Evaluating the estimation of GNSS-based PWV in Angola, Mozambique, and Nigeria

For accurate weather predictions and analysis of extreme events, a good estimate of the amount of water content in the atmosphere is essential. This information is provided by several techniques like radio sondes that measure this parameter at various heights. However, most of them are very limited spatially and temporarily or suffer from measurement specific constraints. To complement these techniques, Precipitable Water Vapor (PWV) can be measured by GNSS (Global Navigation Satellite System) CORS (Continuously Operating Reference Stations) networks. PWV can be derived from the delay in the GNSS signal when it passes through the troposphere, when the temperature and pressure are also known at the station location. In the framework of SUGGEST-AFRICA, we are finalizing the implementation of a system to use the national GNSS stations for the automatic computation of PWV in Angola, Mozambique, and Nigeria based on the national GNSS networks of these countries. SUGGEST-AFRICA also funded the installation of 15 weather stations to obtain the pressure and temperature, collocated with five stations in which country. When there are no nearby meteorological stations, we will use values from global/regional models. In this presentation, we describe the implemented system to obtain the PWV from GNSS+T+P. Methodologies have been optimized to passive and actively access the GNSS data; the PWV estimations are computed using PPP (Precise Point Positioning), which permits the estimation of each individual station separately; solutions have been validated using internal and external values; and computed solutions are being transferred timely to the national meteorological institutes of each country. Finally, we also present first results for the variation of PWV in Angola, Mozambique, and Nigeria over time based on the analysis of the historical processed data. This study is supported by SUGGEST-AFRICA, funded by Fundacao Aga Khan and FCT. It uses computational resources provided by C4G Collaboratory for Geosciences (PINFRA/22151/2016). It is also supported by project FCT/UIDB/50019/2020 IDL funded by FCT.