Simulated Wind Fields Research Articles

Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields

The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy.

An Automated Method to Analyze Tropical Cyclone Surface Winds from Real-Time Aircraft Reconnaissance Observations

Abstract This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within 5 h prior and 3 h after analysis time and makes use of prescribed methods to move observations to a common flight level (CFL; 700 hPa) for analysis and to reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the undersampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is <10 kt (<5 m s−1). Significance Statement Many applications need estimates of 2D surface winds in tropical cyclones in real time. While real-time aircraft-based observations of the winds inside tropical cyclones have been available for several decades, there have been few automated and objective methods to analyze this information to provide estimates of the strength and distribution of the surface winds. Here, we provide details of one method that fuses these unique observations to provide useful 2D analyses of the winds in and around tropical cyclones.

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.

Dynamic response of a sea-crossing cable-stayed suspension bridge under simultaneous wind and wave loadings induced by a landfall typhoon

Sea-crossing bridges with long spans are sensitive to wind and wave environments. The estimation of the dynamic response under simultaneous wind and wave loadings is hence important for the safety of bridges, especially during the landfall of typhoons when extreme wind and wave actions usually occur. In this paper, a framework combining the simulation of typhoon wind and wave fields, calculation of aerodynamic and hydrodynamic loadings, and structural analysis was adopted to assess the dynamic response of a sea-crossing bridge. A cable-stayed suspension bridge located in the typhoon-affected area was taken as an example. The inhomogeneity of wave loading, the time-varying dynamic response of bridges during the landfall process, and the effect of critical wave load calculation on extreme response were presented and discussed. Results demonstrated that wave loadings at different pies show strong inhomogeneity during the landfall process. The typhoon-induced simultaneous wind and wave loadings could affect not only the amplitude of the power spectrum density but also the number of dominant modes of the response. In addition, the error brought by excluding the wave inhomogeneity and the time lag of wind and wave in estimating the extreme response was acceptable when the maximum wind loadings were adopted.

Wind energy assessment considering a truncated distribution of probabilistic turbulence power spectral parameters

Accurate probabilistic wind spectra are beneficial to reproducing or predicting wind speeds, then assessing wind energy. The non-truncated distribution is widely-used for fitting spectral parameters. However, it is not consistent with the bounded measured parameters. Therefore, the truncated distribution, firstly, is proposed in modelling three spectral parameters in the Ma'anshan Yangtze River (MYR) Bridge. The distribution parameters conditional to wind velocities are determined through estimated extreme values under a given recurrence period, which is derived from the established joint distribution of extreme spectral parameter and wind velocities. The fitness of the truncated distribution on measured values shows better performance than the non-truncated distribution. The dimension-reduction (DR) expression of stochastic wind fields is represented by considering probability-truncated wind spectra. The simulation of stochastic wind fields at the MYR Bridge is realized based on the proposed DR scheme. The comparisons of wind velocities and wind power densities are implemented between truncated and non-truncated distributions. Results shows the feasibility of the proposed method to exhibit probability of local wind fields. Meanwhile, wind speeds and wind power densities are more significant in the truncated distribution.

Low-level wind shear of wind field modelling and simulation

Low-level wind shear of wind field modelling and simulation

Geometry effects on wind fields and consequent wave generation in the Strait of Hormuz: A case study

This study investigates the impact of surface wind forcing on wave conditions in the Strait of Hormuz, a region with complex wave interactions. The WRF model is used to simulate the wind field with higher accuracy, enabling the generation of waves for both normal and storm conditions in 2011. A sensitivity analysis examines the WRF model's simulated wind field with variations in initial and boundary conditions, spatial resolutions, and adopting two static topographic datasets. Comparisons between simulated wave parameters and observed data from ADCPs at two stations flanking the strait reveal the importance of accurate wind forcing for obtaining a realistic estimation of wave conditions. Wave errors are found to be influenced by fetch length, with larger errors observed for shorter fetches (∼100 km). However, these errors gradually decrease as the distance from the coast increases. The study emphasizes the importance of incorporating accurate wind data and considering fetch characteristics when simulating wave conditions, which can enhance maritime safety, coastal engineering, and offshore operations in the Strait of Hormuz and other similar regions worldwide. In conclusion, small-scale wind and waves features over the Strait of Hormuz can be better captured with a combination of higher-resolution wind modeling and wave simulation grids with higher spatial resolution.

