Weather Research And Forecasting Simulations Research Articles

High‐Resolution Climate Simulations Over the Eastern Mediterranean Black Sea Region Using the Pseudo‐Global Warming Method With a CMIP6 Ensemble

AbstractThe strong drying expected in the Eastern Mediterranean with climate change could cause mass migration of people already living under water shortages. On the other hand, precipitation is expected to increase toward the region’s north, particularly those along the interior of the eastern Black Sea coasts, which could worsen existing floods. However, this double‐sided adverse phenomenon and its underlying reasons have been investigated by relatively low‐resolution models in the Eastern Mediterranean Black Sea (EMBS) region, where orographic precipitation prevails. This study performs 4 km resolution Weather Research and Forecasting (WRF) model simulations with and without climate change signals retrieved from a CMIP6 GCM ensemble via pseudo‐global‐warming (PGW). The WRF simulations captured the large‐scale dynamics affecting the EMBS fairly well: an anticyclonic low‐level circulation and enhancing subsidence stem from anomalous ridge development over the central Mediterranean in winter (DJF), and a cyclonic low‐level circulation and weakening subsidence rise from heat‐low development over the Eastern Mediterranean in summer (JJA). The resulting picture of future warming and drying over the area generally supports the literature, although new insights emerge in anomalous precipitation increase, especially in the summer season over the Greater Caucasus and nearby regions. Most likely, the warmer‐than‐expected Caspian Sea induces a large increase in specific humidity and, thus, a large moisture source in the lower troposphere and an extension of the heat‐low effect in the mid‐troposphere. In addition, the high‐resolution WRF simulations provide added value over the complex topography of the Caucasian Mountain range for new insights into this region.

Sensitivity of WRF-Simulated 2 m Temperature and Precipitation to Physics Options over the Loess Plateau

The current paper evaluates the weather research and forecasting (WRF) model sensitivity to five different combinations of cumulus, microphysics, radiation, and planetary boundary layer (PBL) schemes over Loess Plateau for the period 2015, in terms of 2 m temperature and precipitation. The WRF configuration consists of a 10 km resolution domain nested in a coarser domain driven by European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data. The model simulated 2 m temperature and precipitation have been evaluated at daily and monthly scales with gridded observational dataset. The analysis shows that all experiments reproduce well the daily 2 m temperature, with overestimation particularly in the low-temperature range. Precipitation is less well simulated, with underestimation in all range, especially for intense rainfall. Comparing with ERA-Interim, WRF shows no clear benefit in simulating daily 2 m temperature while prominent improvement in simulating daily precipitation. WRF simulations capture the annual cycle of monthly 2 m temperature and precipitation with a warm bias and wet bias for most experiments in summer. Some reasonable configurations are identified. The “best” configuration depends on the criteria.

Outlook for Offshore Wind Energy Development in Mexico from WRF Simulations and CMIP6 Projections

This study presents a comprehensive assessment of the offshore wind energy potential in Mexico across 40 years (1979–2018) of numerical simulations using the Weather Research and Forecasting (WRF) model and data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The WRF identifies three regions with moderate to good wind potential: off the north coast of Tamaulipas (Zone I), the northwest coast of Yucatan (Zone II), and the Gulf of Tehuantepec (Zone III). The analysis involves comparing 47 CMIP6 climate models with the WRF results and selecting the best performing models to obtain future projections for the short term (2040–2069) and the long term (2070–2099). Two ensemble-based strategies were implemented. The first one, which uses an intersection approach from which four CMIP6 models were considered, reveals positive percentage differences in Zone II for both future projections, especially for the long-term one. In Zones I and III, positive values are also observed near the coast, mainly for the long-term projection, but they are considerably lower compared to those in Zone II. The second ensemble strategy uses weight assignment through the Mean Absolute Percentage Error, so that a greater weight is given to the model that performed better in each particular zone, potentially providing more accurate results. The findings suggest the likelihood of increased offshore wind energy in these three zones of Mexico, for both short- and long-term future projections, with positive percentage differences of up to 10% in certain areas.

