) model in its version 4.3.3 during different experiments on a torrential precipitation event associated with the 2017 El Niño Costero in Peru. The results are compared with two reference datasets: precipitation estimations from CHIRPS satellite data and SENAMHI meteorological station values. The event, which had significant economic and social impacts, is simulated using two nested domains with resolutions of 9 km (d01) and 3 km (d02). A total of 22 experiments are conducted, resulting from the combination of two planetary boundary layer (PBL) schemes: Yonsei University (YSU) and Mellor–Yamada–Janjic (MYJ), with five cumulus parameterization schemes: Betts–Miller–Janjic (BMJ), Grell–Devenyi (GD), Grell–Freitas (GF), Kain–Fritsch (KF), and New Tiedtke (NT). Additionally, the effect of turning off cumulus parameterization in the inner domain (d02) or in both (d01 and d02) is explored. The results show that the YSU scheme generally provides better results than the MYJ scheme in detecting the precipitation patterns observed during the event. Furthermore, it is concluded that turning off cumulus parameterization in both domains produces satisfactory results for certain regions when it is combined with the YSU PBL scheme. However, the KF cumulus parameterization is considered the most effective for intense precipitation events in this region, although it tends to overestimate precipitation in high mountain areas. In contrast, for lighter rains, combinations of the YSU PBL scheme with the GD or NT parameterization show a superior performance. It is worth nothing that for all experiments here used, there is a clear underestimation in terms of precipitation, except in high mountain regions, where the model tends to overestimate rainfall. Full article

The primary goal of this study is to introduce the initial phase of developing an impact-based forecasting system for extreme heatwaves, utilizing a novel multivariate index which, at this early stage, already employs a combination of a statistical approach and physical principles related to human body water loss. This system also incorporates a mitigation plan with hydration-focused measures. Since 1990, heatwaves have become increasingly frequent and intense across many regions worldwide, particularly in Europe and Asia. The main health impacts of heatwaves include organ strain and damage, exacerbation of cardiovascular and kidney diseases, and adverse reproductive effects. These consequences are most pronounced in individuals aged 65 and older. Many national meteorological services have established metrics to assess the frequency and severity of heatwaves within their borders. These metrics typically rely on specific threshold values or ranges of near-surface (2 m) air temperature, often derived from historical extreme temperature records. However, to our knowledge, only a few of these metrics consider the persistence of heatwave events, and even fewer account for relative humidity. In response, this study aims to develop a globally applicable normalized index that can be used across various temporal scales and regions. This index incorporates the potential health risks associated with relative humidity, accounts for the duration of extreme heatwave events, and is exponentially sensitive to exposure to extreme heat conditions above critical thresholds of temperature. This novel index could be more suitable/adapted to guide national meteorological services when emitting warnings during extreme heatwave events about the health risks on the population. The index was computed under two scenarios: first, in forecasting heatwave episodes over a specific temporal horizon using the WRF model; second, in evaluating the relationship between the index, mortality data, and maximum temperature anomalies during the 2003 summer heatwave in Spain. Moreover, the study assessed the annual trend of increasing extreme heatwaves in Spain using ERA5 data on a climatic scale. The results show that this index has considerable potential as a decision-support and health risk assessment tool. It demonstrates greater sensitivity to extreme risk episodes compared to linear evaluations of extreme temperatures. Furthermore, its formulation aligns with the physical mechanisms of water loss in the human body, while also factoring in the effects of relative humidity. Full article

The continental United States is a global hotspot of severe thunderstorms and therefore is particularly vulnerable to social and economic damages from high-impact severe convective weather (SCW), such as tornadoes, thunderstorm winds, and large hail. However, our knowledge of the spatiotemporal climatology and variability of SCW occurrence is still lacking, and the potential change in SCW frequency and intensity in response to anthropogenic climate warming is highly uncertain due to deficient and sparse historical records and the global and regional climate model’s inability to resolve thunderstorms. This study investigates SCW in the Central and Eastern United States in spring and early summer for the current and future warmed climate using two multi-year continental-scale convection-permitting Weather Research and Forecasting (WRF) model simulations. The pair of simulations consist of a retrospective simulation, which downscales the ERA-Interim reanalysis during October 2000–September 2013, and a future climate sensitivity simulation based on the perturbed reanalysis-derived boundary conditions with the CMIP5 ensemble-mean high-end emission scenario climate change. A proxy based on composite reflectivity and updraft helicity threshold is applied to infer the simulated SCW occurrence. Results indicate that the retrospective simulation captures reasonably well the spatial distributions and seasonal variations of the observed SCW events, with an exception of an overestimate along the Atlantic and Gulf coast. In a warmer-moister future, most regions experience intensified SCW activity, most notably in the early-middle spring, with the largest percentage increase in the foothills and higher latitudes. In addition, a shift of simulated radar reflectivity toward higher values, in association with the significant thermodynamic environmental response to climatic warming, potentially increases the SCW severity and resultant damage. Full article

This research examines the impact of various parameterization settings within the Weather Research and Forecasting (WRF) model on the accuracy of short-term weather forecasts for Poland. The study focuses on the sensitivity of key meteorological variables—namely, air temperature, wind speed, relative humidity, and atmospheric pressure—to different combinations of physical parameterization schemes. Utilizing data from the Global Forecast System (GFS) spanning 2019 to 2022, a series of model simulations were conducted with support from the Poznań Supercomputing and Networking Center (PCSS). To assess the model’s performance across different weather stations, statistical metrics such as the mean absolute error (MAE) and root mean square error (RMSE) were employed. The findings indicate that the configuration labeled “p2” produced the most accurate forecasts for temperature, wind speed, and atmospheric pressure, achieving MAE values of 1.5 °C, 1.6 m/s, and 2 hPa, respectively. However, forecast inaccuracies were notably higher in mountainous regions, particularly regarding wind speed. These results underscore the importance of selecting appropriate parameterization settings tailored to regional characteristics, as different configurations can significantly impact the forecast accuracy, especially in complex terrains. This study contributes to the understanding of short-term weather forecasting models for Central Europe, offering potential pathways for improving localized forecast accuracy. Full article

This study employed version 4.2.2 of the Weather Research and Forecasting (WRF) model for this simulation and applied two microphysics schemes, the Thompson scheme (THOM) and Milbrandt–Yau scheme (MY)—which are widely used in convective simulations—to simulate a mesoscale severe convective precipitation event that occurred in southeastern China on 8 May 2017. The simulations were then compared with dual-polarization radar observations using a radar simulator. It was found that THOM produced vertical structures of radar reflectivity (Z_H) closer to radar observations and accumulated precipitation more consistent with ground-based observations. However, both schemes overestimated specific differential phase (K_DP) and differential reflectivity (Z_DR) below the 0 °C level. Further analysis indicated that THOM produced more rain with larger raindrop sizes below the 0 °C level. Due to the close connection between raindrop breakup, evaporation rate, and raindrop size, sensitivity experiments on the breakup threshold (D_b) and the evaporation efficiency (E_E) of the THOM scheme were carried out. It was found that adjusting D_b significantly changed the simulated raindrop size distribution and had a certain impact on the strength of cold pool; whereas modifying E_E not only significantly changed the intensity and scope of the cold pool, but also had great effect on the raindrop size distribution. At the same time, comparison with dual-polarization radar observations indicated that reducing the D_b can improve the model’s simulation of polarimetric radar variables such as Z_DR. This paper specifically analyzes a severe convective precipitation event in the Guangdong region under weak synoptic conditions and a humid climate. It demonstrates the feasibility of a method based on polarimetric radar data that modifies D_b of THOM to achieve better consistency between simulations and observations in southeast China. Since the microphysical processes of different Mesoscale Convective Systems (MCSs) vary, the generalizability of this study needs to be validated through more cases and regions in the future. Full article