PWV-GNSS JUMP as a tool for nowcasting in Brazil: an overview, the challenges, and opportunities

Ground-based GNSS (Global Navigation Satellite System) receivers have been used to estimate precipitable water vapor (PWV) with high temporal resolution. The quality in terms of precision and confidence has given the opportunity to explore this feature to predict the occurrence of thunderstorms. A sharp increase in the GNSS-PWV time series before the intense precipitation events has been found, which indicates the occurrence of this phenomenon and consequently demonstrates a good potential for application in nowcasting activities. This increasing pattern in the PWV-GNSS time series before strong precipitation has been termed GPS-PWV-jumps and occurs because of the water vapor convergence and the continued formation of cloud condensate and precipitation particles. This study presents an overview of the development of this technique in Brazil, presenting a summary of the latest results using the data collected in different campaigns in the last years over different regions of Brazilian territory. GNSS receivers and several instruments to observe the precipitation, such as disdrometers and X-band radar, were used. The long database has been explored, and extensive analyses of results were carried out using wavelet cross-correlation analysis, lag correlation method, and contingency table after defining a method to predict the precipitation using GNSS-PWV jump information. This approach is innovative because it uses only GNSS data and, consequently, the infrastructure used by geodesic applications, such as GNSS receiver networks present in big cities, can be explored for this purpose without additional investments. However, there are some challenges that need to be addressed yet, such as the PWV-GNSS-jump production in near real time, which involves the data reception and data processing in a suitable time to be evaluated and applied to the issuance of disaster warnings. Another challenge, just as important as the first, is ensuring that the performance of the GNNS-PWV jump is maintained when using near-real-time estimates. These challenges are treated in this work as an opportunity for researchers exploring artificial intelligence methods, which are discussed, and some possible strategies are presented. The future perspective of the GNSS receiver application as a humidity information data source used in the evaluation and data assimilation process in the community development of the MONAN (Model for Ocean-laNd-Atmosphere predictioN) model is also discussed.