An Assessment of the Coupled Weather Research and Forecasting Hydrological Model on Streamflow Simulations over the Source Region of the Yellow River

The Source Region of the Yellow River (SRYR), renowned as the “Water Tower of the Yellow River”, serves as an important water conservation domain in the upper reaches of the Yellow River, significantly influencing water resources within the basin. Based on the Weather Research and Forecasting (WRF) Model Hydrological modeling system (WRF-Hydro), the key variables of the atmosphere–land–hydrology coupling processes over the SRYR during the 2013 rainy season are analyzed. The investigation involves a comparative analysis between the coupled WRF-Hydro and the standalone WRF simulations, focusing on the hydrological response to the atmosphere. The results reveal the WRF-Hydro model’s proficiency in depicting streamflow variations over the SRYR, yielding Nash Efficiency Coefficient (NSE) values of 0.44 and 0.61 during the calibration and validation periods, respectively. Compared to the standalone WRF simulations, the coupled WRF-Hydro model demonstrates enhanced performance in soil heat flux simulations, reducing the Root Mean Square Error (RMSE) of surface soil temperature by 0.96 K and of soil moisture by 0.01 m3/m3. Furthermore, the coupled model adeptly captures the streamflow variation characteristics with an NSE of 0.33. This underscores the significant potential of the coupled WRF-Hydro model for describing atmosphere–land–hydrology coupling processes in regions characterized by cold climates and intricate topography.

A study of evaporation duct characteristics in the South China Sea during the Winter of 2017

The evaporation duct (ED) near the sea surface is the most frequent type of duct that traps and redirects electromagnetic waves over longer or shorter distances. This study uses radiosonde data from the South China Sea Two-Island Monsoon Experiment (SCSTIMX) in 2017 and the Weather Research and Forecasting (WRF) model to study the ED and ED height (EDH). We enhanced the vertical resolution to 26 layers in the lowest 134 m. The simulations indicate that EDs were prevalent throughout the South China Sea (SCS) area in winter 2017, where the prevailing surface winds are strong northeasterly. The EDH exhibits diurnal variations and significant variability across the SCS. The WRF simulations of ED and EDH generally agree with observations of the trajectory of Taiwan-O.R.1. Our results indicate that the WRF model can simulate the pattern of ED and EDH over SCS. Results reveal that Yonsei University (YSU) boundary layer (BL) parameterization more accurately simulates the vertical temperature and humidity variations in the lower atmosphere, with stronger winds and higher EDH in most areas. The YSU and the Medium Range Forecast Model (MRF) BL schemes show similar ED patterns to the Taiwan-O.R.1 SCS radiosonde observations but consistently underestimate the EDH. While strong surface winds in the winter SCS may contribute to a higher EDH, the strong surface wind-induced vertical mixing also modifies sea surface air temperature, relative humidity, and vapor pressure, affecting the vertical refractivity. Our results indicate that sea surface air temperature is a more reliable factor affecting EDH in the simulations.

The WRF Simulation Influence of Assimilating GNSS Water Vapor and Parameterization Schemes on Typhoon Rumbia

The Weather Research and Forecasting (WRF) model was used to simulate Typhoon Rumbia in this paper. The sensitivity experiments were conducted with 16 different parameterization combination schemes, including four microphysics (WSM6, WSM5, Lin, and Thompson), two boundary layers (YSU and MYJ), and two cumulus convection (Kain–Fritsch and Grell–Freitas) schemes. The impacts of 16 parameterization combination schemes and the data assimilation (DA) of Global Navigation Satellite System (GNSS) water vapor were evaluated by the simulation accuracy of typhoon track and intensity. The results show that the typhoon track and intensity are significantly influenced by parameterization schemes of cumulus and boundary layers rather than microphysics. The averaged track error of Lin_KF_Y is 104.73 km in the entire 72-h simulation period. The track errors of all the other combination schemes are higher than Lin_KF_Y. During the entire 72-h, the averaged intensity error of Thompson_GF_M is 1.36 hPa. It is the lowest among all the combination schemes. As for data assimilation, the simulation accuracy of typhoon tracks can be significantly improved by adding the GNSS water vapor. Thompson_GF_M-DA combination scheme has the lowest average track error of 45.05 km in the initial 24 h. The Lin_KF_Y-DA combination scheme exhibits an average track error of 32.17 km on the second day, 28.03 km on the third day, and 35.33 km during 72-h. The study shows that the combination of parameterization schemes and the GNSS water vapor data assimilation significantly improve the initial conditions and the accuracy of typhoon predictions. The study results contribute to the selection of appropriate combinations of physical parameterization schemes for the WRF-ARW model in the mid-latitude region of the western Pacific coast.

