Improvement of GNSS-PWV Estimation in the Occurrence of Precipitation Events

• Grant number: 23/14739-0
• Support Opportunities: Scholarships in Brazil - Master
• Start date: April 01, 2024
• End date: March 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geodesy
• Principal Investigator: Daniele Barroca Marra Alves
• Grantee: Afonso Marques Albuquerque
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
• Associated scholarship(s): 25/07321-4 - Estimation of neutral atmosphere parameters from GNSS positioning signals: Modeling and analysis using meteorological in situ and radiosonde data, BE.EP.MS

Abstract

Global Navigation Satellite Systems (GNSS) allow determining the position of an object onthe Earth's surface and, in recent decades, its observations have also been used successfullyin studies of the atmosphere. From the delay that the GNSS signal undergoes when goingthrough a neutral atmosphere, the integrated amount of water vapor (PWV - PrecipitableWater Vapor) can be determined with a high temporal resolution (a few minutes). Strongfluctuations in the time series of PWV estimates obtained from GNSS observations, calledGNSS-PWV-jump, can be used to indicate the occurrence of extreme precipitation events.Despite the promising results obtained in very short-term forecasting applications(nowcasting), different improvements in the processing of GNSS data must be investigated.Data from other satellite constellations besides the GPS system (Global Positioning System)can also be explored, as well as adjustments in stochastic modeling related to the treatmentof the influence of the neutral atmosphere in the signal propagation. Thus, in this research,the main objective is to investigate a better GNSS data processing strategy that generatesGNSS-PWV estimates that are more sensitive to the occurrence of precipitation events.Consequently, it is sought to obtain a better performance of these estimates tool for lessintense events.