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Open AccessArticle

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

by Wenqing Xu 1, Like Ning 2,3 and Yong Luo 1,*
1
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
2
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Yucheng Comprehensive Experiment Station, Chinese Academy of Science, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 973; https://doi.org/10.3390/rs12060973
Received: 19 February 2020 / Revised: 13 March 2020 / Accepted: 16 March 2020 / Published: 17 March 2020
(This article belongs to the Special Issue Remote Sensing in Hydrology and Water Resources Management)
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. View Full-Text
Keywords: data assimilation; WRF; WRFDA; 3DVar data assimilation; WRF; WRFDA; 3DVar
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MDPI and ACS Style

Xu, W.; Ning, L.; Luo, Y. Applying Satellite Data Assimilation to Wind Simulation of Coastal Wind Farms in Guangdong, China. Remote Sens. 2020, 12, 973.

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