Search Results (332)

This study investigates high-resolution numerical weather modelling and large eddy simulations (LESs) for wind resource assessment in complex coastal mountainous terrain. The main purpose is to investigate strong-wind events, where earlier research indicates that high wind speeds are underestimated. Using the Weather Research and Forecasting model (WRF), simulations were conducted for the Fakken wind power plant in northern Norway, a region characterised by steep mountains, fjords, and challenging wind patterns. The study evaluates the impact of increasing model resolution, from mesoscale to LESs, on wind speed and power production estimates. Results show that higher-resolution models improve the representation of terrain features, leading to better estimations of wind speed and direction, particularly during strong-wind events such as the Ylva storm in 2017. The LES model demonstrated the ability to capture high-wind events, including localised speed-ups and lee-side amplification, which is critical for accurate wind speed modelling. Comparison with power production data shows the potential of WRF LESs to optimise wind farm operations in complex terrains. Full article

Accurate wind resource assessment is essential for offshore wind energy development, particularly in nearshore sites where atmospheric stability and internal boundary layers significantly influence the horizontal wind distribution. In this study, we investigated wind estimation methods using a high-resolution, 100 m grid Weather Research and Forecasting (WRF) model and coastal onshore wind measurement data. Five estimation methods were evaluated, including a control WRF simulation without on-site measurement data (CTRL), observation nudging (NDG), two offline methods—temporal correction (TC) and the directional extrapolation method (DE)—and direct application of onshore measurement data (DA). Wind speed and direction data from four nearshore sites in Japan were used for validation. The results indicated that TC provided the most accurate wind speed estimate results with minimal bias and relatively high reproducibility of temporal variations. NDG exhibited a smaller standard deviation of bias and a slightly higher correlation with the measured time series than CTRL. DE could not reproduce temporal variations in the horizontal wind speed differences between points. These findings suggest that TC is the most effective method for assessing nearshore wind resources and is thus recommended for practical use. Full article

This project aimed to evaluate the use of meteorological satellite-derived rainfall data to estimate water inflows to dams. In this study, the Lam Pao Dam in the Chi Basin, Thailand, was used as a case study. Rainfall data were obtained using the PERSIANN technique. To improve accuracy, satellite-derived rainfall estimates were adjusted using ground-based rainfall measurements from stations located near and within the catchment area, applying the 1-DVAR method. The Kriging method was employed to estimate the spatial distribution of rainfall over the catchment area. This approach resulted in a Probability of Detection (POD) of 0.92 and a Threat Score (TS) of 0.72 for rainfall estimates in the Chi Basin. Rainfall data from the Weather Research and Forecasting (WRF) numerical models were used as inputs for the HEC-HMS model to simulate water inflows into the dam. To refine rainfall estimates, various microphysics schemes were tested, including WSM3, WSM5, WSM6, Thompson, and Thompson Aerosol-Aware. Among these, the Thomson Aerosol-Aware scheme demonstrated the highest accuracy, achieving an average POD of 0.96, indicating highly reliable rainfall predictions for the Lam Pao Dam catchment. The findings underscore the potential benefits of using satellite-derived meteorological data for rainfall estimation, particularly where installing and maintaining ground-based measurement stations is difficult, e.g., forests/mountainous areas. This research contributes to a better understanding of satellite-derived rainfall patterns and their influence on catchment hydrology for enhanced water resource analysis. Full article

