## 4 Results and Discussion
### High Level Wind Speed Extrapolation and Validation  
In the SJB area, starting from the 30 m and 50 m 10-min observations, the \(\alpha_{30-50}\) is calculated and then used to estimate the wind resource at a higher level. In particular, the 10-min \(\alpha_{30-50}\) is calculated using the filtered dataset \(W\) by implementing the methods of PL, WSE-RG, WSE-WDSD, WSE-WSR and WSE-MO. All the methods are adopted to calculate the wind shear exponents for the three areas and the results are listed in Table 8. At the same time, because the two-level temperature data at GFC and HNH is unavailable and the WSE-RG and WSE-MO methods cannot be used, the wind shear exponents of the two areas are calculated using the PL, WSE-WDSD, and WSE-WSR methods. It is shown that the wind shear exponent is larger under stable conditions and smaller under unstable conditions, especially in HNH. It is found that, using the WSE-WSR method, the variation of wind shear exponent with the atmospheric stability is more obvious than that using the WSE-WDSD method. Using the results of wind shear exponent in Table 8, the wind speeds at the hub height for the three cases are also calculated. The wind speed mean relative error (MRE), root-mean-square error (RMSE) and mean wind speed at the hub height are shown in Table 8. For SJB, the MREs between the measurements and predicted wind speeds obtained by the PL, WSE-WSR, WSE-WDSD, WSE-RG and WSE-MO methods have the following sequence: MRE(PL) > MRE(WSE-WSR) > MRE(WSE-WDSD) > MRE(WSE-RG) > MRE(WSE-MO). For GFC, MRE(PL) > MRE(WSE-WSR) > MRE(WSE-WDSD). Besides, for HNH, it is MRE(PL) > MRE(WSE-WSR) > MRE(WSE-WDSD). The improvements of MRE range from 1.58 to 0.22%, 0.33 to 0.02%, 7.38 to 3.17% for the three cases using the new wind speed extrapolation methods. It is proposed that the new WSE method based on the atmospheric stability better reflects the true changes of wind speed in two dimensions: height and time. It takes the effect of atmospheric stability on the wind profile into account and makes separate calculations for the wind resources at different conditions of atmospheric stability, which can reflect the mixing of atmosphere in the vertical direction. On this basis, the accuracy of atmospheric stability classification will directly affect the accuracy of wind speed estimation. When the two-level temperature data sets are available, it can be seen from Table 9 that the WSE-RG and WSE-MO methods can better estimate the wind speed for the SJB plateau area. For the GFC mountainous area without temperature measurements, both the WSE-WDSD and WSE-WSR methods can better estimate the wind speeds and the WSE-WDSD method is more accurate than the WSE-WSR method. For the HNH plain area, where the underlying surface is farmland, both the WSE-WDSD method and WSE-WSR method can better estimate the wind speeds than the PL method. Figure 4: Probability of atmospheric stability classification at SJB: (**a**) monthly variation; (**b**) daily variation. Figure 5 shows MRE and RMSE between the measured and calculated wind speeds in different atmospheric stability conditions. For SJB, MREs and RMSEs of WSE-WDSD under different atmospheric stability conditions are close to each other and are similar to the results of PL. It can be concluded that the WDSD method cannot be applied to the complex terrain of plateau to classify the atmospheric stability. In contrast, combining the results of MREs and RMSEs, it is found that the accuracy of the WSE-RG method is improved in the cases of A, B and C, and the WSE-WSR method is suitable for the cases of C, D and E, while the WSE-MO method improves the calculation accuracy in all the conditions. For GFC, both MREs and RMSEs of the WSE-WSR method are large when the