scholarly journals On the Ability of the WRF Model to Reproduce the Surface Wind Direction over Complex Terrain

2013 ◽  
Vol 52 (7) ◽  
pp. 1610-1617 ◽  
Author(s):  
Pedro A. Jiménez ◽  
Jimy Dudhia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Complex Terrain ◽  
Grid Point ◽  
Surface Wind ◽  
Winter Season ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Surface Flow ◽  
High Horizontal Resolution

AbstractThe ability of the Weather Research and Forecasting (WRF) model to reproduce the surface wind direction over complex terrain is examined. A simulation spanning a winter season at a high horizontal resolution of 2 km is compared with wind direction records from a surface observational network located in the northeastern Iberian Peninsula. A previous evaluation has shown the ability of WRF to reproduce the wind speed over the region once the effects of the subgrid-scale topography are parameterized. Hence, the current investigation complements the previous findings, providing information about the model's ability to reproduce the direction of the surface flow. The differences between the observations and the model are quantified in terms of scores explicitly designed to handle the circular nature of the wind direction. Results show that the differences depend on the wind speed. The larger the wind speed is, the smaller are the wind direction differences. Areas with more complex terrain show larger systematic differences between model and observations; in these areas, a statistical correction is shown to help. The importance of the grid point selected for the comparison with observations is also analyzed. A careful selection is relevant to reducing comparative problems over complex terrain.

scholarly journals Sensitivity of the WRF model to PBL parametrizations and nesting techniques: evaluation of surface wind over complex terrain

2015 ◽  
Vol 8 (7) ◽  
pp. 5437-5479 ◽  
Author(s):  
J. J. Gómez-Navarro ◽  
C. C. Raible ◽  
S. Dierer
Keyword(s):  
Wind Speed ◽  
Complex Terrain ◽  
Surface Wind ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Economic Damage ◽  
Climate Modelling ◽  
Spectral Nudging ◽  
Pbl Scheme ◽  
Wind Storms

Abstract. Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a setup of the WRF model that minimizes systematic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable setup: the horizontal resolution, the PBL parameterization scheme and the way WRF is nested to the driving dataset. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model setups, but sharing the same driving dataset. The results show that the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis and spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks. The model skill is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields.

Improving the Representation of Resolved and Unresolved Topographic Effects on Surface Wind in the WRF Model

2012 ◽  
Vol 51 (2) ◽  
pp. 300-316 ◽  
Author(s):  
Pedro A. Jiménez ◽  
Jimy Dudhia
Keyword(s):  
Wind Speed ◽  
Grid Point ◽  
High Surface ◽  
Surface Wind ◽  
Wrf Model ◽  
Surface Wind Speed ◽  
Topographic Effects ◽  
Low Wind Speed ◽  
Sink Term ◽  
Wind Simulation

AbstractThe Weather Research and Forecasting (WRF) model presents a high surface wind speed bias over plains and valleys that constitutes a limitation for the increasing use of the model for several applications. This study attempts to correct for this bias by parameterizing the effects that the unresolved topographic features exert over the momentum flux. The proposed parameterization is based on the concept of a momentum sink term and makes use of the standard deviation of the subgrid-scale orography as well as the Laplacian of the topographic field. Both the drag generated by the unresolved terrain and the possibility of an increase in the speed of the flow over the mountains and hills, where it is herein shown that WRF presents a low wind speed bias, are considered in the scheme. The surface wind simulation over a complex-terrain region that is located in the northeast of the Iberian Peninsula is improved with the inclusion of the new parameterization. In particular, the underestimation of the wind speed spatial variability resulting from the mentioned biases is corrected. The importance of selecting appropriate grid points to compare with observations is also examined. The wind speed from the nearest grid point is not always the most appropriate one for this comparison, nearby ones being more representative. The new scheme not only improves the climatological winds but also the intradiurnal variations at the mountains, over which the default WRF shows limitations in reproducing the observed wind behavior. Some advantages of the proposed formulation for wind-resource evaluation are also discussed.

scholarly journals Assessment of the WRF model performance in air temperature and wind speed simulation over a complex Antarctic topography

