## 2 Data and Methodology
### Verification of Simulation Results
#### 2.3.2 Verify Performance for WRF Model  
The source of all the meteorological data used in this paper is from the China Meteorological Administration ([[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/)) (accessed on 12 December 2022)). These kinds of data have advantages of high accuracy and stability, but the data of each station only represent the surrounding information. Taking the daily average wind speed from December 2016 to February 2017 as the verification data, the physical parameter scheme of case3 in Table 3, the original land use data of the WRF model, and the two vertical stratification methods mentioned above are used to calculate the meteorological information of Litang. Table 4 shows the evaluation index results of the two schemes. The scheme of vertical layers densified near the ground has increased the correlation coefficient by 77.72%, reduced the mean bias by 61.13%, and reduced the root mean square error by 59.04% compared with the non-density scheme. Figure 5 shows the comparison of the change trends of the simulation results and the measured results. It can be seen that the change trend of the calculation results with vertical layers densified near the ground is more consistent with the measured results. Therefore, near-surface encryption can significantly improve the simulation results of the mountain wind field. In order to validate the computed results of four cases presented in Table 3, the daily average wind speed results and precipitation results from December 2016 to February 2017 at Kangding were used for comparison. Table 5 shows the correlation coefficients of the two data. For rainfall, the Purdue-Lin scheme demonstrated a higher correlation coefficient \begin{table}
\begin{tabular}{c c c c} \hline
**ETA Levels** & **R** & **MBE** & **RMSE** \\ \hline
32 & 0.10 & 7.23 & 7.69 \\
41 & 0.44 & 2.81 & 3.15 \\ \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of different ETA levels. Figure 5: Wind speed variation trends at different ETA levels. compared to the Thompson scheme, while for wind speed, the YSU scheme outperformed the MRF scheme. Figure 6 displays the actual simulated and observed values. All four schemes accurately captured the changing trends, but the maximum and minimum wind speeds were higher than the actual results. Considering the comparison of correlation coefficients and the actual trend results, case3, shown in Table 3, appeared to be more reasonable. Therefore, this study adopted the third scheme for calculations in both region 1 and region 2. To verify the accuracy of the calculation results of case3 comprehensively, this paper further validates the results of temperature, wind speed, and rainfall calculated by the WRF model. The simulated data are the WRF calculation results under the parameter settings in Tables 1 and 2 and case3 of Table 3. The validation data used to verify the calculation results of temperature and wind speed are the ground-based meteorological observation data of Kangding and Litang stations. Because there was no precipitation in the actual situation of the two meteorological stations in the observation data of 2022, the feasibility verification of the WRF model for rainfall used the daily average observation data of Litang from December 2016 to February 2017. The comparison results are shown in Table 6, and it can be found that the correlation coefficient of temperature is above 0.80, and the correlation coefficients of wind speed and rainfall are also above 0.50. In addition, the average deviation and root mean square error values are relatively small. By comparing the correlation coefficients presented in this paper with those published in the literature for temperature [39], wind speed [40], and rainfall [41] in similar terrain, it can be observed in Table 6 that the calculated correlation coefficients for temperature and wind speed in this study are slightly higher than those reported in previous studies.