Simulated Wind Speed Research Articles

The Influence of Refined Urban Morphological Parameters on Dynamical and Thermal Fields in a Single-Layer Urban Canopy Model

In this study, localised and non-uniform urban morphology (UM) and urban fraction (UF) parameters are implemented in a single-layer urban canopy scheme in the Weather Research and Forecasting (WRF) mesoscale meteorological model. The purpose of this research is to evaluate the effect of the refined parameterisation scheme on the simulation of dynamic and thermal fields in the urban canopy of the Guangzhou metropolitan area. The results showed that, compared with the default urban canopy parameters of the WRF model, using the localised UM parameters resulted in the most significant improvement in the 10 m wind speed simulation. In urban districts, the mean bias between the observed and simulated 10 m wind speed was reduced significantly by 59% from 2.63 m/s to 1.09 m/s during the daytime. For the thermal environment simulation during the daytime, higher UF and UM values resulted in lower surface albedos and generated narrower street canyons compared with the default modelling setting, which caused more heat to be trapped in the urban canopy and ultimately led to an increase in the surface skin temperature (TSK) and a largely increased ground heat flux (GRD). As a result, at night, more heat was transferred from the ground to the surface, producing a higher TSK. The effect of the localised UF on the sensible heat flux (HFX) was closely related to the near-surface temperature gradient. The UM caused the HFX to increase during the daytime, which was related to the near-surface heat exchange coefficient in the lower model layers. As the high-resolution UM significantly altered the urban geometry, the dynamic environment simulation resulted in a large increase in friction velocity and a decrease in wind speed.

Evaluation of the Effect of WRF Physical Parameterizations on Typhoon and Wave Simulation in the Taiwan Strait

Typhoons and typhoon waves can cause disasters in coastal areas around the world. The Taiwan Strait often experiences typhoons, especially in summer. Numerical models have been adopted to predict typhoons and reduce losses. The Weather Research and Forecasting (WRF) model is widely used in typhoon simulations, and the Simulating WAves Nearshore (SWAN) model performs well in wave simulations. However, significant uncertainty remains in terms of choosing suitable WRF physical parameterizations in different situations. To evaluate the effect of WRF physical parameterizations on wind and wave simulations, 27 simulation experiments were designed. Three typhoon events (Goni, Dujuan, and Meranti) with different tracks that influenced the Taiwan Strait were simulated. Three parameters (wind speed, wind direction and significant wave height) were assessed using Taylor diagrams, and it was found that the best simulation experiment changed according to typhoon tracks and physical parameters. In wind speed simulation, the best simulation experiment is 12 for typhoon Dujuan and 19 for typhoon Goni and Meranti. From the perspective of wind direction simulation, experiments 26, 23, and 2 performed best for typhoons Goni, Dujuan, and Meranti. And experiments 19, 1, and 20 had the best performances in significant wave height simulation for typhoons Goni, Dujuan, and Meranti. The WRF-SWAN model using the best simulation experiment reduced the error and exhibited good performance in the wind and wave simulations. Skill scores of three parameters were all over 70 for typhoon Goni and 80 for typhoon Dujuan and Meranti. The applicability of the best simulation experiments was demonstrated in typhoon simulations with similar tracks. The accuracy of the wave simulation depended on wind speed, wind direction, and their interaction. In addition, a scheme’s sensitivity changed with different typhoon tracks. This study provides references for designing physical parameterizations for use with the WRF-SWAN model, which may help to simulate typhoons and typhoon waves in the Taiwan Strait more accurately in the future.

Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets

Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements.

