## 4 Discussion  
The results show that ERA5 data exhibit relatively small biases in horizontal wind speed at lower altitudes (50 m to 1000 m), but at higher altitudes (above 1000 m), significant overestimations occur, particularly during winter, where wind speed errors increase progressively above 500 m. This indicates that ERA5 tends to smooth out local strong convective or turbulent phenomena, making it difficult to accurately capture atmospheric dynamics. Additionally, during the strong convective weather conditions in summer, ERA5 significantly underestimates horizontal wind speeds, which corresponds to the frequent occurrence of strong convective events such as thunderstorms and heavy rain in this region. These weather phenomena hinder ERA5 from fully reproducing the variability in local wind fields, with the most pronounced discrepancies occurring at lower levels between 50 m and 500 m.  
Regarding vertical wind speed, ERA5 reanalysis data consistently underestimate values at all altitudes, especially during the convectively active summer months, where ERA5 fails to capture the local vertical motion induced by convective activity. This underestimation is likely due to the relatively low spatial resolution of ERA5, which cannot resolve fine-scale convective or turbulent phenomena, particularly in regions with complex terrain. In contrast, the Windcube 100S lidar effectively captures the local vertical wind speed variations during strong convective weather, further emphasizing the limitations of ERA5 in reproducing local-scale processes. In the analysis of temperature and humidity, ERA5 data in the lower layers (500 m to 1000 m) align relatively well with the observations, but above 1000 m, it shows significant temperature overestimation, with the magnitude of the overestimation increasing with altitude. This overestimation is particularly pronounced in summer, where temperature deviations above 2000 m are notably significant, possibly due to ERA5's inability to accurately represent the modulation of local thermal processes by the terrain. In terms of humidity, ERA5 tends to overestimate relative humidity during both winter and summer, especially at higher altitudes (1500 m to 3000 m), where the overestimation is more pronounced. The Foehn effect may partly explain this phenomenon, as the interaction of local airflow with the complex terrain exacerbates the biases in ERA5 data. In the comparison of boundary layer heights, ERA5's performance is closely tied to seasonal and weather conditions. During the winter, ERA5 generally overestimates daytime boundary layer heights, while underestimating nocturnal stable boundary layer heights. This discrepancy is linked to the \"dome effect\" observed during polluted weather conditions, where aerosols suppress surface heating, limiting the full development of the thermal boundary layer [45]. In summer, the overestimation of daytime boundary layer heights in ERA5 becomes even more pronounced, with deviations reaching up to 2000 m, largely due to overestimated surface temperatures and heating effects. Furthermore, intense convective weather events, such as the thunderstorm on August 29, led to strong updrafts that transported boundary layer air into the free atmosphere, causing a substantial local increase in boundary layer height, which ERA5 failed to capture. Additional factors, such as vertical transport driven by mountain airflow and radiative forcing, further exacerbate the discrepancies between ERA5 data and observations. In Baotou, a geographically complex industrial region, variations in boundary layer height significantly impact pollutant dispersion and other atmospheric processes, making the inclusion of boundary layer height data in comparative analyses crucial. This study offers valuable reference data for meteorological research and climate modeling in Inner Mongolia's industrial cities, providing essential scientific support for weather forecasting and climate change studies.