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Rainfall mapping from cellular commercial microwave links: parameters calibration and uncertainty analysis in subtropical climate

Grant number: 17/09708-7
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): February 01, 2018
Effective date (End): September 30, 2018
Field of knowledge:Agronomical Sciences - Agricultural Engineering
Principal Investigator:Marcos Vinícius Folegatti
Grantee:Wagner Wolff
Supervisor abroad: Remko Uijlenhoet
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Local de pesquisa : Wageningen University, Netherlands  
Associated to the scholarship:16/15342-2 - Lysimetric calibration and uncertainty analysis of empirical parameters of the SEBAL algorithm in subtropical climate, BP.PD

Abstract

Understanding the spatio-temporal variation of rainfall is important to help the water management. In this context, the rainfall retrieval algorithm (RRA) from microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and may complement rainfall estimates and mapping from ground-based weather radars, rain gauges, and satellites. However, the calibration of the RRA parameters aim at finding a unique optimal parameter set, which is inconsistent with the fact that multiple similar or equivalent solutions may exist due to uncertainties in to model structure, input data, and parameters. These uncertainties are increased in regions where the seasons are well-defined, as in subtropical climates. Thus, the aim of this work is going to be to calibrate the RRA parameters according to its uncertainty in subtropical climate. The work is going to be carried out in São Paulo state, Brazil, which has a large covering of commercial cellular communication network. The microwave links and rainfall data from rain gauges are make available by the telecommunication national agency (ANATEL) and water national agency (ANA), respectively. The rainfall data is going to be used as the standard measure to calibrate the RRA parameters. Thus, the calibration is going to be done using the maximum likelihood method for residuals adjusted to Gaussian distribution. The stochastic optimization method Particle Swarm Optimization is going to be used to the numerical maximization of log-likelihood function. The new parameters empirical are going to be obtained in uncertainty levels and are going to be used to compose the update of RRA to rainfall spatial interpolation in subtropical climate. (AU)