Assessment of the Extreme Precipitation by Satellite Estimates over South America

Abstract

<jats:p>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.</jats:p>

Evaluation of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Measurement (IMERG) Product in the São Francisco Basin (Brazil)

“…Estimates of the parameters (β 0 and β 1 ) of the linear regression model, the determination coefficient (R 2 ), correlation coefficient (r), the corresponding p-value. It stands out that the greatest accumulation of precipitation is concentrated between the months of October and April, a period during the rainy season in much of the region, consistent with previous analyses obtained from rain gauge and satellite data [5,6,51]. A possible explanation for this underestimation of precipitation in the NEB east coast is the type of weather systems which usually affect this region.…”

Objective Evaluation of Satellite Precipitation Datasets for Heavy Precipitation Events Caused by Typhoons in the Philippines

“…This result is consistent with those of other studies, one of which confirmed that the IMERG and GSMaP datasets overestimate the frequency of light to moderate rainfall events (1-10 mm) and underestimate the frequency of extreme rainfall events (> 0 mm) (Liu et al 2020a). Another study revealed that the GSMaP and PERSIANN datasets underestimate the frequency of extreme rainfall (75-100 mm/day) (Palharini et al 2020). Fang et al (2019) discovered that the IMERG dataset underestimates extreme precipitation.…”

“…The calibrated rain gauge of the TRMM, GSMaP, CMORPH, CHIRPS, and PERSIANN datasets outperformed compare to the near-real-time of each datasets in terms of estimating heavy rainfall in South America from 2012 to 2016 (Palharini et al 2020). The accuracy of the CMORPH dataset was higher than that of the PERSIANN dataset in terms of radar precipitation observations of heavy rainfall during seven major flood events in regions of various terrains in northern Italy and southern France between 2003 and 2008 (Stampoulis et al 2013).…”

“…Revisions in the algorithms of SPDs had been made based on key studies which improved the capability of SPDs in detecting heavy rainfall. Studies have evaluated the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset during an extreme rainfall event (Pombo and de Oliveira 2015;Parida et al 2017;Huang et al 2018;Rashid et al 2018;Liu et al 2019;Palharini et al 2020). Pombo and de Oliveira (2015) evaluated the performance of the TRMM 3B42 dataset in estimating the annual maximum daily rainfall in Angola.…”

An Approach to Reliability Characterization of GSMaP Near-Real-Time Precipitation Product

“…Many previous studies have validated the performance of the GSMaP in various regions and over different periods by comparing its results with ground-based precipitation data from gauges and radars (e.g., Kubota et al 2009Ngo-duc et al 2013;Prakash et al 2016;Beck et al 2017;Nodzu et al 2019;Trinh-Tuan et al 2019;Palharini et al 2020). The GSMaP algorithm mainly consists of an algorithm for retrieving precipitation from PMW sensors (the PMW algorithm) and an algorithm for estimating precipitation from both PMW sensors and IR radiometer data (the PMW-IR combined algorithm).…”