Search Results (855)

Air pollution poses significant risks to public health, ecosystems, and regional economies, particularly in rapidly developing regions. Despite its importance, the Middle East remains relatively understudied in regional air quality, with limited evaluations of pollutant transport and model performance. This study applies the WRF (Weather Research and Forecasting) model coupled with the CAMx (Comprehensive Air Quality Model with Extensions) model to simulate meteorology and air quality over West Asia, with a focus on the United Arab Emirates (UAE). Six representative months are analyzed, including three winter periods (January 2018, 2020, 2022) and three summer periods (June 2017, 2019, 2021). WRF shows good agreement with observations, reproducing near-surface temperature with an index of agreement (IOA) between 0.90 and 1.00 and generally low wind speed (MB < ±0.5 m s⁻¹) and wind direction biases (MB < ±0.5), although cloud-radiative forcing is underestimated during winter. CAMx reproduces PM_2.5 concentrations with moderate-to-high correlations (r = 0.44–0.65) and low bias, while AOD and O₃ column concentration show larger uncertainties. Satellite-based evaluation indicates good performance for NO₂ and CO column abundances but larger discrepancies for HCHO and SO₂, particularly during summer. Overall, the results demonstrate that the WRF-CAMx modeling system provides a reliable framework for regional air quality simulations over West Asia, while highlighting uncertainties associated with emissions, atmospheric chemistry, and satellite retrieval products. Full article

Quantifying the impact of land use changes on the threat of flash-floods is a critical consideration in flood hazard planning and risk reduction, and is an area of active research. Here, a coupled Weather Research and Forecasting model hydrological extension package (i.e., WRF-Hydro) modeling approach is applied to simulate flash-flooding processes for short-duration, localized, intense precipitation events. To better understand the effect of urbanization on flash floods, a series of numerical experiments is performed surrounding Ellicott City, Maryland, a location which has experienced both significant heavy rainfall events and suburban development over the past several decades. Two intense rainfall events occurring on 30 July 2016 and 27 May 2018 are investigated, respectively, to first calibrate the hydrologic model performance and then quantify the sensitivity of flash flooding to varying degrees of urbanization. Performing the same experiments using observed historical land use states is of more limited insight, as the thrust of suburban development in the Ellicott City region significantly predates satellite-derived land use datasets. Results confirm that urbanization produces larger river streamflow, higher water stages, faster hydrologic responses to achieve peak flow discharge, and shorter recession limbs, even for very intense, short-duration events. The collective findings suggest that WRF-Hydro is applicable for both watershed flash flood prediction and hypothesis testing, and demonstrates potential utility to urban development decision-makers in locations such as Ellicott City, which could face future increases in catastrophic flooding. Full article

Landfalling typhoons are the dominant trigger of short-duration extreme rainfall along the Zhejiang coast. It is necessary to estimate the recurrence of future typhoon rainfall at the city scale under the global-warming scenarios. Using Super Typhoon Lekima (2019) as a representative high-impact event, this study develops an event-based assessment framework for Taizhou city by combining the Weather Research and Forecast (WRF) model simulation, pseudo-global-warming (PGW) perturbation experiments, and generalized extreme value analysis. The historical simulation is first evaluated against the China Meteorological Administration best track, storm intensity evolution, and station rainfall observations. Future counterparts of the same event are then generated using CMIP6-derived thermodynamic perturbations under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Finally, scenario-dependent rainfall totals are projected onto a historical GEV curve to identify equivalent historical rainfall return periods. Results show that the WRF setup reproduces the main track, intensity tendency, and rainfall timing of Lekima with reasonable fidelity. The ensemble-mean cumulative rainfall over the Taizhou area increases from 204.75 mm in the historical simulation to 335.85, 366.72, 400.79, and 464.08 mm under the four SSPs, respectively. These increases translate into equivalent historical rainfall return periods of 47.40, 84.61, 164.28, and 604.05 years, compared with 5.24 years for the historical case. The results indicate that the moderate thermodynamic rainfall amplification produces a highly nonlinear escalation of event rarity based on historical frequency statistics. This implies that future typhoon rainfall should be interpreted using scenario-aware benchmarks within the historical reference framework. Full article

Assessing the contribution of tropical cyclones to regional precipitation variability is essential for understanding the associated hydrometeorological benefits and risks. This study quantifies the contribution of tropical cyclones to annual precipitation in the northernmost part of South America from 2016 to 2025, utilizing data from surface rain gauges. Simulations using the Weather Research and Forecasting (WRF) model, configured with 2 km grid spacing and 38 vertical levels, estimate the influence of relative humidity at 850 hPa and ambient temperature at 500 hPa on precipitation over the continental region when each convective system is nearest to the coastline. During Hurricanes Matthew (2016) and Melissa (2025), contributions to the annual average precipitation reached 51% and 47%, respectively, with the highest values observed near the northern South American coastline. The contributions of Harvey (2017), Iota (2020), Julia (2022), and Beryl (2024) to annual precipitation were 0–26%, 0–18%, 0–12%, and 0–19%, respectively. Precipitation distribution was heterogeneous during the passage of tropical storms. The extent of accumulated precipitation was influenced by the cyclone’s trajectory and proximity to mountainous regions. Patterns of relative humidity at 850 hPa did not correspond to a uniform precipitation distribution. Between 6% and 30% of rain gauges did not record precipitation during the analyzed tropical cyclone events. These findings highlight that tropical cyclone-induced precipitation is strongly influenced by complex interactions between atmospheric dynamics and topography. Future research should assess the contributions of these systems to groundwater and surface reservoirs that support indigenous communities in rural areas. Full article

The Global Navigation Satellite System Reflectometry (GNSS-R) technique provides global ocean surface wind observations unaffected by rainfall with high spatiotemporal resolution. The Fengyun-3E (FY-3E) mission, as the first operational GNSS-R satellite in China, offers low-latency data suitable for numerical weather prediction (NWP). However, the dense along-track sampling of GNSS-R winds poses challenges for observation error specification in data assimilation. In this study, FY-3E GNSS-R winds are assimilated into the Weather Research and Forecasting (WRF) model to investigate the impacts of different observation error configurations. Both static and dynamic error specifications, with and without data thinning, are evaluated through a sensitivity experiment and subsequent Observing System Experiments (OSEs). The results indicate that using a static observation error of 6 m/s without data thinning achieves the best performance. Under this configuration, GNSS-R winds influence atmospheric analyses from the surface up to approximately 700 hPa in a single assimilation case, while cycling experiments further extend the impact vertically and spatially. These findings highlight the importance of appropriate observation error specification for dense GNSS-R data and provide a practical reference for their assimilation in WRF, with potential applicability to other NWP systems. Full article

Accurate wind estimation is essential for wind resource assessment. In this study, using scanning lidar measurements and high-resolution WRF simulations from two nearshore areas in Japan, we developed two extensions of the Temporal Correction (TC) method, which corrects wind fields generated by the Weather Research and Forecasting (WRF) model using on-site measurements. First, when using a single measurement point for correction, we derived two empirical formulas to predict appropriate correction coefficients based on reference–target correlation coefficients of wind speed obtained from WRF simulations and developed a method (TC-pred) using these formulas. TC-pred was shown to have higher wind speed estimation accuracy and a broader range of applicability than the conventional TC method. Next, we extended the TC-pred method to allow the use of multiple measurement points as references by introducing a weighting formula for each reference point. Wind speed estimation accuracy improved as the number of reference points increased, primarily because the probability of including reference points with high reference–target correlation coefficients increased. This suggests that it is effective for the suppression of wind estimation uncertainty to determine measurement layout such that the correlation coefficient between at least one reference point and each target point in the target area exceeds a certain value. Full article