2021 ◽  
Author(s):  
Michael Matějka ◽  
Kamil Láska ◽  
Klára Jeklová ◽  
Jiří Hošek
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Antarctic Peninsula ◽  
Wind Direction ◽  
Surface Wind ◽  
Wrf Model ◽  
Seasonal Effects ◽  
Winter Period ◽  
Glacier Mass Balance ◽  
Model Accuracy

<p>The Antarctic Peninsula experiences a strong climate variability which is well reflected in glacier mass balance and state of the other cryospheric components. A better insight into the interactions of the atmosphere and the cryosphere can be obtained using numerical atmospheric models. In the presented work, the Weather Research and Forecasting (WRF) model at 700 m horizontal resolution was validated in the northern part of James Ross Island, Antarctic Peninsula. The model topography was based on the Reference Elevation Model of Antarctica. Sea ice cover was updated daily using high–resolution satellite observations. The WRF output was compared with air temperature, wind speed and wind direction observations from multiple automatic weather stations located in a complex topography and a mosaic of land cover types of Ulu Peninsula. Two periods in 2019/2020 representing contrasting seasons (summer and winter) were selected to identify possible seasonal effects on model accuracy. The three WRF boundary layer schemes (MYJ, MYNN, QNSE) were tested and the spatial and seasonal variability in their performance was evaluated. Simulated air temperatures were in very good agreement with measurements (mean bias –1.7 °C to 1.4 °C). The model was within 2 °C of observations in 47–72 % of the winter period and in 66–79 % of the summer period. An exception was a strong air temperature inversion at two winter days when the model accuracy decreased at low–altitude sites. Additional analysis of the WRF output revealed a good skill in simulating near–surface wind speed with higher correlation coefficients in winter (0.81–0.93) than in summer (0.41–0.59). Wind speed mean bias was mostly lower than 2.5 m·s<sup>–1</sup>, but higher wind speed overestimation was found at a coastal site during the winter validation period. The model successfully captured wind direction, showing only small differences to the observed values. Finally, the model accuracy at coastal and low–altitude sites was found to be more sensitive to the strength of synoptic–scale wind than at higher–altitude sites.  </p>

scholarly journals Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

2015 ◽  
Vol 8 (10) ◽  
pp. 3349-3363 ◽  
Author(s):  
J. J. Gómez-Navarro ◽  
C. C. Raible ◽  
S. Dierer
Keyword(s):  
Wind Speed ◽  
Complex Terrain ◽  
Model Performance ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Data Set ◽  
Spectral Nudging ◽  
Pbl Scheme ◽  
Wind Storms ◽  
Set Up

Abstract. Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a set-up of the Weather Research and Forecasting Model (WRF) model that minimises systematic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable set-up: the horizontal resolution, the planetary boundary layer (PBL) parameterisation scheme and the way the WRF is nested to the driving data set. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model set-ups, but sharing the same driving data set. The results show that the lack of representation of the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis nudging, spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks. The model performance is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields.

scholarly journals Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3050 ◽  
Author(s):  
Carlos Otero-Casal ◽  
Platon Patlakas ◽  
Miguel A. Prósper ◽  
George Galanis ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Wrf Model ◽  
Statistical Error ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Integrated System ◽  
Combined System ◽  
Local Flow ◽  
Bayesian Filter

Regional microscale meteorological models have become a critical tool for wind farm production forecasting due to their capacity for resolving local flow dynamics. The high demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting (WRF) atmospheric model with an optimization obtained by the conjunction of a Kalman filter and a Bayesian model. This study focuses on the development and validation of this combined system in a very dense wind farm cluster located in Galicia (Northwest of Spain). A period of one year is simulated at 333 m horizontal resolution, with a daily operational forecasting set-up. The Kalman-Bayesian filter was tested both directly on wind speed and on the U-V (zonal and meridional) components for nowcasting periods from 10 min to 6 h periods, all of them with important applications in the wind industry. The results are quite promising, as the main statistical error indices are significantly improved in a 6 h forecasting horizon and even more in shorter horizon cases. The Mean Annual Error (MAE) for 1 h nowcasting horizon is 1.03 m/s for wind speed and 12.16 ° for wind direction. Moreover, the successful utilization of the integrated system in test cases with different characteristics demonstrates the potential utility that this tool may have for a variety of applications in wind farm operations and energy markets.