Dynamics of diabatically forced anticyclonic plumes in the stratosphere

AbstractA new class of vortices has been observed in the stratosphere following extreme wildfires (Canada 2017, Australia 2020) and volcanic eruptions (Raikoke 2019). These vortices are long‐lived mesoscale anticyclones (hundreds to 1,000 km in diameter) trapping plumes of aerosols and combustion/volcanic compounds. Owing to their unusual composition, these anticyclonically trapped plumes (ATPs) are associated with a significant radiative heating, which fuels their ascent through the stratosphere. This article investigates the dynamics of ATPs using two complementary approaches: analytically, in a potential vorticity (PV) perspective, and through idealised numerical simulations with the Weather Research and Forecasting (WRF) model. In both cases, we consider the vortical flow forced by a heating Lagrangian tracer. By reformulating the problem in the potential radius–potential temperature coordinate system introduced for tropical cyclones, we first clarify that ATP formation is concomitant with the injection of air into the stratosphere at extratropical latitudes. Then, we derive a set of simplified one‐dimensional equations describing the subsequent evolution of the flow after the injection. The equation obtained for the tracer is a variant of the classical Burgers' equation. In qualitative agreement with the three‐dimensional WRF simulations, this theoretical model predicts that ATPs develop an upper tracer front associated with sustained near‐zero anticyclonic PV, followed by a smooth tracer tail of cyclonic PV. Radiative relaxation of the temperature perturbations induced by the anticyclone and the presence of an initial PV anomaly tend to stabilise ATPs during their ascent. Finally, we note that the theory predicts a similar relationship between the plume and anticyclonic PV for cooled ATPs, which is supported by three‐dimensional simulations and may apply to the 2022 Hunga volcanic plume.

Quantifying the impact of urbanization on wet deposition over Xiong'an New Area in northern China

AbstractXiong'an New Area (XNA) in China is undergoing rapid urbanization due to economic development and population growth. However, the urbanization process can significantly impact the local environment. This study applied a combined method of Weather Research and Forecasting (WRF) and empirical calculations to quantitatively identify changes in precipitation and wet deposition over XNA and its internal lake, Baiyangdian (BYD), during the summer months of June to September. Five different urbanization scenarios were established by varying the WRF subsurface conditions. By comparing these scenarios with historical observations, it was found that WRF simulations could effectively reproduce the precipitation and temperature changes in the region. This study examined how different levels of urbanization affect precipitation and estimated changes in PM2.5 wet deposition. The study's results indicate that as urbanization increases, it leads to an increase in precipitation and wet deposition in the XNA region. Using the relatively low urbanization level in 1980 as a benchmark for comparison, it was observed that when XNA becomes fully urbanized, the increase in precipitation and wet deposition in the region exceeds 7%. The BYD Lake can have a moderating effect on the environment, absorbing more than 50% of the local precipitation and PM2.5 wet deposition with less than 25% of its area, compared to the XNA interval of the statistics, while the increase in wet deposition in the BYD Lake area is more than 4% after full urbanization in XNA. The findings also emphasize the importance of the need to balance economic growth and environmental protection in urban planning and development.

Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models

In recent years, the energy crisis has become increasingly severe, and global attention has shifted towards the development and utilization of wind energy. The establishment of wind farms is gradually expanding to encompass forested regions. This paper aims to create a Weather Research and Forecasting (WRF) model suitable for simulating wind fields in forested terrains, combined with a long short-term time (LSTM) neural network enhanced with attention mechanisms. The simulation focuses on capturing wind characteristics at various heights, short-term wind speed prediction, and wind energy assessment in forested areas. The low-altitude observational data are obtained from the flux tower within the study area, while high-altitude data are collected using mobile radar. The research findings indicate that the WRF simulations using the YSU boundary layer scheme and MM5 surface layer scheme are applicable to forested terrains. The LSTM model with attention mechanisms exhibits low prediction errors for short-term wind speeds at different heights. Furthermore, based on the WRF simulation results, a wind energy assessment is conducted for the study area, demonstrating abundant wind energy resources at the 150 m height in forested regions. This provides valuable support for the site selection in wind farm development.

