Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

Abstract

Knowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the Tropical Rainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB.

Thunderstorm Efficiency Regimes in South America as Observed by STARNET and TRMM

“…Daily TRMM‐3B42 Version 7 rain estimates (3B42) have been chosen because they represent the best available daily and monthly precipitation maps in South America (Michot et al, ), as daily 3B42 rain estimates are rain gauge adjusted (bias removal). For this study, the quarter degree 3B42 spatial resolution has been averaged for an area of 1 × 1° of longitude and latitude.…”

Suitability of TRMM Products with Different Temporal Resolution (3-Hourly, Daily, and Monthly) for Rainfall Erosivity Estimation

“…Moreover, TRMM Multi-satellite Precipitation Analysis (TMPA) combined the data from TRMM-PR, VIRS, TMI with passive microwave, infrared and visible measurements available from national and international satellites, and could provide rainfall data series with the temporal resolution of 3-hourly and spatial resolution of 0.25 • × 0.25 • at the coverage area of global 50 • S to 50 • N [28,29]. Numerous studies have validated that TRMM products have acceptable accuracy [28,29,[33][34][35][36][37][38], and good performance has been achieved using TRMM rainfall data in many research fields, including hydrological modeling [33,[39][40][41][42][43][44][45], rainfall characteristics [35,46,47], weather processes [48,49], latent heat flux [50][51][52], extreme precipitation [53,54], and drought/flood monitoring [55][56][57][58][59][60].…”

The southern Amazon rainy season: The role of deforestation and its interactions with large‐scale mechanisms

“…Michot et al . () have demonstrated that at the daily scale, in most parts of the Amazon Basin, the Tropical Rainfall Measurement Mission (TRMM) 3B42 rainfall estimates provide an exhaustive and quite good precipitation dataset. This enhanced data availability is crucial both to represent the spatial trends in the region and to obtain the relationships that can isolate the effects of deforestation and the interactions between deforestation and large‐scale processes.…”