## 3 Results
### Impact of Vertical Wind Shear on Clouds as Interpreted from Model Simulation  
#### 3.2.1 Model Evaluation  
Before analyzing the simulated results, the modeled clouds and precipitation need to be evaluated. Figure 10 shows the map of composite reflectivity in the inner domain modeled by WRF and the time-height diagram of the modeled reflectivity. The distribution of composite reflectivity can be evaluated using Figure 2a, though this is not a direct comparison. It can be seen that WRF can capture the general distribution of orographic cloud and precipitation well compared to satellite images (Figure 10a). Most of the Tian Shan Mountains were covered by orographic clouds in this case, and precipitation was detected over both slopes. The maximum modeled radar reflectivity was found to be about 35 dBZ, and the modeled echoes with a high reflectivity revealed that convection was occurring over the mountain. Note that we are not focusing on large-scale precipitation in this paper, but rather the consistency between the modeled and observed cloud distributions is the basis for our analysis of the detailed cloud evolution using model outputs. To evaluate the cloud evolution more directly, we plot the modeled radar reflectivity in Figure 10b, which can be compared to the MRR reflectivity shown in Figure 4a. As seen from the figure, WRF captured the temporal evolution of the clouds well. Precipitation started at approximately 9:00 UTC, and there were three segments of precipitation. However, due to the relatively low resolution, the fine structures of the clouds cannot be represented. The precipitation modeled using WRF is evaluated in Figure 11. In the central Tian Shan area, the accumulated precipitation from the WRF model (Figure 11b) was consistent  
Figure 10: **(a)** Map of composite reflectivity at 9:00 UTC modeled by WRF; **(b)** Time–height diagram of the modeled reflectivity at the observational site. The two black lines indicate the cross-sections, which will be analyzed in Section 3.2.2.  
with that measured by the rain gauges (Figure 11a). The correlation coefficient between the model and the observation was greater than 0.7. In addition, WRF showed that relatively more precipitation was received over the ridges and less at the foot of the mountains. Near the observational site, the amount of accumulated precipitation was 3-7 mm in this case. The average observed precipitation rate of the three gauges on the north slope of the central Tian Shan area varied between 0 and 1 mm h\({}^{-1}\) (Figure 11c). The precipitation rate modeled at these three stations was slightly larger than the observed rate, and the precipitation started slightly earlier. However, the variations in the precipitation rate seen between the model and observation were consistent, with a correlation coefficient of 0.907. Sensitivity tests conducted using several other configurations of WRF provided worse results for the modeled cloud and precipitation (not shown). In general, WRF captured the characteristics of the cloud evolution and precipitation reasonably well. However, there were some differences between the modeled and observed precipitation structures for multiple reasons. Firstly, the model resolution was 1 km, much larger than the resolution of MRR, and the vertical structure of the precipitation was not as fine as that in the MRR observation. In addition, a 1 km resolution cannot fully reveal the terrain complexity, which may have an important impact on the small-scale structure of orographic precipitation. Moreover, the reflectivity modeled from the WRF output was for a radar wavelength of 10 cm, which was different than the MRR wavelength; thus, the magnitudes of reflectivity were expected to differ between the model and the observation. Finally, there were many sources of uncertainty in modeling orographic clouds using the WRF, especially in the boundary layer and the microphysics parameterizations [5].