Paleoclimate records have long documented the existence of multicentury hydroclimate anomalies in the Altiplano of South America. However, the causes and mechanisms of these extended events are still unknown. Here, we present a climate modeling experiment that explores the oceanic drivers and atmospheric mechanisms conducive to long-term precipitation variability in the southern Altiplano (18–25° S; 70–65 W; >3500 masl). We performed a series of 100-year-long idealized simulations using the Weather Research and Forecasting (WRF) model, configured to repeat annually the oceanic and atmospheric forcing leading to the exceptionally humid austral summers of 1983/1984 and 2011/2012. The aim of these cyclical experiments was to evaluate if these specific conditions can sustain a century-long pluvial event in the Altiplano. Unlike the annual forcing, long-term negative precipitation trends are observed in the simulations, suggesting that the drivers of 1983/1984 and 2011/2012 wet summers are unable to generate a century-scale pluvial event. Our results show that an intensification of the anticyclonic circulation along with cold surface air anomalies in the southwestern Atlantic progressively reinforce the lower and upper troposphere features that prevent moisture transport towards the Altiplano. Prolonged drying is also observed under persistent La Niña conditions, which contradicts the well-known relationship between precipitation and ENSO at interannual timescales. Contrasting the hydroclimate responses between the Altiplano and the tropical Andes result from a sustained northward migration of the Atlantic trade winds, providing a useful analog for explaining the divergences in the Holocene records. This experiment suggests that the drivers of century-scale hydroclimate events in the Altiplano were more diverse than previously thought and shows how climate modeling can be used to test paleoclimate hypotheses, emphasizing the necessity of combining proxy data and numerical models to improve our understanding of past climates. 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

Between 14 and 16 June 2023, an extratropical cyclone affected the south-southeastern coast of Brazil, causing significant damage and loss of life. In the state of Rio Grande do Sul, Civil Defense authorities reported at least 16 fatalities. Although numerical models can simulate the general characteristics of extratropical cyclones, they often struggle to accurately represent the intensity and timing of strong winds and heavy precipitation. One approach to improving such simulations is the use of convective-permitting models (CPMs), in which convection is explicitly resolved. In this context, the main objective of this study is to assess the performance of the Weather Research and Forecasting (WRF) model in CP mode, nested in the ERA5 reanalysis, in representing both the synoptic and mesoscale structures of the cyclone, as well as its associated strong winds and precipitation. The WRF-CP successfully simulated the cyclone’s track, though with some discrepancies in the cyclone location during the first 12 h. Comparisons with radar-based precipitation estimates indicated that the WRF-CP captured the location of the observed precipitation bands. During the cyclone’s occlusion phase—when precipitation was particularly intense—hourly simulated precipitation and 10 m wind (speed, zonal, and meridional components) were evaluated against observations from meteorological stations. WRF-CP demonstrated strong skill in simulating both the timing and intensity of precipitation, with correlation coefficients exceeding 0.4 and biases below 0.5 mm h⁻¹. Some limitations were observed in the simulation of 10 m wind speed, which tended to be overestimated. However, the model performed well in simulating the wind components, particularly the zonal component, as indicated by predominantly high correlation values (most above 0.4), suggesting a good representation of wind direction, which is a function of the zonal and meridional components. Overall, the simulation highlights the potential of WRF-CP for studying extreme weather events, including the small-scale structures embedded within synoptic-scale cyclones responsible for producing adverse weather. 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

The objective of this article is to conduct a review to analyze global trends in the use of air pollution models under the influence of climate variability (CV) over urban areas. Five scientific databases were used (2013–2024): Scopus, ScienceDirect, SpringerLink, Web of Science, and Google Scholar. The frequency of citations of the variables of interest in the selected scientific databases was analyzed by means of an index using quartiles (Q). The results showed a hierarchy in the use of models: regional climate models/RCMs (Q3) > statistical models/SMs (Q3) > chemical transport models/CTMs (Q4) > machine learning models/MLMs (Q4) > atmospheric dispersion models/ADMs (Q4). RCMs, such as WRF, were essential for generating high-resolution projections of air pollution, crucial for local impact assessments. SMs, such as GAM, excelled in modeling nonlinear relationships between air pollutants and climate variables. CTMs, such as WRF-Chem, simulated detailed atmospheric chemical processes vital for understanding pollutant formation and transport. MLMs, such as ANNs, improved the accuracy of predictions and uncovered complex patterns. ADMs, such as HYSPLIT, evaluated air pollutant dispersion, informing regulatory strategies. The most studied pollutants globally were O₃ (Q3) > PM (Q3) > VOCs (Q4) > NOx (Q4) > SO₂ (Q4), with models adapting to their specific characteristics. Temperature emerged as the dominant climate variable, followed by wind, precipitation, humidity, and solar radiation. There was a clear differentiation in the selection of models and variables between high- and low-income countries. CTMs predominated in high-income countries, driven by their ability to simulate complex physicochemical processes, while SMs were preferred in low-income countries, due to their simplicity and lower resource requirements. Temperature was the main climate variable, and precipitation stood out in low-income countries for its impact on PM removal. VOCs were the most studied pollutant in high-income countries, and NOx in low-income countries, reflecting priorities and technical capabilities. The coupling between regional atmospheric models and city-scale air quality models was vital; future efforts should emphasize intra-urban models for finer urban pollution resolution. This study highlights how national resources and priorities influence air pollution research over cities under the influence of CV. Full article

