Search Results (818)

This study investigates the characteristics, meteorological drivers, and transport pathways of air pollution in Yining City from 2020 to 2024 based on meteorological records and air pollutant monitoring data. An integrated modeling approach combining the Weather Research and Forecasting (WRF) model and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was employed. Results reveal an overall annual decrease in ambient pollutant concentrations in Yining, with PM_2.5 and PM₁₀ consistently below the national secondary standards, In contrast, the O₃ concentration shows a marked yearly increase. Pronounced seasonal variations were identified: the elevated O₃ concentrations in summer were driven by high temperatures and intense solar radiation. The significant increase in PM_2.5 and PM₁₀ concentrations during winter was predominantly attributed to coal-based heating emissions and temperature inversion conditions. Pollutant concentrations were strongly associated with gaseous precursors (e.g., CO and NO₂) and meteorological factors. Higher temperatures and lower relative humidity aggravated O₃ formation, whereas lower temperatures and higher relative humidity favored PM_2.5 pollution. Correlation analysis revealed that NO₂ and CO showed the strongest correlations with PM_2.5 (r = 0.84) and O₃ (r = −0.62), respectively. Backward trajectory analysis revealed that higher pollution levels were associated with air masses originating from the southwest and southeast. Full article

This study investigates the sensitivity of an urban parameterization scheme of the Weather Research and Forecasting model (WRF). The model sensitivity is tested during the period April–May 2020 over the greater Paris region. The parent domain covers Europe with a 12 km horizontal resolution, with a nested one covering the greater Paris region with a 3 km horizontal resolution. A multi-layer urban scheme called Building Effect Parameterization coupled with the Building Energy Model (BEP-BEM) was applied in two simulations: (1) BEP-BEM Paris, with urban options tailored for the Paris region, which were derived from Earth Observation data, and (2) BEP-BEM Europe, which uses an updated urban parameter table with an estimated average profile for European cities. These two simulations were compared with observations and a WRF simulation using a simple urban parameterization (BULK approach). BULK and multi-layer urban scheme experiments present a similar general error for April, underestimating temperature, while the BEP-BEM runs overestimate temperature for May. The simulation with the advanced tailored urban parameterization over Paris appears to have the best overall performance in this 2-month period. Full article

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

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

Low-level wind shear (LLWS) is a critical issue in aviation meteorology, posing serious risks to flight safety—especially at plateau airports with high elevation and complex terrain. This study investigates a convective wind shear event at Xining Airport on 29 May 2021. Multi-source observations—including the Doppler Wind Lidar (DWL), the Doppler weather radar (DWR), reanalysis datasets, and automated weather observation systems (AWOS)—were integrated to examine the event’s fine-scale structure and temporal evolution. High-resolution simulations were conducted using the Large Eddy Simulation (LES) framework within the Weather Research and Forecasting (WRF) model. Results indicate that the formation of this wind shear was jointly triggered by convective downdrafts and the gust front. A northwesterly flow with peak wind speeds of 18 m/s intruded eastward across the runway, generating multiple radial velocity couplets on the eastern side, closely associated with mesoscale convergence and divergence. A vertical shear layer developed around 700 m above ground level, and the critical wind shear during aircraft go-around was linked to two convergence zones east of the runway. The event lasted about 30 min, producing abrupt changes in wind direction and vertical velocity, potentially causing flight path deviation and landing offset. Analysis of horizontal, vertical, and glide-path wind fields reveals the spatiotemporal evolution of the wind shear and its impact on aviation safety. The WRF-LES accurately captured key features such as wind shifts, speed surges, and vertical disturbances, with strong agreement to observations. The integration of multi-source observations with WRF-LES improves the accuracy and timeliness of wind shear detection and warning, providing valuable scientific support for enhancing safety at plateau airports. Full article

Summer sea breezes provide cooling in coastal cities; however, their temporal cooling distribution and inland penetration distance remain inadequately studied. This study employed the mesoscale Weather Research and Forecasting (WRF) model to analyze the sea breeze cooling capacity (SBCC) in detail. The results identified the distance from the coast, cooling timing, and proximity to inland rivers as key factors influencing the SBCC. The cooling range and intensity of sea breezes exhibited a temporal pattern, initially increasing and then decreasing, with the rate of increase significantly exceeding the decline. The maximum cooling range (277.44 km²) and strongest cooling intensity (37,989.61 °C.h) occurred at 10:00. Between 11:00 and 14:00, the cooling effect remained stable over its longest inland distance (16.2 km). The SBCC intensified notably closer to the coastline. Furthermore, inland rivers significantly enhanced the cooling effect, with the sea breeze penetration distance correlating positively with the proximity to these rivers. A detailed analysis of the SBCC’s spatial extent and cooling distance provides a crucial basis for effectively mitigating urban heat in coastal cities. Full article