An Efficient Calibration System of Optical Interferometer for Measuring Middle and Upper Atmospheric Wind

Detection of the Doppler shift of airglow radiation in the middle and upper atmosphere is one of the most important methods for remote sensing of the atmospheric wind field. Laboratory and routine field calibration of an optical interferometer for wind measurement is very important. We report a novel calibration system that simulates a frequency shift of airglow emission lines introduced by wind in the middle and upper atmosphere for calibrating passive optical interferometers. The generator avoids the shortcomings of traditional motor-driven Doppler-shift generators in terms of stability and security while improving accuracy and simplifying assemblies. A simulated wind speed can be determined simultaneously using the light-beat method. The wind error simulated by the generator mainly comes from the light source, which is about 0.63 m/s. An experimental demonstration was conducted using a calibrated Fabry–Perot interferometer and showed that the root mean square of the measurement uncertainty is 0.91 m/s. The novel calibration system was applied to calibrate an asymmetric spatial heterodyne spectrometer (ASHS)-type interferometer successfully. The results demonstrate the feasibility of the system.

Comparison of deterministic wind speed generation models for wind erosion modeling

The diurnal patterns of hourly wind speed data are fundamentally important in precise wind erosion modeling. Several deterministic wind speed simulation models, including the Guo (1/π), Guo (1/2), wind generator (WINDGEN), Ephrath, Debele, and Goudriaan models, have been developed to quantitatively generate hourly wind speed data. In this study, the performances of these popular deterministic wind speed generation models were investigated using observed hourly wind speed data obtained from 191 climate stations in arid and semi-arid areas of China. The performances of the Guo (1/π), Guo (1/2) and WINDGEN models were the best for hourly wind speed generation. Linear regression analysis revealed that all models tended to underestimate wind speeds. The patterns of diurnal variation and the amplitude of the measured hourly wind speed data generally governed the model performance. The performances of the Guo (1/π), WINDGEN, Guo (1/2), Goudriaan, Debele, and Ephrath models successively decreased for the daily average erosive wind power density (AWPD) estimation. To improve the Guo (1/π) and Guo (1/2) models for AWPD evaluation, the calibrated parameters (π and 2) of the second term in the Guo (1/π) and Guo (1/2) models showed different values for each station. The accuracy of the AWPD obtained by the Guo (1/π) and Guo (1/2) models with the calibrated parameters were significantly improved, which could further influence the accuracy of wind erosion modeling. More studies are required to investigate the relationship between the calibrated parameters in the Guo (1/π) and Guo (1/2) models and geographic conditions linked with wind erosion.

Investigation of unique Arabian Sea tropical cyclone with GPU-based WRF model: A case study of Shaheen

The recent severe cyclonic storm (SCS) Shaheen was a rare and unique Tropical Cyclone (TC) formed on September 30, 2021, on the Saurashtra coast off Gujrat, from a depression remnant of another cyclone Gulab off the Bay of Bengal. Under the influence of warm waters, this unique storm travelled westwards, skirting Pakistan's Makran coast before making landfall on northern Oman on October 4, 2021. The present study attempts to understand cyclone Shaheen's track, intensity, and synoptic features with the GPU-based WRF-ARW (v-3.8.1) on the google cloud platform (GCP). A 9 km resolution model domain covering the cyclone's track is initiated with an 18 UTC cycle of the Global Forecasting System (GFS) data. The model skilfully captured Shaheen's track and intensity up to 24 h of lead time which was in close agreement with the India Meteorological Department (IMD) estimates. The simulated track showed an overall track error of approximately 73 km compared to IMD. However, the surface wind stress was in close agreement with IMD. The spatial-temporal analysis of the simulated maximum surface wind speed showed the formation of the distinct eye of the cyclone moving away from the Indian coast towards the Gulf of Oman. Results indicated that Shaheen had a strong low-level (850 hPa) relative vorticity of approximately 193.56 10-5s-1 on 2nd October during the matured severe cyclonic stage, which got reduced to 77.88 × 10-5s-1 on 4th October as the system weakened. Comparison of the simulated synoptic parameters of the storm with reanalysis datasets ERA-5 and NGFS datasets indicated that the model simulated a stronger TC. The cyclone system showed significant relative humidity of about 80–90% with the well-delineated eyewall throughout various stages. The study shows the GPU-based WRF model's reliability with the current set of parameterizations for investigating a unique TC, Shaheen, on a public cloud infrastructure.

Deep learning-based urban morphology for city-scale environmental modeling.

Herein, we introduce a novel methodology to generate urban morphometric parameters that takes advantage of deep neural networks and inverse modeling. We take the example of Chicago, USA, where the Urban Canopy Parameters (UCPs) available from the National Urban Database and Access Portal Tool (NUDAPT) are used as input to the Weather Research and Forecasting (WRF) model. Next, the WRF simulations are carried out with Local Climate Zones (LCZs) as part of the World Urban Data Analysis and Portal Tools (WUDAPT) approach. Lastly, a third novel simulation, Digital Synthetic City (DSC), was undertaken where urban morphometry was generated using deep neural networks and inverse modeling, following which UCPs are re-calculated for the LCZs. The three experiments (NUDAPT, WUDAPT, and DSC) were compared against Mesowest observation stations. The results suggest that the introduction of LCZs improves the overall model simulation of urban air temperature. The DSC simulations yielded equal to or better results than the WUDAPT simulation. Furthermore, the change in the UCPs led to a notable difference in the simulated temperature gradients and wind speed within the urban region and the local convergence/divergence zones. These results provide the first successful implementation of the digital urban visualization dataset within an NWP system. This development now can lead the way for a more scalable and widespread ability to perform more accurate urban meteorological modeling and forecasting, especially in developing cities. Additionally, city planners will be able to generate synthetic cities and study their actual impact on the environment.

Improving extreme offshore wind speed prediction by using deconvolution

This study proposes an innovative method for predicting extreme values in offshore engineering. This includes and is not limited to environmental loads due to offshore wind and waves and related structural reliability issues. Traditional extreme value predictions are frequently constructed using certain statistical distribution functional classes. The proposed method differs from this as it does not assume any extrapolation-specific functional class and is based on the data set's intrinsic qualities. To demonstrate the method's effectiveness, two wind speed data sets were analysed and the forecast accuracy of the suggested technique has been compared to the Naess-Gaidai extrapolation method. The original batch of data consisted of simulated wind speeds. The second data related to wind speed was recorded at an offshore Norwegian meteorological station.

Wetting-consolidation type dust suppressant based on sugarcane bagasse as an environmental material: Preparation, characterization and dust suppression mechanism

Coal mines generate a lot of dust during production and transportation, which not only damages the health of personnel, but also causes environmental pollution. Based on the problems of low extraction efficiency of cellulose matrix and low economy of existing dust suppressants for biomass materials, this paper uses bagasse extracted cellulose from sugar production waste as a matrix and adds polyvinyl alcohol and polyacrylamide as monomers to prepare a wetting-crusting type highly efficient environmental protection dust suppressant for coal mine production and transportation process. The dust suppression effect of the product was analyzed by the performance tests of dust suppression efficiency, consolidation layer strength and permeability. The dust suppression rate of the product prepared in this paper remained above 90% at the simulated wind speed of 10 m/s, the consolidation layer strength of 42.3 KPa was much higher than that of the pure water solution, and the average permeation rate within 30 min was greater than that of the surfactant solution. It is proved that the dust suppressant prepared in this paper has good dust suppression effect, high consolidated layer strength and good permeability, and the product prepared in this paper using environmentally friendly biomass raw materials has good degradability, and the mechanism of the dust suppressant is illustrated by MS simulation. The biomass dust suppressant can meet the requirements of dust suppression in the process of coal mining and transportation and is non-toxic and environmentally friendly.

Optimal Power Flow with Stochastic Renewable Energy Using Three Mixture Component Distribution Functions

The growing usage of renewable energy sources, such as solar and wind energy, has increased the electrical system’s unpredictability. The stochastic behavior of these sources must be considered to obtain significantly more accurate conclusions in the analysis of power systems. To depict renewable energy systems, the three-component mixture distribution (TCMD) is introduced in this study. The mixture distribution (MD) is created by combining the Weibull and Gamma distributions. The results show that TCMD is better than original distributions in simulating wind speed and solar irradiance by reducing the error between real data and the distribution curve. Additionally, this study examines the optimal power flow (OPF) in electrical networks using the two stochastic models of solar and wind energy. The parameters of the probability distribution function (PDF) are optimized using the Mayfly algorithm (MA), which also solves single- and multi-objective OPF issues. Then, to prove the accuracy of the MA method in solving the OPF problem, single- and multi-objective OPF is applied on a standard IEEE-30 bus system to minimize fuel cost, power loss, thermal unit emissions, and voltage security index (VSI), and results are compared with other metaheuristic methods. The outcomes show that the MA technique is dependable and effective in overseeing this challenging problem. Additionally, the suggested OPF MA-based is studied in the OPF problem while accounting for the uncertainty in the models of the wind and solar systems and taking the emissions, VSI, power loss, and fuel cost into consideration in the objective function. The significance of the work lies in the application of a unique optimization technique to a hybrid electrical system using TCMD stochastic model using actual wind and solar data. The proposed MA method could be valuable to system operators as a decision-making aid when dealing with hybrid power systems.

Evaluation of Turbulence and Dispersion in Multiscale Atmospheric Simulations over Complex Urban Terrain during the Joint Urban 2003 Field Campaign

Abstract This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment. Significance Statement Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments.

A Wind-Driven Rotating Micro-Hybrid Nanogenerator for Powering Environmental Monitoring Devices.

In recent years, environmental problems caused by natural disasters due to global warming have seriously affected human production and life. Fortunately, with the rapid rise of the Internet of Things (IoT) technology and the decreasing power consumption of microelectronic devices, it is possible to set up a multi-node environmental monitoring system. However, regular replacement of conventional chemical batteries for the huge number of microelectronic devices still faces great challenges, especially in remote areas. In this study, we developed a rotating hybrid nanogenerator for wind energy harvesting. Using the output characteristics of triboelectric nanogenerator (TENG) with low frequency and high voltage and electromagnetic generator (EMG) with high frequency and high current, we are able to effectively broaden the output voltage range while shortening the capacitor voltage rising time, thus obtaining energy harvesting at wide frequency wind speed. The TENG adopts the flexible contact method of arch-shaped film to solve the problem of insufficient flexible contact and the short service life of the rotating triboelectric generator. After 80,000 cycles of TENG operation, the maximum output voltage drops by 7.9%, which can maintain a good and stable output. Through experimental tests, the maximum output power of this triboelectric nanogenerator is 0.55 mW at 400 rpm (wind speed of about 8.3 m/s) and TENG part at an external load of 5 MΩ. The maximum output power of the EMG part is 15.5 mW at an external load of 360 Ω. The hybrid nanogenerator can continuously supply power to the anemometer after running for 9 s and 35 s under the simulated wind speed of 8.3 m/s and natural wind speed of 5.6 m/s, respectively. It provides a reference value for solving the power supply problem of low-power environmental monitoring equipment.

Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe

Abstract. As wind energy increases its share of total electricity generation and its integration into the power system becomes more challenging, accurately representing the spatio-temporal variability in wind data becomes crucial. Wind fluctuations impact power and energy systems, e.g. energy system planning, vulnerability to storm shutdowns, and available voltage stability support. To analyse such fluctuations and their spatio-temporal dependencies, time series of wind speeds at an hourly or higher frequency are needed. We provide a comprehensive evaluation of the global and mesoscale-model-derived wind time series against observations by using a set of metrics that we present as requirements for wind energy integration studies. We also perform a sensitivity analysis to find the best model setup of the Weather Research and Forecasting (WRF) model, focusing on evaluating the wind speed fluctuation metrics. The results show that using higher spatial resolution in the WRF model simulations improves the representation of temporal fluctuations; however, higher-spatial-resolution simulations often lower the correlations of wind time series with measurements. Thus, we recommend finer-spatial-resolution simulations for modelling power ramp or voltage stability studies but ERA5 rather than mesoscale simulations for studies where correlations with measurements are essential. We also show that the nesting strategy is an important consideration, and a smoother transition from the forcing data to the nested domains improves the correlations with measurements. All mesoscale model simulations overestimate the value of the spatial correlations in wind speed as estimated from observations. Still, the spatial correlations and the wind speed distributions are insensitive to the mesoscale model configuration tested in this study. Regarding these two metrics, mesoscale model simulations present more favourable results than ERA5.

Typhoon Forecasts with Dynamic Vortex Initialization Using an Unstructured Mesh Global Model

Abstract A dynamical vortex initialization (DVI) scheme is implemented on unstructured meshes for the global model MPAS for typhoon forecasts. The DVI extracts the departure vortex within a specified radius of the vortex center and implants this vortex at the observed vortex location in continuously cycled 1-h integrations of the model. The cycling integration is stopped when either the simulated central sea level pressure or maximum wind speed of the typhoon has reached the value in the best track data, denoted as P-match or V-match, respectively. The DVI may spin up the initial vortex with a more contracting eyewall, but still keeping the same size of the outer vortex. Forecasts for 16 typhoons over the western North Pacific in 2015–20 are investigated. Predictions from the experiments with the 60–15-km variable-resolution MPAS mesh show that both P-match and V-match significantly improve the track forecasts, where V-match mostly requires less cycle runs than P-match. Cycling results with P-match or V-match are also dependent on the choice of physics suites within MPAS. Positive impacts are larger for V-match than P-match using the mesoscale reference physics suite, with significantly improved track forecasts and earlier intensity forecasts. Intensity differences resulting from the DVI have gradually decreased with forecast time, which are closely correlated to the differences in the averaged tropospheric potential vorticity of the inner vortex. The DVI with the 60–15–3-km variable-resolution mesh also works well and improves intensity forecasts. The DVI can also help produce asymmetric structures and spin up inner vortex cores for typhoons near high topography, which leads to improved intensity forecasts.

A rank correlation based translation model for simulating wind speed time series

Abstract The translation model is a useful tool for simulating non-Gaussian wind speed time series, it employs marginal transformation to fit the distribution function of wind speed, and deploys a Gaussian process with a specified autocorrelation function (ACF) ρ Z ( τ ) ${\rho }_{Z}\left(\tau \right)$ to match the ACF of wind speed. However, determining ρ Z ( τ ) ${\rho }_{Z}\left(\tau \right)$ requires solving an integral equation, which is not computationally convenient. In this paper, an easy-to-use method is presented for determining ρ Z ( τ ) ${\rho }_{Z}\left(\tau \right)$ . Based on Spearman’s rank correlation coefficient, a rank ACF is proposed to characterize the time-dependent behavior of wind speed time series, whereby ρ Z ( τ ) ${\rho }_{Z}\left(\tau \right)$ can be specified by an analytical formula. In the case that only discrete values of ρ Z ( τ ) ${\rho }_{Z}\left(\tau \right)$ are available, a Laguerre polynomial based model is developed to fit ρ Z ( τ ) ${\rho }_{Z}\left(\tau \right)$ . Furthermore, if the distribution function of wind data is unknown, the kappa distribution is introduced to fit the quantile function of wind speed. Finally, a case study is conducted to check the proposed method, the results indicate that the rank correlation based translation model can yield a satisfactory representation for statistical features of wind speed time series.

Wave climate projection in the Persian Gulf: An ensemble of statistically downscaled CMIP6-GCMs

