Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade

Abstract

Abstract In contrast to previous studies validating numerical weather prediction (NWP) models using observations from the global positioning system (GPS), this paper focuses on the validation of seasonal and interannual variations in the water vapor. The main advantage of the performed validation is the independence of the GPS water vapor estimates compared to studies using water vapor datasets from radiosondes or satellite microwave radiometers that are already assimilated into the NWP models. Tropospheric parameters from a GPS reanalysis carried out in a common project of the Technical Universities in Munich and Dresden were converted into precipitable water (PW) using surface pressure observations from the WMO and mean atmospheric temperature data from ECMWF. PW time series were generated for 141 globally distributed GPS sites covering the time period from the beginning of 1994 to the end of 2004. The GPS-derived PW time series were carefully examined for their homogeneity. The validation of the NWP model from NCEP shows that the differences between the modeled and observed PW values are time dependent. In addition to establishing a long-term mean, this study also validates the seasonal cycle and interannual variations in the PW. Over Europe and large parts of North America the seasonal cycle and the interannual variations in the PW from GPS and NCEP agree very well. The results reveal a submillimeter accuracy of the GPS-derived PW anomalies. In the regions mentioned above, NCEP provides a highly accurate database for studies of long-term changes in the atmospheric water vapor. However, in the Southern Hemisphere large differences in the seasonal signals and in the PW anomalies were found between GPS and NCEP. The seasonal signal of the PW is underestimated by NCEP in the tropics and in Antarctica by up to 40% and 25%, respectively. Climate change studies based on water vapor data from NCEP should consider the large uncertainties in the analysis when interpreting these data, especially in the tropics.