This study provides a comprehensive evaluation of the vertical accuracy of ERA5 reanalysis data for boundary layer height and key meteorological variables, based on high-precision observational data from Baotou, located on the Mongolian Plateau, during the winter (January–March) and summer (July–August) months of 2021. Results indicate that ERA5 exhibits significant biases in horizontal wind speed, with deviations ranging from −5 to 8 m/s at 50 m, primarily driven by sandstorms in winter and convective weather in summer. The most pronounced errors occur below 500 m. Vertical wind speeds are consistently underestimated in both seasons, with biases reaching up to 1 m/s, particularly during active summer convection. ERA5 also struggles to reproduce low-level wind directions accurately. In winter, correlation coefficients range from 0.43 to 0.64 below 200 m and improve to above 0.7 at 500 m. In summer, correlation coefficients are lower, ranging from 0.3 to 0.5 below 200 m, with reduced accuracy at 500 m compared to winter. Temperature deviations increase above 2000 m, with a relative overestimation of 3% at 3000 m. Relative humidity is generally overestimated by 5–20% between 1000 and 2000 m in winter and by 10–30% in summer. For boundary layer heights, ERA5 overestimates daytime mixed-layer heights by up to 2000 m in summer and 500–800 m in winter. In contrast, ERA5 captures nocturnal stable boundary layer heights well during winter. This comprehensive evaluation of the vertical structure accuracy of ERA5 reanalysis data, conducted in a heavily industrialized city on the Mongolian Plateau, offers essential insights for improving meteorological studies and refining climate models in the region. The findings provide valuable reference data for enhancing weather forecasting and supporting climate change research, particularly in complex terrain areas.

The accuracy of ERA5 reanalysis datasets and their applicability in the coastal area of Bohai Bay are crucial for weather forecasting and environmental protection research. However, synthesis evaluation of ERA5 in this region remains lacking. In this study, using a tropospheric wind profile radar (CFL-06L) placed in coastal Huanghua city, the deviations of ERA5 reanalysis data are assessed from the ground to an altitude of 5 km. The results indicate that the wind speed of ERA5 reanalysis data exhibits good consistency from the surface to the tropospheric level of about 5 km, with R² values ranging from 0.5 to 0.85. The lowest mean wind speed error, less than 3 m/s, occurs in the middle layer, while larger errors are observed at the surface and upper layers. Specifically, at 150 m, the R² is as low as 0.5, with numerous outliers around 5000 m. Seasonal analysis shows that the ERA5 wind field performs best in summer and worst in autumn and winter, especially at lower levels affected by circulation systems, high stratus clouds, and aerosols, with errors reaching up to 10 m/s. Further analysis of extreme weather events, such as heavy rain; hot, dry winds; and snowstorms, reveals that the effects of sea-land winds and strong convective systems significantly impact the observation of wind profiles and the assimilation of reanalysis data, particularly under the constrain of boundary layer height. Additionally, we also find that the transition of sea-land breeze is capable of triggering the nighttime low-level jet, thereby downward transporting the aloft ozone to the ground and resulting in an abnormal increase in the surface ozone concentration. The study provides a scientific basis for improving meteorological forecasting, optimizing wind energy resource utilization, and formulating environmental protection policies, highlighting its significant scientific and practical application value.

The updated MODIS Collection 6.1 (C6.1) Dark Target (DT) aerosol optical depth (AOD) is extensively utilized in aerosol-climate studies in China. Nevertheless, the long-term accuracy of this data remains under-evaluated, especially for the forested areas. This study was undertaken to substantiate the accuracy of MODIS Terra (MOD04) and Aqua (MYD04) at 3 km resolution AOD retrievals at six forested sites in China from 2004 to 2022. The results revealed that MODIS C6.1 DT MOD04 and MYD04 datasets display good correlation (R = 0.75), low RMSE (0.20, 0.18), but significant underestimation, with only 53.57% (Terra) and 52.20% (Aqua) of retrievals within expected error (EE). Both the Terra and Aqua struggled in complex terrain (Gongga Mt.) and high aerosol loads (AOD > 1). In northern sites, MOD04 outperformed MYD04 with better correlation and a relatively high number of retrievals percentage within EE. In contrast, MYD04 outperformed MOD04 in central region with better R (0.69 vs. 0.62), and high percentage within EE (68.70% vs. 63.62%). Since both products perform well in the central region, MODIS C6.1 DT products are recommended for this region. In southern sites, MOD04 product performs relatively better than MYD04 with a marginally higher percentage within EE. However, MYD04 shows better correlation, although a higher number of retrievals fall below EE compared to MOD04. Seasonal biases, driven by snow and dust, were pronounced at northern sites during winter and spring. Southern sites faced issues during biomass burning seasons and complex terrain further degraded accuracy. MOD04 demonstrated a marginally superior performance compared to MYD04, yet both failed to achieve the global validation benchmark (66% within). The proposed results highlight critical limitations of current aerosol retrieval algorithms in forest and mountainous landscapes, necessitating methodological refinements to improve satellite-based derived AOD accuracy in ecological sensitive areas.