Evaluating the performance of WRF in simulating winds and surface meteorology during a Southern California wildfire event

The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CA and intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event.

Impact of a gravity wave process on the upper stratospheric ozone valley on the Qinghai-Tibetan Plateau

Atmospheric gravity waves are essential meso-small-scale oscillations that facilitate material exchange within the atmosphere. These waves can significantly affect the ozone layer in the Upper Troposphere and Lower Stratosphere (UTLS) on the Qinghai-Tibetan Plateau. However, the influence of gravity wave dynamic processes on upper stratospheric ozone has rarely been studied. This paper identifies the gravity waves on the Tibetan Plateau based on ECMWF Reanalysis 5 (ERA5) data, analyzes the response of the upper stratospheric ozone to the event, and simulates the dynamic propagation mechanism of gravity waves by the Weather Research and Forecasting (WRF) model. This analysis reveals that between 15:00 and 21:00 UTC on July 29, 2015, gravity waves propagated from the surface of Tibetan Plateau (450 hPa) up to the upper stratosphere (20 − 3 hPa) in an arc-shaped structure and tilted to the east with height. The gravity wave signals started to weaken at 21:00 UTC on the same day. Influenced by the easterly rapids, gravity waves partially broke near 3 hPa at 02:30 UTC on July 30, but gravity wave signals were still present, and gravity waves completely broke and released energy at 04:00 UTC. During this process, ozone in the 20–3 hPa region (upper stratosphere) on the Qinghai-Tibetan Plateau responds well to gravity waves, and the ozone mixing ratio began to drop in ozone concentration 30 min after the partial breakup (03:00 UTC). The ozone dropped drastically by about 0.014 ppmv from 04:00 to 05:00 UTC. The WRF simulation results agree well with ERA5 and accurately capture the intricate characteristics of gravity waves. Furthermore, the breakup of gravity waves caused a total drop in ozone of 0.024 ppmv.

A Machine Learning Approach to Model Over-Ocean Tropical Cyclone Precipitation

Abstract Extreme rainfall found in tropical cyclones (TCs) is a risk for human life and property in many low- to midlatitude regions. Probabilistic modeling of TC rainfall in risk assessment and forecasting can be computationally expensive, and existing models are largely unable to model key rainfall asymmetries such as rainbands and extratropical transition. Here, a machine learning–based framework is developed to model overwater TC rainfall for the North Atlantic basin. First, a catalog of high-resolution TC precipitation simulations for 26 historical events is assembled for the North Atlantic basin using the Weather Research and Forecasting (WRF) Model. The simulated spatial distribution of rainfall for these historical events are then decomposed via principal component analysis (PCA), and quantile regression forest (QRF) models are trained to predict the conditional distributions of the first five principal component (PC) weights. Conditional distributions of rain-rate levels are estimated separately using historical satellite data and a QRF model. With these models, probabilistic predictions of rainfall maps can be made given a set of storm characteristics and local environmental conditions. The model is able to capture storm total rainfall compared to satellite observations with a correlation coefficient of 0.96 and r2 value of 0.93. Additionally, the model shows good accuracy in modeling hourly total rainfall compared to satellite observations. Rain-rate maps predicted by the model are also compared to historical satellite observations and to the WRF simulations during cross validation, and the spatial distribution of estimates captures rainfall variability consistent with TC rainbands, wavenumber asymmetries, and possibly extratropical transition.

