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.

This study aims to analyze the climatological classification of precipitating clouds in the Northeast of Brazil using the radar on board the Tropical Rainfall Measuring Mission (TRMM) satellite. Thus, for this research a time series of 15 years of satellite data (period 1998–2012) was analyzed in order to identify what types of clouds produce precipitation estimated by Precipitation Radar (PR) and how often these clouds occur. From the results of this work it was possible to estimate the average relative frequency of each type of cloud present in weather systems that influence the Northeast of Brazil. In general, the stratiform clouds and shallow convective clouds are the most frequent in this region, but the associated rainfall is not as abundant as precipitation caused by deep convective clouds. It is also seen that a strong signal of shallow convective clouds modulates rainfall over the coastal areas of Northeast of Brazil and adjacent ocean. In this scenario, the main objective of this study is to contribute to a better understanding of the patterns of cloud types associated with precipitation and building a climatological analysis from the classification of clouds.

The number of natural disasters triggered by extreme events is increasing worldwide and significantly impacts modern society. Extreme rainfall is one of the most important factors contributing to these events. A better understanding of the physical process that causes extreme rainfall can allow rapid responses from decision-makers to lessen the impact of natural disasters on the local population. Satellite monitoring is widely used for this purpose and is essential for regions where terrestrial observations are limited or non-existent. The primary purpose of this study is to describe the performance of satellite products for extreme rainfall events that caused natural disasters in various climate regimes in Brazil and discuss the contribution of mesoscale convective systems (MCS) to these events. We defined regions based on the climatological rainfall distribution. Cases with rain values above the 99th percentile during 2012–2016 were considered statistically extreme. Our analysis is based on three datasets, with precipitation from (i) rain gauge stations, (ii) different satellite-based estimates, and (iii) mesoscale convective tracking data. The methodology was based on identifying extreme rainfall events, analyzing the performance of satellite precipitation estimates and, finally, quantifying the influence of convective systems on extreme rain. Although all regions of Brazil may be affected by natural disasters caused by extreme rains, the results suggest that the impacts caused in each region are different in magnitude. Convective systems explained over 90% of extreme rains in the case analyzed in Brazil’s south and about 60% to 90% of extreme rains in the case analyzed in the Northeast. In general, satellite products have identified rain events; however, in the southern region of Brazil, products have tended to overestimate rainfall, while other regions have tended to underestimate extreme rain values. The methods used in satellite precipitation estimation products have limitations to accurately identifying specific extreme rain events.

The São Francisco River basin is one of the largest in the Brazilian territory. This basin has enormous economic, social and cultural importance for the country. Its water is used for human and animal supply, irrigation and energy production. This basin is located in an area with different climatic characteristics (humid and semiarid) and studies related to precipitation are very important in this region. In this scenario, the objective of this investigation is to present an assessment of rainfall estimated through the Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) product compared with rain gauges over the São Francisco river basin in Brazil. For that, a period from of 20 years and 18 surface weather stations were used to evaluate the product. Based on different evaluation techniques, the study found that the IMERG is appropriate to represent precipitation over the basin. According to the results, the performance of the IMERG product depends on the location where the rain occurs. The bias ranged from −1.67 to 0.34 mm, the RMSE ranged from 5.36 to 10.36 mm and the values of the correlation coefficients between the daily data from the IMERG and rain gauge ranged from 0.28 to 0.61. The results obtained by Student t-test, density curves and regression analysis, in general, show that the IMERG is able to satisfactorily represent rain gauge data. The exception is the eastern portion of the basin, where the product, on average, underestimates the precipitation (p-value < 0.05) and presents the worst statistical metrics.

The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle.