Applying Satellite Data Assimilation to Wind Simulation of Coastal Wind Farms in Guangdong, China

Wenqing Xu,Yong Luo,Like Ning

doi:10.3390/rs12060973

Abstract

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.

Highlights

Energy from fossil fuels has played a major role in the development of modern human civilization, but it brings serious environmental problems and climate issues, such as atmospheric environmental pollution and global warming
Storm et al [3] used the Weather Research and Forecast (WRF) [4] model to simulate the LLJ, and the model was able to capture some characteristics of LLJ, which indicates that WRF model can be used for short-term wind energy simulation
The results showed that the bias of the wind speed simulation after the 3DVar data assimilation was significantly reduced

Summary

Introduction

Energy from fossil fuels has played a major role in the development of modern human civilization, but it brings serious environmental problems and climate issues, such as atmospheric environmental pollution and global warming. After many years of development, wind speed simulation in wind resource assessment and prediction has two methods: the statistical method and numerical simulation. Costa et al [2] made a brief review about the development of the short-term wind speed forecast during 30 years of history, highlighting that the main forecast method has changed from the statistical model into the numerical model, and that the integration between both models has begun to be used. Storm et al [3] used the Weather Research and Forecast (WRF) [4] model to simulate the LLJ (low-level jet), and the model was able to capture some characteristics of LLJ, which indicates that WRF model can be used for short-term wind energy simulation

Methods

Results

Discussion

Conclusion