scholarly journals Numerical Simulation of the Aeroelastic Response of Wind Turbines in Typhoons Based on the Mesoscale WRF Model

2019 ◽  
Vol 12 (1) ◽  
pp. 34
Author(s):  
Long Wang ◽  
Cheng Chen ◽  
Tongguang Wang ◽  
Weibin Wang
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Direction ◽  
Flow Direction ◽  
Wrf Model ◽  
Normal Operation ◽  
Average Wind Speed ◽  
Aeroelastic Response ◽  
Response Characteristics ◽  
Wind Speeds

A new simulation method for the aeroelastic response of wind turbines under typhoons is proposed. The mesoscale Weather Research and Forecasting (WRF) model was used to simulate a typhoon’s average wind speed field. The measured power spectrum and inverse Fourier transform method were coupled to simulate the pulsating wind speed field. Based on the modal method and beam theory, the wind turbine model was constructed, and the GH-BLADED commercial software package was used to calculate the aerodynamic load and aeroelastic response. The proposed method was applied to assess aeroelastic response characteristics of a commercial 6 MW offshore wind turbine under different wind speeds and direction variation patterns for the case study of typhoon Hagupit (2008), with a maximal wind speed of 230 km/h. The simulation results show that the typhoon’s average wind speed field and turbulence characteristics simulated by the proposed method are in good agreement with the measured values: Their difference in the main flow direction is only 1.7%. The scope of the wind turbine blade in the typhoon is significantly larger than under normal wind, while that under normal operation is higher than that under shutdown, even at low wind speeds. In addition, an abrupt change in wind direction has a significant impact on wind turbine response characteristics. Under normal operation, a sharp variation of the wind direction by 90 degrees in 6 s increases the wind turbine (WT) vibration scope by 27.9% in comparison with the case of permanent wind direction. In particular, the maximum deflection of the wind tower tip in the incoming flow direction reaches 28.4 m, which significantly exceeds the design standard safety threshold.

The Effects of SST-Induced Surface Wind Speed and Direction Gradients on Midlatitude Surface Vorticity and Divergence

2010 ◽  
Vol 23 (2) ◽  
pp. 255-281 ◽  
Author(s):  
Larry W. O’Neill ◽  
Dudley B. Chelton ◽  
Steven K. Esbensen
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Spatial Scales ◽  
Kuroshio Extension ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Small Scale ◽  
Strong Wind ◽  
Sst Variability ◽  
Seawinds Scatterometer

Abstract The effects of surface wind speed and direction gradients on midlatitude surface vorticity and divergence fields associated with mesoscale sea surface temperature (SST) variability having spatial scales of 100–1000 km are investigated using vector wind observations from the SeaWinds scatterometer on the Quick Scatterometer (QuikSCAT) satellite and SST from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Aqua satellite. The wind–SST coupling is analyzed over the period June 2002–August 2008, corresponding to the first 6+ years of the AMSR-E mission. Previous studies have shown that strong wind speed gradients develop in response to persistent mesoscale SST features associated with the Kuroshio Extension, Gulf Stream, South Atlantic, and Agulhas Return Current regions. Midlatitude SST fronts also significantly modify surface wind direction; the surface wind speed and direction responses to typical SST differences of about 2°–4°C are, on average, about 1–2 m s−1 and 4°–8°, respectively, over all four regions. Wind speed perturbations are positively correlated and very nearly collocated spatially with the SST perturbations. Wind direction perturbations, however, are displaced meridionally from the SST perturbations, with cyclonic flow poleward of warm SST and anticyclonic flow poleward of cool SST. Previous observational analyses have shown that small-scale perturbations in the surface vorticity and divergence fields are related linearly to the crosswind and downwind components of the SST gradient, respectively. When the vorticity and divergence fields are analyzed in curvilinear natural coordinates, the wind speed contributions to the SST-induced vorticity and divergence depend equally on the crosswind and downwind SST gradients, respectively. SST-induced wind direction gradients also significantly modify the vorticity and divergence fields, weakening the vorticity response to crosswind SST gradients while enhancing the divergence response to downwind SST gradients.