The CYGMEN (Cyprus GNSS Meteorology Enhancement) infrastructure project aims to establish a meteorological cluster (CyMETEO) in Cyprus of a lightning detection network, a dense GNSS (Global Navigation Satellite System) network for atmospheric water vapor estimation, a Radar Wind Profiler, and a microwave radiometer. Additionally, observational data generated by CyMETEO infrastructure will be assimilated into the Weather Research and Forecasting (WRF) model with the aim of improving short-term weather forecasting. The preliminary results of precipitable water vapor (PWV) estimation by employing (a) a GNSS network, (b) a microwave radiometer, (c) radiosonde, and (d) ERA5 reanalysis datasets over the Athalassas super-site in Nicosia, during May 2025, are intercompared in this study. Full article

Understanding and predicting the distribution of mineral dust in the atmosphere remains a major scientific challenge due to the complex nature of dust emission, transport, and deposition processes. Dust aerosols have a profound impact on climate, air quality, and biogeochemical cycles, making their accurate representation in models critical. In this study, we employ the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate dust events over the Mediterranean. To reduce model uncertainties, we assimilate satellite-derived dust optical depth observations from the MIDAS (Mineral Dust Aerosol Satellite) dataset. The assimilation of MIDAS data leads to significant improvements in the spatial and temporal accuracy of dust forecasts. The enhanced model outputs offer continuous in time and space dust fields that are particularly valuable for applications such as air quality management and the optimization of solar energy systems. Full article

Amid intensifying global climate change, coastal cities are facing increased heat stress. The sea breeze plays a crucial role in mitigating the urban heat island effect and improving outdoor thermal comfort, warranting detailed investigation of its spatiotemporal impacts. This research, conducted in Sendai, Japan, combines the Weather Research and Forecasting (WRF) model with the Rayman thermal comfort model to assess the spatiotemporal evolution of the Physiological Equivalent Temperature (PET) on typical sea breeze days, exploring heat stress patterns. The findings indicate significant PET reductions in the area due to sea breeze influence, although high heat stress persists in urban centers. The coastal zone (0–4 km) experiences the longest period of low heat stress, whereas the inland zone (20–26 km) suffers from poor thermal comfort. Heat stress intensifies in the northwestern inland regions, while improvement progresses from the coast inland. Vegetated areas reach low heat stress states earlier than built-up areas; both coastal and urban zones quickly revert to “no heat stress” conditions. The results demonstrate that the cooling effect of sea breezes decreases with distance, its efficacy hindered by urban environments, whereas vegetated lands prolong comfort inland. These insights are crucial for planning thermal environments in coastal cities. Full article

Squall lines are some of the most common types of mesoscale cloud systems in tropical and subtropical regions. Thunderstorms associated with these systems are among the major causes of weather-related disasters and socio-economic losses in many regions across the world. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating squall line features over the South African Highveld region. Two squall line cases were selected based on the availability of South African Weather Service (SAWS) weather radar data: 21 October 2017 (early austral summer) and 31 January–1 February 2018 (late austral summer). The European Centre for Medium-Range Weather Forecasts ERA5 datasets were used as observational proxies to analyze squall line features and compare them with WRF simulations. Mid-tropospheric perturbations were observed along westerly waves in both cases. These perturbations were coupled with surface troughs over central interior together with the high-pressure systems to the south and southeast of the country creating strong pressure gradients over the plateau, which also transports relative humidity onshore and extending to the Highveld region. The 2018 case also had a zonal structured ridging High, which was responsible for driving moisture from the southwest Indian Ocean towards the eastern parts of South Africa. Both ERA5 and WRF captured onshore near surface (800 hPa) winds and high-moisture contents over the eastern parts of the Highveld. A well-defined dryline was observed and well simulated for the 2017 event, while both ERA5 and WRF did not show any dryline for the 2018 case that was triggered by orography. While WRF successfully reproduced the synoptic-scale processes of these extreme weather events, the simulated rainfall over the area of interest exhibited a broader spatial distribution, with large-scale precipitation overestimated and convective rainfall underestimated. Our study shows that models are able to capture these systems but with some shortcomings, highlighting the need for further improvement in forecasts. Full article

This study investigates the impact of assimilating FY-3D Microwave Radiation Imager (MWRI) radiance data into the Weather Research and Forecasting (WRF) model, utilizing a 3D-Var data assimilation system, on the forecast accuracy of Typhoon Muifa (2022). The research focuses on the selection of data from different channels, land/ocean coverage, and orbits of the MWRI, along with the synergistic assimilation strategy with MWHS-2 data. Ten assimilation experiments were conducted, starting from 0600 UTC on 14 September 2022, covering a 42 h forecast period. The results show that after assimilating the microwave radiometer data, the brightness temperature deviation in the ocean area was significantly reduced compared to the simulation without data assimilation. This led to an improvement in the accuracy of typhoon track and intensity predictions, particularly for predictions beyond 24 h. Furthermore, the assimilation of land data and single-orbit data (particularly from the western orbit) further enhanced forecast accuracy, while the joint assimilation of MWHS-2 and MWRI data yielded additional error reductions. These findings underscore the potential of satellite data assimilation in improving typhoon forecasting and highlight the need for optimal land observation and channel selection techniques. Full article

