Abstract
In developing countries, accurate rainfall estimation with adequate spatial distribution is limited due to sparse rain gauge networks. One way to solve this problem is the use of satellite-based precipitation products. These satellite products have significant spatial coverage of rainfall estimates and it is of fundamental importance to investigate their performance across space–time scales and the factors that affect their uncertainties. In the open literature, some studies have already analyzed the ability of satellite-based rain estimation products to estimate average rainfall values. These investigations have found very close agreement between the estimates and observed data. However, further evaluation of the satellite precipitation products is necessary to improve their reliability to estimate extreme values. In this scenario, the main goal of this work is to evaluate the ability of satellite-based precipitation products to capture the characteristics of extreme precipitation over the tropical region of South America. The products evaluated in this investigation were 3B42 RT v7.0, 3B42 RT v7.0 uncalibrated, CMORPH V1.0 RAW, CMORPH V1.0 CRT, GSMAP-NRT-no gauge v6.0, GSMAP-NRT- gauge v6.0, CHIRP V2.0, CHIRPS V2.0, PERSIANN CDR v1 r1, CoSch and TAPEER v1.5 from Frequent Rainfall Observations on GridS (FROGS) database. Some products considered in this investigation are adjusted with rain gauge values and others only with satellite information. In this study, these two sets of products were considered. In addition, gauge-based daily precipitation data, provided by Brazil’s National Institute for Space Research, were used as reference in the analyses. In order to compare gauge-based daily precipitation and satellite-based data for extreme values, statistical techniques were used to evaluate the performance the selected satellite products over the tropical region of South America. According to the results, the threshold for rain to be considered an extreme event in South America presented high variability, ranging from 20 to 150 mm/day, depending on the region and the percentile threshold chosen for analysis. In addition, the results showed that the ability of the satellite estimates to retrieve rainfall extremes depends on the geographical location and large-scale rainfall regimes.
Highlights
Precipitation is one of the most important meteorological variables to investigate in the hydrological cycle context
Extreme precipitation data collected by the satellites are difficult to process, requiring complex algorithms for this task [5]
Since the launch of the first satellite completely focused on precipitation studies (TRMM), the academic community has been evaluating the performance of satellite precipitation estimates obtained by different algorithms [24,55,56,57,58]
Summary
Precipitation is one of the most important meteorological variables to investigate in the hydrological cycle context. The meteorological systems responsible for intense rainfall are complex and evolve rapidly, making measurement difficult. The interest in investigating the impact of extreme events has been growing due their effects on daily life [2,3,4]. In this scenario, different techniques have been developed to monitor precipitation using satellites. Extreme precipitation data collected by the satellites are difficult to process, requiring complex algorithms for this task [5] Improvement of these algorithms for data retrieval is essential to quantify extreme events in terms of frequency and intensity, and to generate useful information for decision-makers
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