The ocean–atmosphere turbulent heat exchange plays a critical role in the energy and moisture budgets of the Tropical Atlantic Ocean (TAO) and in weather and climate forecasts. However, its estimation strongly depends on the choice of bulk parameterization, as direct in situ measurements are sparse. This study evaluates sensible (Hs) and latent (Hl) heat fluxes derived from three bulk parameterization schemes used operationally in models at the Brazilian Center for Weather Forecast and Climate Studies (CPTEC) of the National Institute for Space Research (INPE), Brazil: the Brazilian Atmospheric Model (BAM), the Modular Ocean Model version 6 (MOM6), and the Weather Research and Forecasting (WRF) model. Using daily in situ observations from seven Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) buoys across the TAO during 1997–2023, we computed monthly mean fluxes and compared them against the Coupled Ocean–atmosphere Response Experiment (COARE) algorithm version 3.0b (COARE 3.0b) reference. COARE version 3.6 (COARE 3.6) and European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis 5th generation (ERA5) data were included as additional benchmarks. All offline schemes were forced with identical buoy data, isolating differences in internal physical assumptions. Hl is approximately one order of magnitude larger than Hs across all sites, and inter-scheme differences are substantially larger for Hl (±50 W∙m⁻²) than for Hs (±5 W∙m⁻²). All schemes reproduce the seasonal cycle linked to the Intertropical Convergence Zone (ITCZ) migration and trade-wind variability, with correlations generally exceeding 0.8 (p < 0.001) for most buoys. However, systematic magnitude biases remain. The Coordinated Ocean Research Experiments (CORE) bulk formulation implemented in MOM6 (MOM6-CORE) shows high temporal correlation (often r ≈ 1.0) but a persistent negative bias for both Hs and Hl (e.g., B1 Hl bias = −24.0 W∙m⁻²), indicating weaker turbulent exchange relative to COARE 3.0b. BAM overestimates Hs (by 1–3 W∙m⁻²) and underestimates Hl at most northern and southern sites, while the parametrization of the Yonsei University (YSU) implemented in the WRF model (WRF-YSU) amplifies Hs variability intermittently, particularly at the equator (B4). As expected, COARE 3.6 remains the closest to the reference (differences < 1 W∙m⁻² for Hs and <7 W∙m⁻² for Hl; r ≈ 0.99). ERA5 captures temporal variability well (r ≈ 0.7–0.9) but systematically overestimates Hl (positive bias up to +47.6 W∙m⁻² at B7), implying stronger evaporative cooling. Buoy-specific regimes modulate skill. The choice of bulk formulation thus remains a first-order source of uncertainty in turbulent heat flux estimates over the TAO, with direct implications for mixed-layer heat budgets, SST evolution, and coupled ocean–atmosphere variability. MOM6-CORE provides the most consistent performance relative to the COARE reference and emerges as the most robust option for operational applications at CPTEC/INPE. The findings also provide guidance for improving the representation of ocean–atmosphere turbulent exchanges in MONAN (Model for Ocean-Land-Atmosphere Prediction), the new Brazilian Earth System Model under development for weather and climate prediction. Full article

Accurate simulation of landfalling typhoons is essential for urban resilience in the densely populated Pearl River Delta. Using Super Typhoon Mangkhut (2018) as a case study, this paper evaluates the Weather Research and Forecasting (WRF) model through a contribution analysis designed to disentangle the roles of surface layer, planetary boundary layer (PBL), urban canopy model (UCM), and eddy-coefficient/diffusion closure parameterizations in wind-hazard prediction. Model results are validated against observations at the Hong Kong Observatory headquarters (HKO) and King’s Park (KP) stations, demonstrating that the hierarchy of physical controls is strongly metric-dependent. Substantial and structured spread is found among the tested configurations. Controlled comparisons show that PBL selection is the primary driver of variability in peak timing and high-wind persistence, whereas surface-layer formulation and diffusion closure exert secondary but systematic influences by shifting distributional centers and reshaping variability and upper tails. Urban canopy effects are comparatively weaker in aggregate but become more apparent during the impact and recovery phases. Overall, the results confirm that no single parameterization is consistently optimal across all metrics and motivate a multi-objective physics-selection strategy, in which multi-physics ensembles are used to better represent uncertainty in wind-event duration and associated loading risks in complex urban environments. Full article

The last atmospheric emission inventory for Cuenca, a city located in the Andean region of southern Ecuador, was developed for the year 2021 (EI 2021) and encompasses both primary pollutants (NO_x, CO, VOC, SO₂, PM₁₀, and PM_2.5) and greenhouse gases (CO₂, CH₄, and N₂O). We formally assessed the quality of this emission inventory by modeling air quality levels in October 2021 using the Weather Research and Forecasting with Chemistry (WRF-Chem 3.2) model at a high spatial resolution (1 km). Although we conducted simulations with different combinations of boundary conditions for chemical species and hourly profiles to disaggregate daily on-road traffic emissions, we selected two sets of numerical experiments to report. The results indicated that most of the assessed meteorological and air quality variables were modeled acceptably, suggesting that the EI 2021 emission inventory is a reasonable estimation of real emissions. The results also indicated the current capacity of WRF-Chem to model the atmosphere in a complex Andean city using the “one atmosphere” approach, highlighting the variables with good, fair, and poor modeling performance. We propose future research directions to improve emission inventories and the performance of atmospheric modeling in the Equatorial Andean region. Finally, the results and spatial distribution of the EI 2021 were compared to the emission data from the last version of the EDGAR Emissions Dataset (which has a spatial resolution of 11.1 km), one of the most used global emission datasets. We concluded that, for the Equatorial Andean region and for modeling purposes, the EDGAR Dataset results should be reviewed, both to account for the effects of height above sea level on the magnitude of primary pollutant emissions and their spatial configuration. Full article

