Wind speed and direction predictions by WRF and WindSim coupling over Nyg?rdsfjell

The Science of Making Torque from Wind

Bilal, M.^1 ; Solbakken, K.^1 ; Birkelund, Y.^1

Department of Physics and Technology, UiT, Arctic University of Norway, Mailbox 6050, Langnes, Troms

9037, Norway^1

关键词: Complex terrains;    Coupling methods;    ITS applications;    Root mean square errors;    Standard deviation;    Weather Research and Forecast models;    Wind directions;    Wind speed and directions;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/753/8/082018/pdf
DOI  :  10.1088/1742-6596/753/8/082018

来源: IOP

【 摘 要 】

In this study, the performance of the mesoscale meteorological Weather Research and Forecast (WRF) model coupled with the microscale computational fluid dynamics based model WindSim is investigated and compared to the performance of WRF alone. The two model set-ups, WRF and WRF-WindSim, have been tested on three high-wind events in February, June and October, over a complex terrain at the Nygårdsfjell wind park in Norway. The wind speeds and wind directions are compared to measurements and the results are evaluated based on root mean square error, bias and standard deviation error. Both model set-ups are able to reproduce the high wind events. For the winter month February the WRF-WindSim performed better than WRF alone, with the root mean square error (RMSE) decreasing from 2.86 to 2.38 and standard deviation error (STDE) decreasing from 2.69 to 2.37. For the two other months no such improvements were found. The best model performance was found in October where the WRF had a RMSE of 1.76 and STDE of 1.68. For June, both model set-ups underestimate the wind speed. Overall, the adopted coupling method of using WRF outputs as virtual climatology for coupling WRF and WindSim did not offer a significant improvement over the complex terrain of Nygårdsfjell. However, the proposed coupling method offers high degree of simplicity when it comes to its application. Further testing is recommended over larger number of test cases to make a significant conclusion.