Wave climate projection can provide useful information for climate change adaptation and mitigation. This study explores the future variability of wave conditions in the Persian Gulf under three different shared socio-economic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Near-surface wind speed simulations of the CMIP6 (Coupled Model Intercomparison Project Phase 6) models are evaluated against the ERA5 reanalysis outputs to find the general circulation models (GCMs) with relatively better performance. The outputs related to the selected GCMs are regionalized through a Weibull-based bias-correction technique, applied to the interpolated grid points. A third-generation numerical wave model is employed to simulate wave characteristics over the computational domain using the modified wind field of each CMIP6 GCM. An ensemble model is obtained by applying the arithmetic mean on the wave model's output of the selected GCMs. The ensemble model projects a mild decrease in the future significant wave height, 95th wave height, and peak wave period to the corresponding values in the historical period. The mean wave direction shows partial variations intensifying for the higher emission scenarios in the southern part of the gulf. Projections of extreme waves for the return periods of 2, 5, 10, 50, and 100 years demonstrate insignificant variation in the future extreme conditions. Moreover, the frequency of extreme events under different SSPs is expected to decrease slightly. Finally, wave steepness and wave age calculations for a location in the middle of the gulf show a mixed condition of swells and sea waves with insignificant variability under climate change scenarios.

Improving Wind Speed Forecasting for Urban Air Mobility Using Coupled Simulations

Hazardous weather, turbulence, wind, and thermals pose a ubiquitous challenge to Unmanned Aircraft Systems (UAS) and electric-Vertical Take-Off and Landing (e-VTOL) aircrafts, and the safe integration of UAS into urban area requires accurate high-granularity wind data especially during landing and takeoff phases. Two models, namely, Open-Source Field Operation and Manipulation (OpenFOAM) software package and Weather Research and Forecasting (WRF) model, are used in the present study to simulate airflow over Downtown Oklahoma City, Oklahoma, United States. Results show that computational fluid dynamics wind simulation driven by the atmospheric simulation significantly improves the simulated wind speed because the accurate modeling of the buildings affects wind patterns. The evaluation of different simulations against six Micronet stations shows that WRF-CFD numerical evaluation is a reliable method to understand the complicated wind flow within built-up areas. The comparison of wind distributions of simulations at different resolutions shows better description of wind variability and gusts generated by the urban flows. Simulations assuming anisotropy and isotropy of turbulence show small differences in the predicted wind speeds over Downtown Oklahoma City given the stable atmospheric stratification showing that turbulent eddy scales at the evaluation locations are within the inertial subrange and confirming that turbulence is locally isotropic.

Adjustment of extreme wind speed in regional climate downscaling over southwestern South Atlantic

AbstractIn this study, we estimated near‐surface wind speed 100‐year return values over the southwestern South Atlantic Ocean, using present‐day (1979–2018) simulations from the regional climate models (RCMs) WRF and RegCM4. Extreme wind events, associated with hazardous conditions over coastal and oceanic areas, must be well represented in numerical models for risk assessment, and few studies focused on the added value offered by RCMs to wind extremes. Events were selected with the peaks‐over‐threshold method and extremes were calculated by fitting peaks to a generalized Pareto distribution. For the assessment of model performance, we used the satellite‐based dataset from the Cross‐Calibrated Multi Platform (CCMP), which has great agreement with in situ observations. While modern reanalysis underestimated higher wind speed quantiles, the CCMP was able to represent these quantiles. Dynamical downscaling with the WRF (RegCM4) indicates an underestimation (overestimation) of wind speed for upper quantiles. To mitigate the effects of these differences in the extreme value estimate, we applied a linear adjustment in the simulated wind speed using the CCMP as reference. This application reduced the bias for higher wind speeds in simulations for all regions, except over the coastal area near Argentina and Uruguay, where downscaling already realistically represents extreme events. The spatial distribution of the simulated extremes is compatible with previous results based on reanalysis and satellite data, although with finer‐scale structure, especially over the southern South Atlantic, a region frequently affected by cyclone occurrence and extreme near‐surface winds. The extreme wind speed maps estimated for 100 years reflect these conditions with values reaching around 30 m·s−1 in the area, even after the wind adjustment. Besides, the fine‐scale features aggregate important new information related to extreme winds in the region, which is a relevant added value in the study of return values estimates.

Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)

Abstract. The Weather Research and Forecasting (WRF) model offers a multitude of physics parameterizations to study and analyze the different atmospheric processes and dynamics that are observed in the Earth's atmosphere. However, the suitability of a WRF model setup is known to be highly sensitive to the type of weather phenomena and the type and combination of physics parameterizations. A multi-event sensitivity analysis is conducted to identify general trends and suitable WRF physics setups for three extreme weather events identified to be potentially harmful for the operation and maintenance of wind farms located in the Belgian offshore concession zone. The events considered are Storm Ciara on 10 February 2020, a low-pressure system on 24 December 2020, and a trough passage on 27 June 2020. A total of 12 WRF simulations per event are performed to study the effect of the update interval of lateral boundary conditions and different combinations of physics parameterizations (planetary boundary layer, PBL; cumulus; and microphysics). Specifically, the update interval of ERA5 lateral boundary conditions is varied between hourly and 3-hourly. Physics parameterizations are varied between three PBL schemes (Mellor–Yamada–Nakanishi–Niino, MYNN; scale-aware Shin-Hong; and scale-aware Zhang), four cumulus schemes (Kain–Fritsch, Grell–Dévényi, scale-aware Grell–Freitas, and multi-scale Kain–Fritsch), and three microphysics schemes (WRF Single-Moment five-class scheme, WSM5; Thompson; and Morrison). The simulated wind direction and wind speed are compared qualitatively and quantitatively to operational supervisory control and data acquisition (SCADA) data. Overall, a definitive best-case setup common to all three events is not identified in this study. For wind direction and wind speed, the best-case setups are identified to employ scale-aware PBL schemes. These are most often driven by hourly update intervals of lateral boundary conditions as opposed to 3-hourly update intervals, although it is only in the case of Storm Ciara that significant differences are observed. Scale-aware cumulus schemes are identified to produce better results when combined with scale-aware PBL schemes, specifically for Storm Ciara and the trough passage cases. However, for the low-pressure-system case this trend is not observed. No clear trend in utilizing higher-order microphysics parameterization considering the combinations of WRF setups in this study is found in all cases. Overall, the combination of PBL, cumulus, and microphysics schemes is found to be highly sensitive to the type of extreme weather event. Qualitatively, precipitation fields are found to be highly sensitive to model setup and the type of weather phenomena.

Impact of the selected boundary layer schemes and enhanced horizontal resolution on the Weather Research and Forecasting model performance on James Ross Island, Antarctic Peninsula

The output of the various Weather Research and Forecasting (WRF) model configurations was compared with ground-based observations in the northern part of James Ross Island, Antarctic Peninsula. In this region, a network of automatic weather stations deployed at ice-free sites (as well as small glaciers) is operated by the Czech Antarctic Research Programme. Data from these stations provide a unique opportunity to evaluate the WRF model in a complex terrain of James Ross Island. The model was forced by the ERA5 reanalysis data and the University of Bremen sea ice data. The model configurations include a novel Three-Dimensional Scale-Adaptive Turbulent Kinetic Energy (3D TKE) planetary boundary layer scheme and a more traditional Quasi-Normal Scale Elimination (QNSE) scheme. Impact of model horizontal resolution was evaluated by running simulations in both 700 m and 300 m. The validation period, 25 May 2019 to 12 June 2019, was selected to cover different stratification regimes of air temperature and a significant snowfall event. Air temperature was simulated well except for strong low-level inversions. These inversions occurred in 44% of all cases and contributed to a higher mean bias (2.0–2.9°C) at low-elevation sites than at high altitude sites (0.2–0.6°C). The selection of the 3D TKE scheme led to improvement at low-elevation sites; at high altitude sites, the differences between model configurations were rather small. The best performance in wind speed simulation was achieved with the combination of the 3D TKE scheme and 300 m model resolution. The most important improvement was decrease of bias at a coastal Mendel Station from 3.5 m·s‑1 with the QNSE scheme on the 700 m grid to 1.2 m·s‑1 with the 3D TKE scheme on the 300 m grid. The WRF model was also proven to simulate a large snowfall event with a good correspondence with the observed snow height.