Retrieving of atmospheric parameters from multi‐GNSS in real time: Validation with water vapor radiometer and numerical weather model

Abstract

AbstractThe multiconstellation Global Navigation Satellite Systems (GNSS) (e.g., GPS, GLObal NAvigation Satellite System (GLONASS), Galileo, and BeiDou) offers great opportunities for real‐time retrieval of atmospheric parameters for supporting numerical weather prediction nowcasting or severe weather event monitoring. In this study, the observations from different GNSS are combined to retrieve atmospheric parameters based on the real‐time precise point positioning technique. The atmospheric parameters, retrieved from multi‐GNSS observations of a 180 day period from about 100 globally distributed stations, including zenith total delay, integrated water vapor, horizontal gradient, and slant total delay (STD), are analyzed and evaluated. The water vapor radiometer data and a numerical weather model, the operational analysis of the European Centre for Medium‐Range Weather Forecasts (ECMWF), are used to independently validate the performance of individual GNSS and also demonstrate the benefits of multiconstellation GNSS for real‐time atmospheric monitoring. Our results show that the GLONASS and BeiDou have the potential capability for real‐time atmospheric parameter retrieval for time‐critical meteorological applications as GPS does, and the combination of multi‐GNSS observations can improve the performance of a single‐system solution in meteorological applications with higher accuracy and robustness. The multi‐GNSS processing greatly increases the number of STDs. The mean and standard deviation of STDs between each GNSS and ECMWF exhibit a good stability as function of the elevation angle, the azimuth angle, and time, in general. An obvious latitude dependence is confirmed by a map of station specific mean and standard deviations. Such real‐time atmospheric products, provided by multi‐GNSS processing with higher accuracy, stronger reliability, and better distribution, might be highly valuable for atmospheric sounding systems, especially for nowcasting of extreme weather.