The Importance of Wind Simulations over Dried Lake Beds for Dust Emissions in the Middle East

Dust storms are one of the major environmental hazards affecting the Middle East countries, and largely originate in vast deserts and narrow dried lake beds. This study analyzes the inter-annual variation in dust weather conditions from 2000 to 2020 using data obtained from ten meteorological stations located around dried (completely or partly) lakes in Northwest (Urmia Lake) and South (Bakhtegan Lake) Iran. Since the wind regime is one of the most important factors controlling dust emissions in the dust source areas, wind speed simulations from the Weather Research and Forecasting (WRF) Model for 134,113 grid points covering the Middle East area, with a resolution of 5 km, were analyzed and compared with wind measurements at the stations around Urmia and Bakhtegan Lakes from 2005 to 2015. The analysis shows that the annual number of dust days was highly variable, presenting a significant increase at the stations around Urmia Lake during 2008–2011 and at the stations around Bakhtegan Lake in 2007–2012. Eleven years of WRF simulations of the mean diurnal wind patterns revealed that the highest 10 m wind speed occurred mostly around the local noon (12 to 15 UTC), generally coinciding with the majority of the reported dust codes within this time frame, as a result of the association between wind speed and dust emissions (dust weather conditions) around these lake basins. Consequently, accurate wind simulation has high importance for unbiased numerical prediction and forecasting of dust conditions. The comparison between the measured mean monthly 10 m wind speed and WRF-simulated 10 m wind speed revealed that the model overestimated wind data in all the stations around the Bakhtegan Lake but performed better at reconstructing the wind speeds at stations around Urmia Lake. Furthermore, notable differences were observed between measured and simulated wind directions, thus leading to uncertainties in the simulations of the dust-plume transport.

Assessment of physical schemes for WRF model in convection-permitting mode over southern Iberian Peninsula

Convection-permitting models (CPMs) enable the representation of meteorological variables at horizontal high-resolution spatial scales (≤ 4 km), where convection plays a significant role. In this regard, physical schemes need to be evaluated considering factors in the studied region such as orography and climate variability. This study investigates the sensitivity of the Weather Research and Forecasting (WRF) model as CPM to the use of different physics schemes on Andalusia, a complex orography region in the southern part of the Iberian Peninsula (IP). To do that, a set of 1-year WRF simulations was completed based on two “one-way” nested domains: the parent domain (d01) spanning the entire IP with 5 km spatial resolution and the nested domain (d02) for the region of Andalusia at 1 km of spatial resolution. 12 physic schemes were examined from combinations of microphysics (MP) schemes including THOMPSON, WRF single moment 6-class (WSM6), and WRF single moment 7-class (WSM7), and different options for the convection in d01, the Grell 3D (G3), Grell-Freitas (GF), Kain-Fritsch (KF), and deactivated cumulus parameterization (OFF). The simulated precipitation and 2-m temperature for the year 2018, characterized to be a very wet year, were compared with observational datasets from different sources to determine the optimal WRF configuration, including point-to-point and station-point comparisons at different time aggregations (from annual to hourly). In general, greater differences were shown when comparing the results of convection schemes in d01. Simulations completed with GF or OFF presented better performance compared to the reference datasets. Concerning the MP, although THOMPSON showed a better fit in high mountain areas, it generally presents a worse agreement with the reference datasets. In terms of temperature, the results were very similar and, therefore, the selection of the “best” configuration was based mainly on the precipitation results with the WSM7-GF scheme being suitable for Andalusia region.

Evaluation of three radiation schemes of the WRF-Solar model for global surface solar radiation forecast: A case study in Egypt

This study aims to assess the performance of three different radiation schemes in Weather Research and Forecasting (WRF) mesoscale model against ECMWF fifth generation (ERA5) Reanalysis data over Egypt. Dudhia, rrtmg, and Goddard schemes were used to simulate Global Horizontal Irradiance (SR) during 2017. Sixteen stations were selected to represent the different climate conditions in Egypt. The observed SR data is only available at five stations and used to evaluate the accuracy of ERA5 and WRF output. SR data at the other eleven stations were extracted from ERA5 to evaluate the corresponding values from WRF simulation. Coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), monthly standard deviation (STDV) and mean absolute percentage error (MAPE%) are the implemented evaluation statistical parameters in the study. The results showed that ERA5 and WRF Dudhia scheme have reliable results as compared to the observations at the five stations. Also, the WRF Dudhia scheme simulates the SR at the other eleven stations better than rrtmg, and Goddard schemes as compared to ERA5 dataset. Where, the three WRF radiation schemes overestimate the SR along the eleven stations and underestimate at Alexandria station by −0.15 KWh/m2, which may due to the weather conditions over these sites. But the Dudhia scheme over the eleven stations has the lowest MBE (<0.34 KWh/m2), RMSE (<0.58 KWh/m2), and MAPE (<−2%), with R2 more than 0.985 at most stations.

Knee Point‐Based Multiobjective Optimization for the Numerical Weather Prediction Model in the Greater Beijing Area

AbstractDetermination of the optimal parameter values in numerical weather prediction (NWP) models has a significant impact on predictions. Here, we propose a knee point‐based multiobjective optimization (KMO) method to find an optimal solution of the NWP model parameters. We apply it to optimize the Weather Research and Forecasting (WRF) model's summer precipitation and temperature simulations for the Greater Beijing area. The results showed that it required fewer than 125 samples (i.e., 25 times the number of dimensions of the parameter space) to obtain the WRF model's optimal parameter values. The optimal parameters determined by KMO outperform the default parameters in WRF simulations for summer precipitation and temperature prediction in the Greater Beijing area, across all periods (calibration, validation, and testing). Additionally, clear physical interpretations are provided to explain why the optimal parameters lead to improved precipitation and temperature forecasting. Overall, the proposed method is effective and efficient to improve NWP.

Estimation of Crops Future Irrigation Water Needs in a Mediterranean Plain

Agriculture is a vulnerable sector to climate change due to its sensitivity to weather conditions. Changes in climatic parameters such as temperature and precipitation significantly affect productivity as well as the consumption of natural resources like water to meet irrigation water needs. There has been a large amount of research on regional climate change. However, this study placed specific crops at first place and considered their irrigation water needs that will arise due to evapotranspiration increase. The aim of this study was to estimate the future irrigation water needs of wheat, cotton, and alfalfa in the east part of Thessaly Plain in central Greece, where Lake Karla, a recently restored lake, is located. The Weather Research and Forecasting (WRF) model was applied as a high-resolution regional climate model to simulate temperature and precipitation for two 5-year periods, namely 2046–2050 (future period) and 2006–2010 (reference period). Simulations refer to the RCP8.5 emission scenario (worst-case). A methodology proposed by the Food and Agriculture Organization (FAO) of the United Nations was followed to estimate the reference crop evapotranspiration, the crop evapotranspiration based on each crop factor, which was determined for each crop, the effective rainfall, and finally, the irrigation water needs for each crop, for the two 5-year periods. Based on WRF simulations, temperature was projected to be 1.1 °C higher in the future period compared to the reference period, while precipitation and effective precipitation were projected to decrease by 32% and 45%, respectively. Based on the WRF projections, by 2025, the irrigation water needs of wheat and alfalfa are expected to increase by more than 16% and more than 11%, respectively, while irrigation water needs of cotton are expected to increase by 7%. An extension of wheat’s irrigation period for one month (i.e., December) was also identified. Good practices that could be applied in the frame of precision agriculture principles in order to save irrigation water were suggested. The results of this study could be exploited by water resources and land use managers when planning short and long-term strategies to adapt to climate change impacts.

Wind field simulation using WRF model in complex terrain: A sensitivity study with orthogonal design

Accurately simulating wind speed is of utmost importance for wind power assessments. The objective of this study is to investigate the performance of wind speed simulation in complex terrain using different parameterization schemes from the Weather Research and Forecasting (WRF) model, employing an orthogonal design methodology. Specifically, nine WRF simulations are conducted based on the orthogonal test, considering various configurations of the planetary boundary layer (PBL), microphysics (MP), and land surface (LS) options. The numerical results are then compared to actual wind data obtained from a measuring station at two different heights (50 m and 80 m) during summer and winter periods. Furthermore, range and variance analyses are employed to rank the three schemes and identify the optimal combination. Moreover, we examine the impact of each parameterization scheme on the accuracy of wind speed predictions based on the results obtained from the orthogonal simulations. Additionally, we discuss the influence of using different evaluation indices within the orthogonal test on the outcomes and analyze the WRF simulated results under the optimal scheme combination. Lastly, we conduct an uncertainty analysis of results from the optimal scheme combination. The findings reveal that both the PBL and MP schemes exhibit highly significant effects on the accuracy of wind speed predictions (significance: **), followed by the LS scheme (significance: *). The order of importance for these three options is ranked as follows: PBL > MP > LS, which is independent of the seasons. The optimal configurations vary from summer and winter periods. Specifically, the optimal scheme combination is determined to be PBL-ACM2, MP-Kessler, and LS-Noah MP in summer, while PBL-BouLac, MP-Lin, and LS-Noah MP in winter. The simulating accuracy of the wind speeds is satisfactory under this optimal combination when considering the uncertainty of on-site measurements during these two periods. These results provide valuable insights for selecting appropriate PBL, MP, and LS options (from the WRF model) for wind speed estimates and wind power development in the studied region.

