GNSS/MET Water Vapor Data: Quality Control Method and Comparative Analysis of Reanalysis Datasets

This study proposes a comprehensive quality control (CQC) algorithm for the Chinese ground-based navigation satellite system (GNSS) water-vapor products. The CQC algorithm consists of two sections—Quality check and comprehensive decision-making algorithms. The quality check algorithm consists of seven parts, namely, limit check to eliminate errors that exceed reasonable limits, buddy and low-pass filter checks for better time consistency, neighboring station check, anomaly and peak–valley value checks for better spatial consistency, and background check to identify the data that deviate from the background field for assimilation application. After each check, the data that exceed the threshold are identified, and the comprehensive decision-making algorithm is used to score the identified data and flag (correct, suspicious, or error) the data. Based on the quality-controlled observation data, the precipitable water-vapor simulation of five sets of reanalysis data, including China’s first-generation global atmosphere reanalysis (CRA) product, was evaluated. The results show that the simulated total water vapor of all the reanalysis data in winter is slightly higher than the observation in winter and significantly lower than the observation in summer. Spatially, the simulated water vapor content in southern and western China is lower than the observation, and this situation is more obvious in the summer half of the year. Relative to the observation, the average bias (O−B) of CRA is −0.633 mm, and the root–mean–square error is 3.650 mm. The deviation of CRA relative to the observation is slightly lower than ERA-Interim but slightly higher than ERA5, which is significantly better than JRA55 and NCEP2.