###### Abstract  
This study simulated the spatial distribution of wind speeds and wind energy density by using the WRF model, and we used the WRF-simulated results to evaluate the sea surface wind speeds retrieved from the HY-2C and CFOSAT satellite-borne microwave scatterometers over the Yellow Sea region. The main conclusions were as follows: (1) The combination of the MRF boundary layer parameterization scheme, the MM5 near-surface parameterization scheme, and the Global Data Assimilation System (GDAS) initial field demonstrated the best performance in simulating the 10 m wind speed in the Yellow Sea region, with a root-mean-square error (RMSE) of 1.57, bias of 1.24 m/s, and mean absolute percentage error (MAPE) of 17%. (2) The MAPE of the HY-2C inversion data was 9%, while the CFOSAT inversion data had an MAPE of 6%. The sea surface wind speeds derived from the HY-2C and CFOSAT satellite scatterometer inversions demonstrated high accuracy and applicability in this region. (3) The wind speed was found to increase with altitude over the Yellow Sea, with higher wind speeds observed in the southern region compared to the northern region. The wind power density increased with altitude, and the wind power density in the southern area of the Yellow Sea was higher than in the northern region. (4) The CFOSAT satellite inversion products were in good agreement with the WRF simulation results under low wind speed conditions. In contrast, the HY-2C satellite inversion products showed better agreement under moderate wind speed conditions. Under high wind speed conditions, both satellite inversion products exhibited minor deviations, but the HY-2C product had an overall overestimation, while the CFOSAT product remained within the range of \(-\)1 to 1 m/s. (6) The wind power density increased with the satellite-inverted 10 m wind speed. When the 10 m wind speed was less than 9 m/s, the wind power density exhibited a roughly cubic trend of increase. However, when the 10 m wind speed exceeded 9 m/s, the wind power density no longer increased with the rise in 10 m wind speed. These findings provide valuable insights into wind energy resources in the Yellow Sea region and demonstrate the effectiveness of satellite scatterometer inversions for wind speed estimation. The results have implications for renewable energy planning and management in the area. WRF model; satellite inversion wind speeds; wind energy distribution 2023  
1541720231541  
emission reductions and reduced reliance on imported energy through a diversified energy supply. Wind speed is regarded as the most significant factor affecting wind energy, and the most commonly employed approach for wind energy assessment involves the installation of anemometers at the ground level [1; 2; 3; 4] and using ground-based remote sensing devices for measurements [5]. Driven by the advancements in computer technology and numerical simulation methods, the research on wind energy assessment [6; 7] has shifted its focus from primarily relying on field measurements, statistical analysis, and empirical models [8] to increasingly adopting numerical simulation-based methods [9; 10]. Downscaling wind field predictions [11; 12; 13] enables the transformation of large-scale meteorological forecast data to smaller spatial scales, facilitating more precise wind field information. Utilizing downscaled wind field simulations for wind energy density assessment is an internationally recognized research area, with the WRF model [14; 15; 16] as a prominent tool for evaluating wind energy resources. The methods for downscaled wind speed simulation based on the WRF model [17] primarily include the WRF coarse-grid statistical interpolation method [18], the WRF nesting method [19], and the dynamical downscaling method [20]. The WRF coarse-grid statistical interpolation method is simple and computationally efficient, making it suitable for rapidly estimating wind energy resources over large areas.