Can IMERG Precipitation Estimates Suitably Replace TMPA Products For Brazil?

Knowledge of the spatial and temporal distribution of precipitation is of key importance for planning a wide range of socio-economic activities such as agriculture, livestock grazing, energy generation, etc. The availability of accurate and consistent precipitation data is then paramount for the proper assessment of such activities. However, traditional rain gauge measurements are relatively scarce and poorly distributed over the surface of the globe, particularly over remote areas or in developing countries. In the last three decades, satellite-derived precipitation estimate products have been developed using multi-satellites and multisensors. Such products provide an effective way of estimating precipitation data in areas where measurements are scarce, such as deserts [1], forests [2] and oceans [3]. Accordingly, they have been widely used in research and applications worldwide [410]. The first approaches to employ remote sensing techniques for estimating precipitation were performed during the 70s. During that time, satellite images were not digitalized. Barret [11] was one of the pioneers in developing a method for estimating monthly precipitation using the visible channel. This method was called “cloud index”, and was later improved by Follansbee [12], which allowed for estimating the daily precipitation rate. Almost two decades later, at the end of 1997, the Tropical Rainfall Measuring Mission (TRMM) [13] was launched jointly by NASA (National Aeronautics and Space Administration) and JAXA (Japan Aerospace Exploration Agency), aiming to improve precipitation estimates in tropical and subtropical regions. One of the most successful products generated from this mission is the MultiSatellite Precipitation Analysis research version (TMPA) and the real-time version of the same product (TMPA-RT). The TMPA algorithm [4,5,14,15], besides combining precipitation estimates derived from several satellites, is also able to incorporate observed precipitation data [16]. Over the last decade, those retrievals have been improved greatly with different versions of those products. For this study, the latest version of TMPA launched in 2012 (version 7 or TRMM_V7 hereafter) will be used. The TMPA product has been used in scientific research and operational activities which lead to outstanding socio-economic gains, such as studies of extreme precipitation events [17-19], forecasting of natural disasters [20,21], water resources management planning [22], performance of numerical models [23-26], among others. Due to this fact, TMPA validation studies have been performed in several regions of the globe, and those results show great agreement between TMPA products and surface data [27-32]. Nevertheless, particularly over Brazilian territory, systematic bias are still observed for some precipitation regimes associated with shallow convection systems observed near the coast of northeastern Brazil (underestimation of precipitation) [33,34], while in Southern Brazil (close to the border with Argentina and Volume 2 Issue 3 2018