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New strategies for retrieving profiles from GNSS radio occultation data considering the Brazilian atmosphere

Grant number: 21/05285-0
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): November 01, 2021
Effective date (End): October 31, 2023
Field of knowledge:Physical Sciences and Mathematics - Geosciences - Geodesy
Principal researcher:Daniele Barroca Marra Alves
Grantee:Gabriel Oliveira Jerez
Home Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil


GNSS (Global Navigation Satellite Systems) radio occultation (RO) enables investigations of the Earth's atmosphere. The signals propagated from GNSS satellites to the receivers onboard LEO (Low Earth Orbiting) satellites are influenced by the atmosphere in different ways. From the collected signal it is possible to calculate the refractive index and, with that, retrieve profiles of temperature, pressure, humidity and electron density. Because of its homogeneous distribution, the profiles obtained with RO are especially important when considering scenarios such as the Brazilian atmosphere. Due to its location, in a region of intense ionospheric activity and continental dimensions with the presence of the Amazon forest, which causes significant regional and seasonal variations, Brazil has a complex and dynamic atmosphere. However, in the methods related to the retrieval of atmospheric profiles from RO, some generalizations and specific configuration strategies are used. In general, the strategies adopted consider global characteristics, but aiming improvements in specific scenarios, the use of regional strategies could lead to more realistic results. In this context, the present work aims to investigate and develop methods and strategies for retrieving atmospheric profiles from RO, considering the specificities of the Brazilian atmosphere. Regarding the neutral atmosphere, another intended contribution is the development of a strategy for processing atmospheric profiles that contributes to the identification of extreme events, such as precipitation. From the products obtained with this research, ionospheric models and numerical weather prediction models, which use RO data, may benefit from the inclusion of more accurate profiles. (AU)

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