## 2 Materials and Methods
### Dataset
#### 2.2.3 Sensitivity Experiments  
The WRF sensitivity experiments aimed to investigate and assess the impact of different parameterization schemes, initial conditions, and other variables on the simulation results. In this study we conducted two sets of sensitivity experiments to examine the influence of different boundary layer and near-surface parameterization schemes and different initial conditions on wind speed simulations. Different Boundary Layer and Near-Ground Parameterization Schemes (Sensitivity Experiment A)  
The simulated domain in this study is located in the Yellow Sea, with a center coordinate of (34.26\({}^{\circ}\)N, 120.57\({}^{\circ}\)E) (as shown in Figure 1). Three nested schemes were employed for WRF simulation, with horizontal grid resolutions of 150 \(\times\) 150 (9 km), 136 \(\times\) 136 (3 km), and 124 \(\times\) 124 (1 km), respectively. The simulation period was initiated on 11 October 2021 at 00:00 (UTC), and concluded on 18 October 2021 at 18:00 (UTC), with a time interval of 15 min. The GDAS data, updated every 6 h, were utilized as the initial fields. A forecast lead time of 6 h was applied, and the spatial resolution was set at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). The microphysics scheme employed in WRF was the Lin scheme, with the longwave radiation scheme implemented as rrtmg. The land surface process was modeled using the Noah scheme, and the K-F scheme was chosen for cumulus parameterization (with the cumulus parameterization scheme deactivated in the second and third nested domains). In this paper, seven experiments were designed to assess the impact of boundary layer and near-surface parameterization schemes on wind speed simulations. The turbulence and mixing option selected evaluated mixing terms in physical space. The eddy coefficient option adopts a first-order closure model using horizontal Smagorinsky, while no 6 th-order diffusion was applied. Furthermore, Rayleigh damping with a damping coefficient of 0.2 was implemented. The specific experimental setups and assessment results are shown in Table 2. To ensure the accuracy and reliability of the simulation results, a spin-up process was carried out prior to the WRF simulations. The initial 4 h period of the WRF simulations was designated as the spin-up phase, aiming to attain a stable model state. This procedure guaranteed the physical validity of the initial conditions, preventing abrupt jumps and reducing errors and instabilities during the simulation. Figure 1: Simulation area of sensitivity experiment A: the red pentagram shows the location of the wind farm. As shown in Table 2, exp1 (employed the YSU boundary layer scheme and MM5 near-surface scheme) exhibited better simulation results with a correlation coefficient of 0.92, RMSE of 1.95, bias of 1.57, and MAPE of 0.21. Similarly, Exp2 also had good performance with a correlation coefficient of 0.92, RMSE of 1.61, bias of 1.21, and MAPE of 0.18, making it the best-performing combination among the seven setups. Conversely, exp6 and exp7 displayed relatively poor simulation results. Exp6, employing the MYNN2.5 boundary layer scheme and MYNN near-surface scheme, resulted in a low correlation coefficient of 0.11, RMSE of 3.38, bias of 2.61, and MAPE of 0.40. Exp7, adopting the QNSE boundary layer scheme and QNSE near-surface scheme, yielded a correlation coefficient of 0.59, RMSE of 5.68, bias of 5.00, and MAPE of 0.72. Considering the correlation coefficient, RMSE, bias, and MAPE, it can be concluded that exp1 and exp2 demonstrated superior simulation performance, while exp6 and exp7 performed relatively poorly. Therefore, the MRF boundary layer parameterization scheme and MM5 near-surface parameterization scheme were selected for subsequent simulations in this study. Different Initial Fields  
The analysis of Group A's sensitivity experiments determined that the MRF boundary layer parameterization scheme and MM5 near-surface parameterization scheme (i.e., exp2) exhibited the best simulation performance. The experiments utilized the GDAS's initial field.