Improving tropical cyclone (TC) track forecasts is critical for enhancing disaster prevention and mitigation efforts. This study evaluates the effectiveness of the spectral nudging (SN) technique in simulating TC tracks with diverse path patterns over the Western North Pacific using the Weather Research and Forecasting (WRF) model. The results show that the SN technique is remarkably effective in improving tropical cyclone track forecasts for all types of regular track patterns, except for irregular tracks. Specifically, spectral nudging reduced simulated mean track position errors by approximately 60%, 67%, and 77% on average for curving, northwestward-, and westward-moving tracks, respectively. Better simulations of large-scale flow dynamics contributed to these improvements, particularly in scenarios where the subtropical high underwent rapid changes in its circulation patterns. For irregular tracks, applying the SN technique showed mixed results, ranging from 75% error reduction to 20% error increase. This implies that the effectiveness of spectral nudging on the simulation of irregular tracks is case dependent. Since the effectiveness of spectral nudging depends on the scales (spectrum) of the underlying processes creating the irregularities of the tracks, when such irregularities were caused by local and regional-scale factors, spectral nudging became ineffective. Full article

The Three-River Source Region (TRSR) of the Tibetan Plateau (TP) is a critical headwater area with complex alpine terrain and significant climate variability. Accurately simulating 2 m air temperature (T₂) in this region remains challenging for models such as the Weather Research and Forecasting (WRF) model. This study integrated remote sensing data into the WRF model to improve T₂ simulations over the TRSR. Two simulations were conducted for 2020: a control simulation with default static vegetation parameters (EXP_control) and a sensitivity simulation with realistic vegetation and associated surface albedo of 2020 from the Global Land Surface Satellite (GLASS) datasets (EXP_glass). Results showed that incorporating the GLASS-derived datasets significantly alleviated the cold bias in simulated T₂ during winter and spring, while maintaining comparable performance in summer and autumn. The EXP_glass run achieved better agreement with observations (R = 0.98, p < 0.01) and reduced root-mean-square error (RMSE) by 36.4% compared to EXP_control. Energy balance analysis indicated that the GLASS-derived datasets enhanced solar energy absorption and increased net radiation. Consequently, EXP_glass produced greater turbulent heat fluxes and warmer surface skin temperature (TSK) and T₂. This study underscores the importance of accurate land surface characterization and highlights the utility of remote sensing data for improving regional climate model performance in high-altitude regions. Full article

The online coupling of aerosols and clouds and its effect on surface global horizontal irradiance (GHI) has not yet been thoroughly investigated in the Weather Research and Forecasting Model with Solar extensions (WRF-Solar), despite its potential significance for solar energy applications. This study addresses this critical gap by implementing a computationally efficient, coupled aerosol–cloud scheme and evaluating its impacts on GHI predictability. Simulations with online aerosol–cloud coupling are systematically compared to uncoupled simulations during March 2021, a period marked by two distinct pollution episodes over north China. The online coupling enhances aerosol optical depth (AOD) simulations, increasing the correlation coefficient from 0.19 to 0.51 while reducing the absolute bias from 0.54 to 0.48 and root mean square error from 0.82 to 0.72, compared to uncoupled simulations. Enhanced cloud microphysics (droplet concentration, water path) yields better cloud optical depth estimates, reducing all-sky GHI bias by 14.5% (63.5 W/m² for the uncoupled scenario and 54.3 W/m² for the coupled scenario) through improved aerosol–cloud–meteorology interactions. Notably, the simultaneous spatiotemporal improvement of both AOD and GHI suggests enhanced internal consistency in aerosol–cloud–radiation interactions, which is crucial for operational solar irradiance forecasting in pollution-prone regions. The results also highlight the practical value of incorporating online aerosol coupling in solar forecasting models. Full article

As global climate change accelerates, the urban heat island (UHI) phenomenon has become increasingly pronounced, posing significant challenges to urban energy balance, atmospheric processes, and public health. This study used the Weather Research and Forecasting (WRF) model to dynamically downscale two CMIP6 scenarios—moderate forcing (SSP245) and high forcing (SSP585)—focusing on Zhengzhou, a rapidly urbanizing city in central China. High-resolution simulations captured fine-scale intra-urban temperature patterns and analyze the spatial and seasonal variations in UHI intensity in 2030 and 2060. The results demonstrated significant seasonal variations in UHI effects in Zhengzhou for both 2030 and 2060 under SSP245 and SSP585 scenarios, with the most pronounced warming in summer. Notably, under the SSP245 scenario, elevated autumn temperatures in suburban areas reduced the urban–rural temperature gradient, while intensified rural cooling during winter enhanced the UHI effect. These findings underscore the importance of integrating high-resolution climate modeling into urban planning and developing targeted adaptation strategies based on future UHI patterns to address climate challenges. Full article