This study investigates how to use the gridded satellite datasets of observational precipitation to improve the performance of the climatological simulation by using the method of non-parametric quantile mapping (QM). The precipitation in Southeast Asia is simulated in 2001–2005 using the climate model of Weather Research and Forecasting (WRF). Two satellite datasets of observational precipitation, GSMaP and CHIRPS, are used for model training, simulation evaluation, and cross-validation. The evaluations of simulation and bias correction suggest that QM is able to perfectly correct the overall quantile distributions of the simulated precipitation, which is characterized by overestimation at most quantiles, especially for light and extreme precipitation. After the QM correction based on GSMaP (CHIRPS), the relative bias of the monthly average for all months is reduced from 39.3% to 4.1% (from 57.2% to 4.2%). The biases of spatial patterns are largely narrowed from 43.5% (59.4%) to 4.0% (2.5%) for annual-mean precipitation and from 43.5% (59.4%) to 4.0% (2.5%) for extreme precipitation. The results indicate that the QM correction based on the gridded satellite datasets outperforms the raw model output and greatly improves the estimates of the simulated precipitation. Full article

Recent trends of intense forest fires in the Korean Peninsula have increased concerns about more extreme burning in the future under a warming climate. Accurate and reliable fire weather information has become more critical to reduce the risk of forest-related disasters over complex terrain. In this study, two parameterizations reflecting complex topographic effects were implemented in the Weather Research and Forecasting (WRF) model. The model performance was evaluated over the mountainous region in Gangwon-do, South Korea’s most significant forest area. The simulation results of the wildfire case in 2019 show that subgrid-scale orographic parameterization considerably improves model performance regarding wind speed, with a lower root mean square error (RMSE) and bias by 53% and 57%, respectively. Another parameterization, reflecting slope and shading, effectively reflected sunrise and sunset effects. The second parametrization produced little effect on the daily averages of meteorological elements. However, thermodynamic components such as temperature and heat flux show more realistic values during sunset or sunrise when the solar altitude angle is low. The results imply that applying topographic parameterizations is required in numerical simulations, especially for hazardous weather conditions over complex terrain in mountainous regions. 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

Due to the “One City, One Policy” for air pollution prevention and control measures, Tai’an City was the only city in Shandong Province with a year-on-year decrease in O₃ concentrations in 2022. In this study, the WRF-CMAQ model was used to simulate the O₃ concentrations in Tai’an and other inland cities in Shandong Province in September 2022, and the model evaluation method was applied to discover the differences in the O₃ concentrations between Tai’an and other cities. During the periods of high maximum daily 8 h average O₃ (MDA8 O₃), the model only overestimated the O₃ concentrations in Tai’an by 3.4% and underestimated those in other inland cities by −11.0% to −2.2%. Dozens of O₃ simulation scenarios were designed on the basis of the control of O₃ precursor emissions, and the results indicate that the O₃ precursor emissions in Tai’an were at a lower level. On this basis, the impacts of meteorological conditions and O₃ precursor emission changes on O₃ concentrations in Tai’an were quantified. Adverse meteorological conditions and changes in emissions from other inland cities led to a 49.5 µg/m³ increase in the mean MDA8 O₃ in Tai’an during the study period. However, the local emission reduction measures in Tai’an, to some extent, offset these adverse effects, reducing the mean MDA8 O₃ by 5.8 µg/m³. In summary, the Tai’an City might implement effective emission reduction measures during periods of high MDA8 O₃, thereby achieving a reduction in overall O₃ concentrations. This effort secured its leading position in Shandong Province’s O_3–8h-90per ranking in 2022. Full article

