###### Abstract
It can provide large-scale wind speed information relatively quickly, which benefits wind farm planning and forecasting. However, due to statistical interpolation, it may be necessary to look into some microscale variations in the wind field, leading to less accurate wind speed estimates in specific regions. On the other hand, the WRF nesting method employs multiple grids with different spatial resolutions to simulate the wind field, allowing for more refined wind speed predictions at smaller spatial scales. Nonetheless, the nesting simulation requires more computational resources and time, and its results may be influenced by boundary and initial conditions, necessitating careful consideration of the data accuracy. Meanwhile, the dynamical downscaling method can provide a more accurate representation of meteorological processes and the impact of complex terrain on the wind field. However, it involves more complex computations and relies on a more significant amount of observation data for model verification and calibration. Whether it is for future wind power forecasting using the WRF model or wind resource assessment in a specific region, the choice of different physical schemes (parameterizations) for the planetary boundary layer (PBL) and land surface model (LSM) has a significant impact on wind speed predictions within the boundary layer [21; 22; 23; 24; 25; 26; 27]. The performance of PBL schemes in WRF depends on the simulated region and time, making it challenging to determine a universally optimal model configuration [28]. Several approaches exist in WRF for choosing suitable parameterization schemes. Research by [PERSON] et al. [29], which involved the analysis of the WRF model and optical turbulence intensity (Fried parameter), revealed that the YSU boundary layer parameterization scheme used in the WRF model was accurate for the Baikal Lake region. Additionally, a study by [PERSON] et al. [30] highlighted the superior performance of the WRF scheme in forecasting high-latitude areas in China. [PERSON] et al. [31] utilized the WRF model to assess the wind resources near the Atlantic Ocean. [PERSON] et al. [32] employed mesoscale modeling techniques to accurately simulate offshore wind and stability conditions, resulting in more precise wind resource assessments for offshore wind farm development. From 2016 to 2021, the added capacity of offshore wind power in China witnessed significant growth, rising from 590,000 kilowatts in 2016 to 16,900,000 kilowatts in 2021 [33]. With offshore wind power transitioning from the exploration and cultivation stages to a high-growth phase, the spatial distribution of offshore wind farms has gradually expanded from near-shore areas to deeper and more remote offshore regions [34]. In assessing wind power density in deep-sea regions, installing wind measurement devices is cost prohibitive and it only provides a comprehensive evaluation of wind power density across some areas. On the other hand, methods such as WRF simulation and CFD simulation [35] require substantial computational resources. Therefore, satellite-based wind speed retrieval hasemerged as an economically practical approach for assessing wind power density. Satellite retrieval involves using satellite observation data to estimate surface meteorological parameters. With the continuous development of satellite technology and data analysis algorithms, many researchers have widely adopted satellite data for wind energy assessment and the retrieval of wind field information. The evaluation of wind energy resources in the European North Sea region was first conducted by [PERSON] [36] in 2006 using Envisat ASAR data. The study showcased the capability of satellite data to provide precise spatial information for the planning process of offshore wind farms. Similarly, [PERSON] [37] employed ERS-1/-2 data to assess European offshore wind energy resources. The two marine satellites evaluated in this study were the Sino-French Oceanography Satellite (CFOSAT) and the Haiyang-2C (HY-2C) satellite. [PERSON] et al.