Validation of TRMM data in the geographical regions of Brazil

Abstract

The low density of precipitation gauges, the areas of difficult access and the high number of missing values hinder a rapid and effective hydrological monitoring. Thus, the present study aims to statistically validate the precipitation estimates by the data Tropical Rainfall Measuring Mission (TRMM) in relation to the data observed in the Conventional Meteorological Stations (CMSs) in the geographic regions of Brazil. The statistical indicators used were: Correlation Coefficient (r), Mean Absolute Error (MAE), Percentage of Bias (Pbias), T-Test and Mann-Witiney Test. It is concluded that the precipitation data estimated via TRMM are effective and reliable alternatives for hydrological studies in areas that do not have in-situ gauges and/or need to fill missing values in the five regions of the country, especially in the driest months and in larger time scales.

Analysis of the IMERG-GPM Precipitation Product Analysis in Brazilian Midwestern Basins Considering Different Time and Spatial Scales

“…In fact, in many situations, the spatial distribution of rainfall was not properly described by the IMERG satellite retrievals, with a tendency of generating smoother surfaces as compared to the data captured by ground-based information. Nonetheless, as may be inferred from Melo et al, 2015, and Moraes and Gonçalves, 2021 [29,35], the IMERG-GPM may be a preferable alternative for our study region after spatial averaging, as it more properly described the rainfall amounts on a daily time scale. Hence, despite the inaccurate descriptions of daily rainfall extremes, which are not considerably improved under bias correction [6], the short size of our sample, and the relatively poor representation of spatial patterns, we still believe that the IMERG-GPM product may be a useful data source for the Brazilian midwestern region, as compared to well-established alternatives such as TRMM, mainly for continuous rainfall-runoff simulation based on a daily time step, and drought management, which requires data at monthly or longer time scales.…”

“…The area presents marked seasonal features that are known to affect the performance of satellite precipitation products [6]. Previous research on this study region [29,35] has suggested that the TRMM products, which are frequently used as data sources in tropical areas, may present significant biases during the wet season, which in turn provide some justification for performing similar evaluations with the IMERG-GPM counterpart. The remainder of this paper is organized as follows: Section 2 presents the material and methods, with a brief description of the study area and the data, as well as the methods utilized for data quality checking for interpolating the ground-based rainfall amounts and for performance assessment.…”

“…Moreover, retrieval errors may strongly depend on the aggregation time scales; these errors are usually larger for shorter time scales (e.g., hourly or daily) and considerably smaller for longer ones (e.g., monthly or annual) [19]. These facts have prompted a plethora of studies that assessed the performances of distinct retrieval algorithms in different parts of the world by comparing satellite estimates with ground-based measurements (which are, more often than not, erroneously assumed to be error-free), as well as studies that developed mathematical models for bias correction [6,[28][29][30][31][32][33][34][35].…”

“…In order to conduct this evaluation, we compare the satellite retrievals with information derived from the rainfall gauging network operated by the Brazilian Agency of Water and Sanitation (Agência Nacional de Águas e Saneamento Básico, ANA), after first performing spatialization to match the satellite resolution for the Meia Ponte and the Bois River catchments. The Brazilian midwestern region is a relatively poorly gauged area [29,35], with a complex climate that is influenced by a variety of atmospheric systems [38]. The area presents marked seasonal features that are known to affect the performance of satellite precipitation products [6].…”