Satellite-Based Optimization and Planning of Urban Ventilation Corridors for a Healthy Microclimate Environment

Urban ventilation corridors (UVCs) have the potential to effectively mitigate urban heat islands and air pollution. Shanghai, a densely populated city located in eastern China, is among the hottest cities in the country and requires urgent measures in order to enhance its ventilation system. This study introduces a novel approach that integrates land surface temperature retrieval, PM2.5 concentration retrieval, and wind field simulation to design UVCs at the city level. Through remote sensing data inversion of land surface temperature (LST) and PM2.5 concentration, the study identifies the action spaces and compensation spaces for UVCs. The Weather Research and Forecasting (WRF) model, coupled with the multilayer urban scheme Building Effect Parameterization (BEP) model, is employed to numerically simulate and analyze the wind field. Based on the identification of thirty high-temperature zones and high PM2.5 concentration zones as action spaces, and twenty-two low-temperature zones and low PM2.5 concentration zones as compensation spaces in Shanghai, the study constructs seven first-class ventilation corridors and nine secondary ventilation corridors according to local circulation patterns. Unlike previous UVC research, this study assesses the cleanliness of cold air, which is a common oversight in UVC planning. Ignoring the assessment of cold air cleanliness can result in less effective UVCs in improving urban air quality and even exacerbate air pollution in the central city. Therefore, this study serves as a crucial contribution by rectifying this significant deficiency. It not only provides a fresh perspective and methodology for urban-scale ventilation corridor planning but also contributes to enhancing the urban microclimate by mitigating the effects of urban heat islands and reducing air pollution, ultimately creating a livable and comfortable environment for urban residents.

Evaluation of sea surface roughness parameterization in meso-to-micro scale simulation of the offshore wind field

Accurate parameterization of the wind-induced effects on the Marine Atmospheric Boundary Layer (MABL) is of fundamental importance for many applications including weather forecasting and offshore wind energy. Roughness parameterizations derived from different datasets are widely used but their performance is not sufficiently assessed under realistic marine environmental conditions. To this end, a multi-scale atmosphere–wave coupled model is constructed for the first time and used in the simulation of the MABL flow over the North Sea. Results show that our model is able to capture the large-scale variation of the mean wind meanwhile reproducing the appropriate turbulence energy cascade in the frequency domain. The performance of different roughness parameterizations is evaluated in comparison with the measurement data from the FINO1 platform. The Taylor-Yelland method outperforms the others on statistics of the mean wind while yielding the highest bias of the Turbulence Kinetic Energy (TKE) from the observations. However, we find that the discrepancy between our simulation and observation mainly depends on the meso-scale model. The magnitude of the differences caused by roughness parameterizations is negligible, especially in low wind speed conditions. The micro-scale simulation is demonstrated as not sensible to the roughness input in the considered wind–wave condition.

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.

Dimension Reduction Method-Based Stochastic Wind Field Simulations for Dynamic Reliability Analysis of Communication Towers

The communication tower is a lifeline engineering that ensures the normal operation of wireless communication systems. Extreme wind disasters are inevitable while it is in service. Two dimension-reduction (DR) probabilistic representations based on proper orthogonal decomposition (POD) and wavenumber spectral representation (WSR), say DR-POD and DR-WSR, were thus proposed in this study. In order to determine the least representative sample size that satisfies the engineering accuracy requirements, the simulation error and simulation duration of 10 simulation points distributed along the height direction of the communication tower under different representative sample numbers were compared. Furthermore, for the fluctuating wind field with different numbers of simulation points distributed along the height of the communication tower, the simulation accuracy as well as efficiency of the DR-POD and the DR-WSR were compared. Finally, a high-rise communication tower structure’s wind-induced dynamic response study and wind-resistance reliability analysis were performed utilizing an alliance of the probability density evolution method (PDEM) and two DR probabilistic models, taking 10 load points into account. The structural dynamic analysis illustrates that the reliability of the communication tower structure and the wind-induced dynamic response allying the two DR probabilistic models with the PDEM have outstanding consistency.

Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling

Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5.

A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields

AbstractThough tropical cyclone (TC) models have been routinely evaluated against track and intensity observations, little work has been performed to validate modeled TC wind fields over land. In this paper, we present a simple framework for evaluating simulated low‐level inland winds with in‐situ observations and existing TC structure theory. The Automated Surface Observing Systems, Florida Coastal Monitoring Program, and best track data are used to generate a theory‐predicted wind profile that reasonably represents the observed radial distribution of TC wind speeds. We quantitatively and qualitatively evaluated the modeled inland TC wind fields, and described the model performance with a set of simple indicators. The framework was used to examine the performance of a high‐resolution two‐way nested Geophysical Fluid Dynamics Laboratory model on recent U.S. landfalling TCs. Results demonstrate the capacity of using this framework to assess the modeled TC low‐level wind field in the absence of dense inland observations.

The development and validation of the Inhomogeneous Wind Scheme for Urban Street (IWSUS-v1)

Abstract. The layout of urban buildings shows significant heterogeneity, which leads to the significant spatial inhomogeneity of the wind field in and over the canopy of urban street canyons. However, most of the current urban canopy models do not fully consider the heterogeneity of the urban canopy. Large discrepancies thus exist between the wind speeds simulated by the current urban canopy models and those observed in the street canyon. In this study, a parameterization scheme for wind fields, Inhomogeneous Wind Scheme for Urban Street (IWSUS), is developed to better characterize the heterogeneity of the urban canopy. We use a computational fluid dynamics method to generate the IWSUS scheme and compare it with observations of the wind profile and turbulent flux in and over the street canyon for validation. In IWSUS, the wind speed vertical profiles at six representative positions located in a typical street canyon (i.e., the windward or leeward side of a long straight street or the inflow or outflow end) are parameterized separately. The wind profile by IWSUS thus can better describe the horizontal heterogeneity of the urban near-surface wind field, e.g., the dynamic drag effect of buildings in the lower atmospheric layer over the urbanized land use. The validation based on observations shows that the performance of simulation results by IWSUS is better than that by the exponential–logarithmic (exp-log) law widely used in the current urban schemes. We consider typical building arrangement and specific street orientations in IWSUS for wind field simulations, which can better match the distribution characteristics of street canyons around the observation point in the street canyon. The averaged wind profiles and turbulence energy fluxes in the model grids of urban areas by IWSUS are also nearer to the observations than those by the exp-log law. The normalized mean errors (NMEs) between the simulated and the observed vertical average wind speed are 49.0 % for IWSUS and 56.1 % for exp-log law in the range from the ground to 4 times the average height of the buildings and 70 % for IWSUS and 285.8 % for exp-log law in the street canyon (range from the ground to building top). This study proves that the accuracy of simulations of land surface processes and near-ground meteorological processes over the urban canopy can be improved by fully considering the heterogeneity of the urban canopy layout structures and the inhomogeneity of wind field distributions in and over the street canyon. IWSUS is expected to be coupled with mesoscale atmospheric models to improve the accuracy of the wind field, land surface energy budget, meteorological and atmospheric chemistry simulations.

Regional wave climate projections forced by EURO‐CORDEX winds for the Black Sea and Sea of Azov towards the end of the 21st century

AbstractWave phenomena impact high commercial value coastal and offshore activities, infrastructures and transportation. The knowledge of future wave conditions allows for consistent long‐term planning and decision‐making. The present study aims to provide robust, reliable projections of the potential future wave conditions of the Black Sea under the influence of climate change. For this purpose, an eight‐member dynamic wave climate ensemble that accurately represents the Black Sea's present wave climate has been produced, and future projections are assessed and analysed here. The wave climate ensemble was obtained by forcing the Simulating Waves Nearshore spectral wave model with eight regional wind fields from the EURO‐CORDEX. The future simulations of the regional wind fields are based on the RCP8.5 high‐emission scenario. The historical wave climate of the ensemble was evaluated against ERA5 reanalysis data. Projected changes in the mean wind and wave characteristics are examined by comparing historical and future simulations. In addition, the projected trends in the annual means during the future period of the wave simulations for significant wave height (Hs) and wave energy flux are also analysed. The projections for Hs and wind speeds are pronounced with significant decreases down to −10% across the basin, especially in the eastern region of the Black Sea. Changes in the Hs 99% percentiles of up to 16% are projected to occur. The projected changes in the annual mean of the wave energy flux are close to the projected changes in Hs means, while the seasonal changes (between −15% and 12%) are expected to be higher.

Comparison of the wake characteristics and aerodynamic response of a wind turbine under varying atmospheric conditions using WRF-LES-GAD and WRF-LES-GAL wind turbine models

A generalized actuator line model (GAL) is implemented in the Weather Research and Forecasting (WRF) (Skamarock and Klemp, 2008) model to carry out high-fidelity large-eddy simulations (LESs) of turbulent wind fields in stratified atmospheric boundary layer (ABL) flows and study the effects of atmospheric stability on the wake characteristics and aerodynamic response of a wind turbine (WT). The performance of Vestas V27 WT has been evaluated paying attention to the wind turbine wake behavior and the aerodynamic performance using the already available generalized actuator disk (GAD) and the recently implemented GAL model. Spatial distributions of mean velocity components, their variances and instantaneous vorticity in the wake of the wind turbine are analyzed by comparing the results from GAD and GAL approaches. Results of the wake velocity deficit profiles and wind turbine aerodynamic response have been compared with the experimental ones obtained in the Scaled Wind Farm Technology campaign and available LES data reported in the works of Doubrawa et al. (2020) and Jézéquel et al. (2021). The results from the GAD and GAL models, including the aerodynamic effects of nacelle and tower, agree reasonably well with the results from other LES codes and experimental data, with minor differences between these two models regarding velocity deficits in the near-wake region and wind turbine response under varying atmospheric stability conditions. Overall, the GAL model is able to reproduce the observed wake behavior and the aerodynamic response of the Vestas V27 for the atmospheric conditions tested.

