Sensitivity to Different Reanalysis Data on WRF Dynamic Downscaling for South China Sea Wind Resource Estimations

Thankaswamy，Anandh1; Xian，Tao1,2; Ma，Yong Feng1,2; Wang，Lian Ping2,3

2022-05-01

10.3390/atmos13050771

Atmosphere   影响因子和分区

2073-4433

As the world is moving toward greener forms of energy, to mitigate the effects of global warming due to greenhouse gas emissions, wind energy has risen as the most invested-in renewable energy. China, as the largest consumer of world energy, has started investing heavily in wind energy resources. Most of the wind farms in China are located in Northern China, and they possess the disadvantage of being far away from the energy load. To mitigate this, recently, offshore wind farms are being proposed and invested in. As an initial step in the wind farm setting, a thorough knowledge of the wind energy potential of the candidate region is required. Here, we conduct numerical experiments with Weather Research and Forecasting (WRF) model forced by analysis (NCEP-FNL) and reanalysis (ERA-Interim and NCEP-CFSv2) to find the best choice in terms of initial and boundary data for downscale in the South China Sea. The simulations are validated by observation and several analyses. Specific locations along China’s coast are analyzed and validated for their wind speed, surface temperature, and energy production. The analysis shows that the model forced with ERA-Interim data provides the best simulation of surface wind speed characteristics in the South China Sea, yet the other models are not too far behind. Moreover, the analysis indicates that the Taiwan Strait along the coastal regions of China is an excellent region to set up wind farms due to possessing the highest wind speeds along the coast.

sensitivity test South China Sea wind energy WRF modeling

SCI ; EI

英语

通讯

National Natural Science Foundation of China[11961131006];National Natural Science Foundation of China[11988102];National Natural Science Foundation of China[42075071];National Natural Science Foundation of China[42075078];National Natural Science Foundation of China[91741101];National Natural Science Foundation of China[91852205];

Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences

Environmental Sciences ; Meteorology & Atmospheric Sciences

WOS:000801320600001

MDPI

20222112148606

Electric power system interconnection ; Gas emissions ; Global warming ; Greenhouse gases ; Offshore oil well production ; Offshore wind farms ; Weather forecasting ; Wind

Meteorology:443 ; Atmospheric Properties:443.1 ; Air Pollution Sources:451.1 ; Oil Field Production Operations:511.1 ; Wind Power (Before 1993, use code 611 ):615.8 ; Electric Power Systems:706.1

2-s2.0-85130370698

Scopus

1.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou),Guangzhou,1119 Haibin Road, Nansha District,511458,China
2.Guangdong Provincial Key Laboratory of Turbulence Research and Applications,Center for Complex Flows and Soft Matter Research and Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications,Southern University of Science and Technology,Shenzhen,518055,China

Thankaswamy，Anandh,Xian，Tao,Ma，Yong Feng,et al. Sensitivity to Different Reanalysis Data on WRF Dynamic Downscaling for South China Sea Wind Resource Estimations[J]. Atmosphere,2022,13(5).

Thankaswamy，Anandh,Xian，Tao,Ma，Yong Feng,&Wang，Lian Ping.(2022).Sensitivity to Different Reanalysis Data on WRF Dynamic Downscaling for South China Sea Wind Resource Estimations.Atmosphere,13(5).

Thankaswamy，Anandh,et al."Sensitivity to Different Reanalysis Data on WRF Dynamic Downscaling for South China Sea Wind Resource Estimations".Atmosphere 13.5(2022).