Vento Norte (VNOR; Portuguese for North Wind) is a downslope windstorm that develops over modest terrain in the central region of Rio Grande do Sul (RS), southern Brazil. The regional topography is characterized by an abrupt terrain transition with elevation differences of approximately 400–500 m. This atmospheric flow typically occurs during the cold season and is characterized by strong wind gusts, rapid warming, and drying of the planetary boundary layer (PBL). In this study, the performance of different PBL parameterization schemes in the Weather Research and Forecasting (WRF) model is assessed for simulating a VNOR event that occurred between 19 and 20 August 2021 in Santa Maria (SMA), RS. Five high-resolution numerical simulations were conducted using the Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2), Mellor–Yamada–Nakanishi–Niino level 2.5 (MYNN2.5), Quasi-Normal Scale Elimination (QNSE), and Three-Dimensional Turbulent Kinetic Energy (3DTKE) PBL schemes. Model results were evaluated against observations from a flux tower providing turbulence measurements, twice-daily radiosoundings, and hourly surface meteorological observations. Statistical metrics indicate that the MYNN2.5 scheme provided the most accurate representation of the nighttime stable boundary layer preceding the VNOR, as well as its onset and subsequent evolution. Although this study analyzes a single VNOR event and the results may be case-dependent, the overall performance of the MYNN2.5 scheme suggests that it is a promising option for the operational forecasting of VNOR events. These findings provide new insights into the ability of different PBL schemes to reproduce the mean boundary-layer structure and turbulence characteristics associated with downslope windstorms over modest terrain, contributing to the understanding of these events. Full article

A microscale vortex embedded in a cold front over the Pearl River Estuary was observed by weather radars in Hong Kong on the evening of 2 March 2026. This paper presents an observational and simulation study of this vortex. In addition to the reflectivity and Doppler velocity data, the three-dimensional wind field associated with this vortex was analyzed using two radar-based analysis methods. Updrafts were present within the vortex, and the formation of the vortex appears to be related to the horizontal wind shear within the frontal zone and vertical motion triggered by a mid-tropospheric wave. Three commercial aircraft flew across the vortex at low altitude southwest of Lantau Island. Flight data showed marked fluctuations in vertical velocity, including both upward and downward air motions, together with severe turbulence within the vortex. The vortex is therefore of both meteorological interest and operational significance for aviation safety. The event was also simulated using the Weather Research and Forecasting (WRF) model with 200 m resolution. The model reproduced the observed vertical motions and turbulence intensity reasonably well in comparison with aircraft observations. Sensitivity tests with varying sea surface temperature and local terrain over Hong Kong showed no significant impact on the formation of the vortex, confirming that the event was primarily driven by horizontal wind shear in the frontal zone and vertical motion triggered by mid-tropospheric waves. Full article

As industrial parks face increasing pressure to balance economic development with environmental sustainability, optimizing emission strategies becomes critical for achieving sustainable development goals. In this study, a pollutant dispersion module is coupled with the WRF-FDDA-LES (Weather Research and Forecasting four-dimensional data assimilation and large-eddy simulation) to establish a multiscale air quality model for the Pengzhou Industrial Park, Sichuan, China, hereafter referred to as PZ-LESTD. Using PZ-LESTD, the study conducts refined large-eddy simulations of pollutant dispersion from elevated sources in the industrial park on 23 August 2022. The capability of the model in simulating large-scale weather conditions and pollutant transport, together with its performance in refined-grid LES of elevated emission dispersion, is evaluated. Sensitivity experiments with different pollutant emission heights are also carried out. The results demonstrate that the model can satisfactorily reproduce large-scale meteorological variables and pollutant distributions over China and achieve high accuracy in the refined LES simulations. Analysis of the simulated dispersion processes of elevated sources indicates that the current elevated emission strategy in the Pengzhou Industrial Park is effective in mitigating the impact of industrial exhaust on surface air quality in the park and surrounding areas. Sensitivity tests of emission heights reveal that source heights of 20 m to 50 m can significantly reduce impacts on nearby ambient air quality, whereas increasing the source height from 50 m to 160 m results in only minor differences in surface-level pollution, although higher emission sources lead to greater horizontal transport of pollutants. This study provides scientific evidence for sustainable industrial planning and emission management strategies, supporting the transition towards environmentally sustainable industrial parks. The findings contribute to evidence-based policymaking for air pollution prevention and control, facilitating the achievement of sustainable development goals through optimized industrial emission layouts and green industrial transformation. Full article

