Evaluation of FY-3E, CRA, and ERA5 Temperature and Humidity Profiles over North China in Summer

Based on summer ground-based microwave radiometer (MWR) observations over North China, this study systematically evaluates the accuracy of temperature and humidity profile products derived from the Vertical Atmospheric Sounding System (VASS) onboard the FY-3E satellite. The VASS products are compared with the numerical weather prediction (NWP) background fields as well as the ERA5 and CMA-RA 1.5 (CRA) reanalysis datasets. The results show that both ERA5 and CRA exhibit stable and reliable performance in representing temperature and humidity fields under both clear and cloudy conditions over North China. The temperature root mean square error (RMSE) generally ranges from 1.6 K to 2.6 K at different height levels (from 0 km to 10 km), while the RMSE of absolute humidity is approximately 0.4–2.7 g/m3. These results further confirm the reliability of CRA under both clear and cloudy conditions in this region. In contrast, the errors of the VASS products show pronounced variations with height, station, and weather conditions. A clear systematic underestimation of temperature is found at 1–3 km, with a mean bias of about −3.44 K. Humidity is also significantly underestimated in the boundary layer, with a mean bias of approximately −5.91 g/m3. Both temperature and humidity errors decrease rapidly with increasing height. Clear inter-station differences are also identified. Temperature errors show boundary-layer overestimation in Beijing, while Xingtai and Dingzhou exhibit systematic underestimation throughout most of the profile, with mean biases reaching −4.1 K and −3.3 K, respectively. Boundary-layer humidity underestimation is more pronounced in Xingtai and Dingzhou (approximately −6.6 g/m3) than in Beijing (−4.0 g/m3). Weather-based analysis indicates that clouds have a significant impact on the accuracy of the VASS products. Under cloudy conditions, the near-surface temperature mean bias shifts from overestimation under clear skies to underestimation. The magnitude of humidity underestimation under cloudy conditions is approximately twice that under clear conditions. Further comparison shows that the error characteristics of the NWP background fields in the lower and middle troposphere are partly similar to those of the VASS products. This suggests that the current retrieval algorithm still has limited capability to correct background field biases under complex weather conditions. These results provide scientific support for the selection of application scenarios and the optimization of retrieval algorithms for FY-3E/VASS temperature and humidity profile products, and they also support the reliable use of domestic reanalysis datasets in regional studies.