Evaluation of HY-2C and CFOSAT Satellite Retrieval Offshore Wind Energy Using Weather Research and Forecasting (WRF) Simulations

This study simulated the spatial distribution of wind speeds and wind energy density by using the WRF model, and we used the WRF-simulated results to evaluate the sea surface wind speeds retrieved from the HY-2C and CFOSAT satellite-borne microwave scatterometers over the Yellow Sea region. The main conclusions were as follows: (1) The combination of the MRF boundary layer parameterization scheme, the MM5 near-surface parameterization scheme, and the Global Data Assimilation System (GDAS) initial field demonstrated the best performance in simulating the 10 m wind speed in the Yellow Sea region, with a root-mean-square error (RMSE) of 1.57, bias of 1.24 m/s, and mean absolute percentage error (MAPE) of 17%. (2) The MAPE of the HY-2C inversion data was 9%, while the CFOSAT inversion data had an MAPE of 6%. The sea surface wind speeds derived from the HY-2C and CFOSAT satellite scatterometer inversions demonstrated high accuracy and applicability in this region. (3) The wind speed was found to increase with altitude over the Yellow Sea, with higher wind speeds observed in the southern region compared to the northern region. The wind power density increased with altitude, and the wind power density in the southern area of the Yellow Sea was higher than in the northern region. (4) The CFOSAT satellite inversion products were in good agreement with the WRF simulation results under low wind speed conditions. In contrast, the HY-2C satellite inversion products showed better agreement under moderate wind speed conditions. Under high wind speed conditions, both satellite inversion products exhibited minor deviations, but the HY-2C product had an overall overestimation, while the CFOSAT product remained within the range of −1 to 1 m/s. (6) The wind power density increased with the satellite-inverted 10 m wind speed. When the 10 m wind speed was less than 9 m/s, the wind power density exhibited a roughly cubic trend of increase. However, when the 10 m wind speed exceeded 9 m/s, the wind power density no longer increased with the rise in 10 m wind speed. These findings provide valuable insights into wind energy resources in the Yellow Sea region and demonstrate the effectiveness of satellite scatterometer inversions for wind speed estimation. The results have implications for renewable energy planning and management in the area.

The Influence of Mesoscale Atmospheric Convection on Local Infrasound Propagation

AbstractInfrasound—that is, acoustic waves with frequencies below the threshold of human hearing—has historically been used to detect and locate distant explosive events over global ranges (≥1,000 km). Simulations over these ranges have traditionally relied on large‐scale, synoptic meteorological information. However, infrasound propagation over shorter, local ranges (0–100 km) may be affected by smaller, mesoscale meteorological features. To identify the effects of these mesoscale meteorological features on local infrasound propagation, simulations were conducted using the Weather Research and Forecasting (WRF) meteorological model to approximate the meteorological conditions associated with a series of historical, small‐scale explosive test events that occurred at the Big Black Test Site in Bovina, Mississippi. These meteorological conditions were then incorporated into a full‐wave acoustic model to generate meteorology‐informed predictions of infrasound propagation. A series of WRF simulations was conducted with varying degrees of horizontal resolution—1, 3, and 15 km—to investigate the spatial sensitivity of these infrasound predictions. The results illustrate that convective precipitation events demonstrate potentially observable effects on local infrasound propagation due to strong, heterogeneous gradients in temperature and wind associated with the convective events themselves. Therefore, to accurately predict infrasound propagation on local scales, it may be necessary to use convection‐permitting meteorological models with a horizontal resolution ≤4 km at locations and times that support mesoscale convective activity.