With the development of the wind power industry in China, accurate simulation of near-surface wind plays an important role in wind-resource assessment. Numerical weather prediction (NWP) models have been widely used to simulate the near-surface wind speed. By combining the Weather Research and Forecast (WRF) model with the Three-dimensional variation (3DVar) data assimilation system, our work applied satellite data assimilation to the wind resource assessment tasks of coastal wind farms in Guangdong, China. We compared the simulation results with wind speed observation data from seven wind observation towers in the Guangdong coastal area, and the results showed that satellite data assimilation with the WRF model can significantly reduce the root-mean-square error (RMSE) and improve the index of agreement (IA) and correlation coefficient (R). In different months and at different height layers (10, 50, and 70 m), the Root-Mean-Square Error (RMSE) can be reduced by a range of 0–0.8 m/s from 2.5–4 m/s of the original results, the IA can be increased by a range of 0–0.2 from 0.5–0.8 of the original results, and the R can be increased by a range of 0–0.3 from 0.2–0.7 of the original results. The results of the wind speed Weibull distribution show that, after data assimilation was used, the WRF model was able to simulate the distribution of wind speed more accurately. Based on the numerical simulation, our work proposes a combined wind resource evaluation approach of numerical modeling and data assimilation, which will benefit the wind power assessment of wind farms.
High-resolution mesoscale wind-resource assessment of Fiji using the Weather Research and Forecasting (WRF) model