Assessment of extreme rainfall estimates from satellite-based: Regional analysis

Palharini, Rayana Santos Araujo; Vila, Daniel Alejandro; Rodrigues, Daniele Torres; Palharini, Rodrigo Cassinelli; Mattos, Enrique Vieira; Pedra, George Ulguim

Abstract

Excessive rain may cause several problems for society. Understanding the behaviour of extreme rainfall and quantifying it in an assertive manner is important for whole society. The purpose of this work is to evaluate the ability of satellite precipitation products to detect the extreme rainfall over different regions of Brazil. The products evaluated in this investigation were from Frequent Rainfall Observations on GridS (FROGS) database. The results show that, each region of Brazil is characterized by extremes of rain with different intensities. The regions that presented the highest values are south and north regions of Brazil with values around 125.0 mm/ day. In both regions, the GSMAP product (with rain gauges adjustments) have better performance, as shown in the metrics for the south and north regions where bias = -1.20 mm/day and -6.49 mm/day; r = 0.65 and 0.50; std = 10.15 mm/day and 10.63 mm/day; rmse = 9.58 mm/day and 13.16 mm/day respectively. On the other hand, the regions with the lowest intensities are the northeastern region, inland and coast, presented frequent extreme values of approximately 35.0 mm/day. At these regions, both versions of product 3B42RT v7.0 demonstrated a better performance, as demonstrated in the metrics for inland and coast northeastern regions, bias = 2.82 mm/day and -2.94 mm/day; r = 0.18 and 0.30; std = 8.53 mm/day and 6.97 mm/day; rmse = 14.75 mm/day and 7.03 mm/day, respectively. It is worth mentioning that the precipitation values found in this work do not necessarily cause disasters or generate impacts in the analyzed regions, they were considered extreme from a statistical point of view, considering the analyzed database.

Más información

Título según WOS: Assessment of extreme rainfall estimates from satellite-based: Regional analysis
Título de la Revista: REMOTE SENSING APPLICATIONS: SOCIETY AND ENVIRONMENT
Volumen: 23
Editorial: Elsevier
Fecha de publicación: 2021
DOI:

10.1016/j.rsase.2021.100603

Notas: ISI