Evaluation of TRMM/GPM Blended Daily Products over Brazil

Abstract

The precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (named TMPA and TMPA-RT for the near real-time version) are widely used both in research and in operational forecasting. However, they will be discontinued soon. The products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and The Global Satellite Mapping of Precipitation (GSMaP) are analyzed as potential replacements for TMPA products. The objective of this study is to assess whether the IMERG and/or GSMaP products can properly replace TMPA in several regions with different precipitation regimes within Brazil. The validation study was conducted during the period from 1st of April, 2014 to the 28th of February, 2017 (1065 days), using daily accumulated rain gauge stations over Brazil. Six regions were considered for this study: five according to the precipitation regime, plus another one for the entire Brazilian territory. IMERG-Final, TMPA-V7 and GSMaP-Gauges were the selected versions of those algorithms for this validation study, which include a bias adjustment with monthly (IMERG and TMPA) and daily (GSMaP) gauge accumulations, because they are widely used in the user's community. Results indicate similar behavior for IMERG and TMPA products, showing that they overestimate precipitation, while GSMaP tend to slightly underestimate the amount of rainfall in most of the analyzed regions. The exception is the northeastern coast of Brazil, where all products underestimate the daily rainfall accumulations. For all analyzed regions, GSMaP and IMERG products present a better performance compared to TMPA products; therefore, they could be suitable replacements for the TMPA. This is particularly important for hydrological forecasting in small river basins, since those products present a finer spatial and temporal resolution compared with TMPA.

Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

“…Regarding CSI, ETS, POD, FAR and BIAS, IMERG-F and 3B42V7 demonstrate similar behavior with better performance for IMERG-F [29]. Rozante et al [29] inferred that the different precipitation characteristics from these three products might partially result from the quality of the CPC and GPCC global precipitation data sets. In Brazil, the mean gauge number of the CPC data used to calibrate GSMaP is approximately 1000, whereas only approximately 300 rain gauges are included in the GPCC data set for bias-correcting the IMERG and TMPA products [29].…”

“…As parallel SPPs in the GPM-era, IMERG and GSMaP products in several regions of the world were evaluated and quantitatively compared [29,72,74,[78][79][80]. Ning et al [78] concluded that GSMaP-GAUGE version 06 (V06) performs better and has more stable quality results than IMERG-F version 04 (V04) on daily and monthly scales in Mainland China in terms of mean error (ME), RMSE, CC, and POD.…”

“…Among the four near-real-time products (PERSIANN, 3B42RT, IMERG-L, and GSMaP-NRT), Tang et al [80] found that IMERG-L and GSMaP-NRT perform closest-to-ground observations in South China and recommended IMERG-F as the best satellite product in capturing flood hazard events with low false alarms. Rozante et al [29] evaluated IMERG-F V05, GSMaP-GAUGE V07, and 3B42V7 in six Brazilian regions and the entire Brazil territory and found that GSMaP-GAUGE presents better performance than IMERG-F and 3B42V7 in all regions and at various precipitation intensity levels with respect to CSI and equitable threat score (ETS). Regarding CSI, ETS, POD, FAR and BIAS, IMERG-F and 3B42V7 demonstrate similar behavior with better performance for IMERG-F [29].…”

“…Rozante et al [29] evaluated IMERG-F V05, GSMaP-GAUGE V07, and 3B42V7 in six Brazilian regions and the entire Brazil territory and found that GSMaP-GAUGE presents better performance than IMERG-F and 3B42V7 in all regions and at various precipitation intensity levels with respect to CSI and equitable threat score (ETS). Regarding CSI, ETS, POD, FAR and BIAS, IMERG-F and 3B42V7 demonstrate similar behavior with better performance for IMERG-F [29]. Rozante et al [29] inferred that the different precipitation characteristics from these three products might partially result from the quality of the CPC and GPCC global precipitation data sets.…”

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

“…It is worth mentioning that PERSIANN estimates the precipitation based on infrared brightness temperature image (as input) and artificial neural network (as a model) (Ashouri et al 2015), while CMORPH is mainly based on microwave data and only uses infrared data for transporting the microwavebased precipitation features within periods that microwave data are not available at a location (Joyce et al 2004). Because of these improvements, IMERG precipitation products (IPPs) appeared to be better than TMPA and other SPPs in many regions, for example, India (Prakash et al 2018), Pakistan (Anjum et al 2018), Brazil (Rozante et al 2018), and East Asia (Lee et al 2019).…”