The Eastern Mediterranean, notably Cyprus, is a climate change hotspot facing severe heatwaves. Accurate numerical weather prediction of these extremes requires precise land–atmosphere modeling and initial and boundary conditions. This study assesses replacing the default USGS Land-Use and Land-Cover (LULC) dataset with the 10 m ESA WorldCover 2021 dataset in the Weather Research and Forecasting (WRF) model to simulate the 15–29 July 2023 Cyprus heatwave. The updated LULC increased urban representation six-fold. Statistical validations showed significant improvements in 2 m temperature, relative humidity, and 10 m wind speed predictions across 85% of observational sites. Dynamically, it restored urban thermal memory, effectively capturing the daytime Urban Cool Island effect and nocturnal heat release. Furthermore, radiosonde validations showed that the update corrected nocturnal Planetary Boundary Layer Height (PBLH) underestimations and dampened exaggerated daytime convective mixing. However, crucial limitations remain. High-frequency diagnostics indicated the model still suffers from damped thermal inertia, missing the abrupt temperature spikes and rapid nocturnal cooling typical of semi-arid microclimates. Additionally, the updated configuration failed to capture severe atmospheric stagnation during peak heatwave conditions, highlighting that deep-rooted kinetic errors persist within default boundary layer parameterizations despite static surface improvements. Full article

Synergistic governance of air pollution and carbon is crucial for green transition against the backdrop of global climate change. This study explores the spatial synergistic characteristics and driving mechanisms between air pollutants and CO₂ across China in 2021 from both emission and concentration perspectives, filling the gap of single-perspective analysis. We used the Weather Research and Forecasting coupled with the Vegetation Photosynthesis and Respiration Model (WRF-VPRM) to simulate CO₂ concentrations, integrating the China High Air Pollutants (CHAPs) air pollution data, anthropogenic emission inventories, the coupling and coordination degree (CCD) model, and Geodetector analysis. Results show significant regional and seasonal differences in carbon–pollutant coordination. High-emission and high-coordination zones are concentrated in North China, southern Northeast China, and eastern coastal areas, with CO, NO₂, and O₃ exhibiting stronger coordination with CO₂ than PM₁₀, PM_2.5 and SO₂. Emission synergy is mainly driven by population and GDP with strong GDP-related two-factor enhancement, while concentration synergy is mainly driven by air temperature and temperature–NDVI coupling. These findings highlight the joint effects of socioeconomic, meteorological, and ecological factors, supporting targeted pollution reduction and carbon mitigation strategies and providing a scientific basis for China’s dual carbon strategy and sustainable development. Full article

The Artificial Intelligence Forecasting System (AIFS), recently released by the European Centre for Medium-Range Weather Forecasts (ECMWF), represents a major shift in global weather prediction by replacing traditional physically based approaches with machine-learning methods. This study evaluates the impact of using AIFS as initial and lateral boundary conditions for the Weather Research and Forecasting (WRF) model, in contrast to the well-established physically based GFS. The aim of this work is to analyze the sensitivity of these different modelling configurations during three high-impact storms that affected Spain in 2025 and the effects of replacing GFS for AIFS as lateral and boundary conditions for WRF over the accuracy of operational forecasts. The analysis focuses on maximum wind gusts, accumulated precipitation, and the generation of meteorological warnings. Results show that AIFS substantially underestimates wind gusts with mean bias values between −13 and −25 km/h, and its forecasts differ markedly from those of GFS. When coupled with WRF, however, both AIFS-WRF and GFS-WRF produce similar results, with a general tendency to overestimate gusts, with mean bias values between 4 and 15 km/h. In all cases, WRF adds value, improving the representation of wind-related variables compared with the raw global model outputs. For accumulated precipitation, both WRF configurations reproduce the main rainfall patterns associated with the storms. AIFS-WRF shows a stronger tendency to overestimate precipitation, with RMSE values of 64, 23, and 12 mm for the different high-impact storms considered, although it also achieves the highest correlations. Finally, the analysis of meteorological warnings indicates that AIFS alone generates almost no wind gusts alerts. Once coupled with WRF, both configurations generate warnings in the regions where the most severe conditions occurred. Overall, while the added value of mesoscale models such as WRF is well established and confirmed here, the AI-based AIFS does not show clear advantages in comparison with traditional global models for these high-impact events being analyzed. Full article