Observing atmospheric rivers using GNSS radio occultation data

Abstract. Atmospheric Rivers (AR) are comparatively narrow regions in the atmosphere that are responsible for most of the horizontal transport of water vapor in the extra tropics, which are responsible for many extreme precipitation events and floodings at mid-latitudes, including Europe and the US. The critical role of ARs in global moisture transport and precipitation dynamics necessitates accurate water vapor measurements for both understanding and forecasting these phenomena. While the integrated water vapor content (IWV) of ARs can be well measured with microwave and infrared sounders, the vertical structure is less well known. In this study, we analysed if specific humidity profiles and IWV values from Global Navigation Satellite System Radio Occultation (GNSS-RO) measurements provide additional information for the study of ARs, in particular regarding their vertical structure. The retrieval of water vapor from GNSS-RO data requires background information, which is usually incorporated by the one-dimensional variational method (1D-Var) that combine observations and background in an optimal manner. We compared data from the COSMIC Data Analysis and Archive Centre (CDAAC), operated by the University Corporation for Atmospheric Research (UCAR) in Boulder, Colorado with data from the Wegener Center for Climate and Global Change (WEGC) at the University of Graz, Austria. We found that retrievals from both centres agree very well in the altitude range, where the 1D-Var weights the observations strongly, even if the employed background profiles are very different. This demonstrates that GNSS-RO data provide indeed additional vertically-resolved information, which was not already contained in the background or in operational analyses. IWV values from CDAAC and WEGC agree generally very well, however, both tend to underestimate the values obtained by Special Sensor Microwave Imager/Sounder (SSMI/S) data, since GNSS-RO profiles not always reach the lowermost part of the atmosphere, leading to a systematic bias in the IWV data, which decreases with better penetration characteristics of the GNSS-RO data. The results suggest that is promising to combine the GNSS-RO data – with very high vertical resolution with SSMI/S data – with high horizontal resolution to get a more compete view of the 3D structure of ARs.