Having good replication of the soil moisture evolution is desirable to properly simulate the dust emissions and atmospheric dust load because soil moisture increases the cohesive forces of soil particles, modulating the wind erosion threshold above which emissions occur. To reduce errors, one can use soil moisture retrievals from space-borne microwave radiometers. Here, we explore the potential of inserting soil moisture retrievals from the Soil Moisture Active Passive (SMAP) satellite into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to improve dust simulations. We focus our analysis on the contiguous U.S. due to the presence of important dust sources and good observational networks. Our analysis extends over the first year of SMAP retrievals (1 April 2015–31 March 2016) to cover the annual soil moisture variability and go beyond extreme events, such as dust storms, in order to provide a statistically robust characterization of the potential added value of the soil moisture retrievals. We focus on the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model from the Air Force Weather Agency (GOCART-AFWA) dust emission parameterization that represents soil moisture modulations of the wind erosion threshold with a parameterization developed by fitting observations. The dust emissions are overestimated by the GOCART-AFWA parameterization and result in an overestimation of the aerosol optical depth (AOD). Sensitivity experiments show that emissions reduced to 25% in the GOCART-AFWA simulations largely reduced the AOD bias over the Southwest and lead to better agreement with the standard WRF-Chem parameterization of dust emissions (GOCART) and with observations. Comparisons of GOCART-AFWA simulations with emissions reduced to 25% with and without SMAP soil moisture insertion show added value of the retrievals, albeit small, over the dust sources. These results highlight the importance of accurate dust emission parameterizations when evaluating the impact of remotely sensed soil moisture data on numerical weather prediction models. Full article

This study applied the fully coupled model WRF/WRF-Hydro to simulate land, air, and water cycles in the Changbai Mountain area (CMA) in Northeast China. This study evaluated the applicability of the coupled model in the region and analyzed the impact of regional climate change on the water cycle in the study area over the past half-century. The temperature in the Changbai Mountains increased significantly from 1975 to 2020. Precipitation, canopy water, and all types of evapotranspiration showed different increasing trends, whereas surface runoff showed a decreasing trend. The comparison revealed that precipitation, canopy water, canopy evaporation, and total evapotranspiration increased gradually in the low-latitude subbasins, whereas runoff decreased more rapidly. Runoff in the study area showed an annual double peak, which was due to snowmelt in spring and abundant precipitation in summer. Under the influence of climate change, the thawing time of frozen soil and snow cover in the study area will advance, leading to an increase in the spring runoff peak and earlier occurrence time. Our results provide a reference for the study of the water cycle process of the coupled model in cold mountainous areas and a scientific reference for the scientific response to climate change and the protection of regional water resource security. Full article

Air quality simulations were performed for Athens (Greece) in ~1 km resolution applying the models WRF-CAMx for July and December 2019 with the secondary organic aerosol processor (SOAP) and volatility basis set (VBS) organic aerosol (OA) schemes. CAMx results were evaluated against particulate matter (PM) and OA concentrations from the regulatory monitoring network and research monitoring sites (including PM_2.5 low-cost sensors). The repartition of primary OA (POA) and secondary OA (SOA) by CAMx was compared with positive matrix factorization (PMF)-resolved OA components based on aerosol chemical speciation monitor (ACSM) measurements. In July, OA concentrations underestimation was decreased by up to 24% with VBS. In December, VBS introduced small negative biases or resulted in more pronounced (but moderate) underestimations of OA with respect to SOAP. CAMx performance for POA was much better than for SOA, while VBS decreased the overestimation of POA and the underestimation of SOA in both study periods. Despite the SOA concentrations increases by VBS, CAMx still considerably underestimated SOA (e.g., by 65% in July). Better representation of simulated OA concentrations in Athens could benefit by accounting for the missing cooking emissions, by improvements in the biomass burning emissions, or by detailed integration of processes related to OA chemical aging. Full article