The European wind from observational and simulated databases

2020 ◽  
Author(s):  
Elena García-Bustamante ◽  
Jorge Navarro ◽  
Jesús Fidel González-Rouco ◽  
E. Etor Lucio- Eceiza ◽  
Cristina Rojas-Labanda ◽  
...  
Keyword(s):  
Wind Field ◽  
Mesoscale Model ◽  
Surface Wind ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Spatial And Temporal Variability ◽  
Model Simulations ◽  
Wind Variability ◽  
Observational Database ◽  
Wind Velocities

<p>The New European Wind Atlas (https://map.neweuropeanwindatlas.eu) is developed based on the simulated wind field over Europe from a mesoscale model coupled to a microscale component through a statistical downscaling approach. The simulation that provides mesoscale inputs within the model chain has been decided upon a careful sensitivity analysis of potential model configurations. In order to accomplish model resolutions of 3 km over Europe, the broader European domain is partitioned into a set of 10 partially overlapping tiles. The wind field is simulated with the WRF model over these tiles and finally blended into a single domain. The wind outputs from a reference simulation is evaluated on the basis of its comparison with an observational database specifically compiled and quality controlled for the purpose of validating the wind atlas over the complete European domain. The observational database includes surface wind observations at ca. 4000 sites as well as 16 masts datasets. The observational dataset of surface wind (WISED) is informative about the spatial and temporal variability of the wind climatology, punctuated with singular masts that provide information of wind velocities at height. The validation of the mesoscale simulation aims at investigating the ability of the high-resolution simulation to reproduce the observed intra-annual variability of daily wind within the entire domain.</p><p>Observed and simulated winds are higher at the British, North Sea and Baltic shores and lowlands. Correlations are typically over 0.8. Surface wind variability tends to be overestimated in the northern coasts and underestimated elsewhere and inland. Mast wind variability tends to be overestimated except for some southern sites. Seasonal differences seem minor in these respects. Biases and RMSE can help identifying if systematic errors in specific tiles take place.</p><p>Therefore, performing model simulations of a high horizontal resolution over the broader European domain is possible. We can learn about the variability of surface and height wind both from observations and model simulations. Model observations are not perfect, but observations also present uncertainties. Good quality wind data, both at the surface and in masts are a requisite for robust evaluation of models. European wide features of wind variability can be recognized both in observations and simulations.</p>

scholarly journals Surface Wind Regionalization over Complex Terrain: Evaluation and Analysis of a High-Resolution WRF Simulation

2010 ◽  
Vol 49 (2) ◽  
pp. 268-287 ◽  
Author(s):  
Pedro A. Jiménez ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
Jorge Navarro ◽  
Juan P. Montávez ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Dynamical Downscaling ◽  
Surface Wind ◽  
Wrf Model ◽  
Spatial Dimension ◽  
Weather Research And Forecasting ◽  
Observational Period ◽  
Spatiotemporal Resolution ◽  
Wind Variability ◽  
Spatial Coverage

Abstract This study analyzes the daily-mean surface wind variability over an area characterized by complex topography through comparing observations and a 2-km-spatial-resolution simulation performed with the Weather Research and Forecasting (WRF) model for the period 1992–2005. The evaluation focuses on the performance of the simulation to reproduce the wind variability within subregions identified from observations over the 1999–2002 period in a previous study. By comparing with wind observations, the model results show the ability of the WRF dynamical downscaling over a region of complex terrain. The higher spatiotemporal resolution of the WRF simulation is used to evaluate the extent to which the length of the observational period and the limited spatial coverage of observations condition one’s understanding of the wind variability over the area. The subregions identified with the simulation during the 1992–2005 period are similar to those identified with observations (1999–2002). In addition, the reduced number of stations reasonably represents the spatial wind variability over the area. However, the analysis of the full spatial dimension simulated by the model suggests that observational coverage could be improved in some subregions. The approach adopted here can have a direct application to the design of observational networks.

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