Evaluation of surface temperature and pressure derived from MERRA-2 and ERA5 reanalysis datasets and their applications in hourly GNSS precipitable water vapor retrieval over China

Temperature and pressure play key roles in Global Navigation Satellite System (GNSS) precipitable water vapor (PWV) retrieval. The National Aeronautics and Space Administration (NASA) and European Center for Medium-Range Weather Forecasts (ECMWF) have released their latest reanalysis product: the modern-era retrospective analysis for research and applications, version 2 (MERRA-2) and the fifth-generation ECMWF reanalysis (ERA5), respectively. Based on the reanalysis data, we evaluate and analyze the accuracy of the surface temperature and pressure products in China using the the measured temperature and pressure data from 609 ground meteorological stations in 2017 as reference values. Then the accuracy of the two datasets and their performances in estimating GNSS PWV are analyzed. The PWV derived from the pressure and temperature products of ERA5 and MERRA-2 has high accuracy. The annual average biases of pressure and temperature for ERA5 are −0.07 hPa and 0.45 K, with the root mean square error (RMSE) of 0.95 hPa and 2.04 K, respectively. The annual average biases of pressure and temperature for MERRA-2 are −0.01 hPa and 0.38 K, with the RMSE of 1.08 hPa and 2.66 K, respectively. The accuracy of ERA5 is slightly higher than that of MERRA-2. The two reanalysis data show negative biases in most regions of China, with the highest to lowest accuracy in the following order: the south, north, northwest, and Tibet Plateau. Comparing the GNSS PWV calculated using MERRA-2 (GNSS MERRA-2 PWV) and ERA5 (GNSS ERA5 PWV) with the radiosonde-derived PWV from 48 co-located GNSS stations and the measured PWV of the co-location radiosonde stations, it is found that the accuracy of GNSS ERA5 PWV is better than that of GNSS MERRA-2 PWV. These results show the different applicability of surface temperature and pressure products from MERRA-2 and ERA5 data, indicating that both have important applications in meteorological research and GNSS water vapor monitoring in China.