Simulating Multivariate Multidimensional Homogenous Non-Gaussian Field Based on Unified Hermite Polynomial Model

Reasonable simulation of stochastic fields is a key prerequisite for addressing stochastic mechanics problems with Monte Carlo simulation solver. This paper presents a novel method to simulate m-variate and n-dimensional (mV-nD) homogenous non-Gaussian fields according to the prescribed power spectral density matrix (PSDM) and first four moments. In the proposed method, the unified Hermite polynomial model (UHPM) is first extended to mV-nD homogenous non-Gaussian fields. Then, based on the extended UHPM, a complete transformation model from the normalized non-Gaussian correlation function matrix (CFM) into the underlying Gaussian CFM with its applicable range is derived. In addition, two types of potential incompatibility of the transformation arising from the prescribed PSDM and first four moments are defined and neatly solved. Finally, a unified simulation framework for mV-nD homogenous non-Gaussian fields is presented, where the fast Fourier transform technique can be embedded in both spectral representation method and Wiener-Khintchine transformation to speed up the simulation progress. Two numerical examples including the simulations of non-Gaussian wind fields and non-Gaussian fields of material properties are presented to demonstrate the effectiveness of the proposed method.

The Use of Small Uncrewed Aircraft System Observations in Meteorological and Dispersion Modeling

Abstract A series of meteorological measurements with a small uncrewed aircraft system (sUAS) was collected at Oliver Springs Airport in Tennessee. The sUAS provides a unique observing system capable of obtaining vertical profiles of meteorological data within the lowest few hundred meters of the boundary layer. The measurements benefit simulated plume predictions by providing more accurate meteorological data to a dispersion model. The sUAS profiles can be used directly to drive HYSPLIT dispersion simulations. When using sUAS data covering a small domain near a release and meteorological model fields covering a larger domain, simulated pollutants may be artificially increased or decreased near the domain boundary because of inconsistencies in the wind fields between the two meteorological inputs. Numerical experiments using the Weather Research and Forecasting (WRF) Model with observational nudging reveal that incorporating sUAS data improves simulated wind fields and can significantly affect mixing characteristics of the boundary layer, especially during the morning transition period of the planetary boundary layer. We conducted HYSPLIT dispersion simulations for hypothetical releases for three case study periods using WRF meteorological fields with and without assimilating sUAS measurements. The comparison of dispersion results on 15 and 16 December 2021 shows that using sUAS observational nudging is more significant under weak synoptic conditions than under strong influences from regional weather. Very different dispersion results were introduced by the meteorological fields used. The observational nudging produced not just an sUAS-nudged wind flow but also adjusted meteorological fields that further impacted the mixing calculation in HYSPLIT.

Simulation of turbulent wind field in multi-spatial dimensions using a novel non-uniform FFT enhanced stochastic wave-based spectral representation method

Simulation of stochastic wind field is necessary for wind-induced vibration analysis of wind-sensitive structures. Stochastic wave-based spectral representation method (SWSRM) is one of the most widely used methods for wind field simulation. However, it may face challenges to simulate turbulent wind field in multi-spatial dimensions due to its large memory consumption and low simulation efficiency. In this study, a novel non-uniform FFT (NUFFT) enhanced SWSRM is proposed. The approach for selecting sampling points of wave number is also established based on adaptive integral method. Numerical experiments are further designed to demonstrate the validity and effectiveness of the enhanced SWSRM. Results show that the proposed method can significantly improve the simulation efficiency and reduce the memory consumption in multi-spatial dimensions. In addition, the simulation points can be arbitrarily selected using NUFFT enhanced SWSRM. More importantly, the proposed method can improve the turbulent spectral accuracy in low-frequency region.

Multivariate non-Gaussian process simulation based on HPM-JTM hybrid model

For the simulation of wind field or wind load, the Gaussian assumption is not applicable in some situations. Hence the simulation of non-Gaussian process becomes significant. In order to avoid the iteration and maintain a wide application range, a HPM-JTM hybrid model is proposed based on translation process. During the simulation, the correlation function of the related Gaussian process can be solved by analytical or numerical expression. Through a numerical example, the application of the model is presented. Results show that the model provides a reasonable estimation for the target case, which can be regarded as an appropriate candidate for the simulation of multivariate non-Gaussian process.