Retrieval of Atmospheric Water Vapor Profiles From COSMIC-2 Radio Occultation Constellation Using Machine Learning

This study uses data from January 2020 to July 2022 to develop an artificial neural network (ANN)-based model to retrieve atmospheric water vapor (Wv) pressure profiles from Constellation Observing System for Meteorology, Ionosphere (COSMIC-2) observations over the Indian region. Prior to using the COSMIC-2 (C2) refractivity, we evaluated the C2 refractivity’s quality by comparing it with radiosonde (RS) and European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis v5 (ERA5) data. The average and standard deviation of the differences between the refractivity of C2 and that of ERA5 and RSs are, respectively, within ±0.5% and less than 2% in the middle and upper troposphere, but there are larger (3%–5%) differences in the lower troposphere. The C2 refractivity, latitude, longitude, height, month, and the knowledge of whether a specific pixel is over land or water are all inputs to the ANN model, and its output consists Wv. The operationally retrieved 1-D variational (1D-Var)-based Wv from University Corporation for Atmospheric Research (UCAR) and National Environmental Satellite, Data, and Information Service (NESDIS) as well as the retrieved Wv using the proposed ANN model are compared with ERA5 and RS. Overall, the proposed model outperforms NESDIS-retrieved Wv, while the Wv of the proposed models has less error than those of UCAR in the lower atmosphere and perform comparable to UCAR in the middle and upper troposphere. Given that the proposed model can retrieve the Wv solely from C2 data and does not require any external meteorological data, the results are quite promising.