Continental Passive Microwave-Based Rainfall Estimation Algorithm: Application to the Amazon Basin

Abstract

This paper presents a new statistical algorithm to estimate rainfall over the Amazon Basin region using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm relies on empirical relationships derived for different raining-type systems between coincident measurements of surface rainfall rate and 85-GHz polarization-corrected brightness temperature as observed by the precipitation radar (PR) and TMI on board the TRMM satellite. The scheme includes rain/no-rain area delineation (screening) and system-type classification routines for rain retrieval. The algorithm is validated against independent measurements of the TRMM-PR and S-band dual-polarization Doppler radar (S-Pol) surface rainfall data for two different periods. Moreover, the performance of this rainfall estimation technique is evaluated against well-known methods, namely, the TRMM-2A12 [the Goddard profiling algorithm (GPROF)], the Goddard scattering algorithm (GSCAT), and the National Environmental Satellite, Data, and Information Service (NESDIS) algorithms. The proposed algorithm shows a normalized bias of approximately 23% for both PR and S-Pol ground truth datasets and a mean error of 0.244 mm h Ϫ1 (PR) and Ϫ0.157 mm h Ϫ1 (S-Pol). For rain volume estimates using PR as reference, a correlation coefficient of 0.939 and a normalized bias of 0.039 were found. With respect to rainfall distributions and rain area comparisons, the results showed that the formulation proposed is efficient and compatible with the physics and dynamics of the observed systems over the area of interest. The performance of the other algorithms showed that GSCAT presented low normalized bias for rain areas and rain volume [0.346 (PR) and 0.361 (S-Pol)], and GPROF showed rainfall distribution similar to that of the PR and S-Pol but with a bimodal distribution. Last, the five algorithms were evaluated during the TRMM-Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) 1999 field campaign to verify the precipitation characteristics observed during the easterly and westerly Amazon wind flow regimes. The proposed algorithm presented a cumulative rainfall distribution similar to the observations during the easterly regime, but it underestimated for the westerly period for rainfall rates above 5 mm h Ϫ1. NESDIS 1 overestimated for both wind regimes but presented the best westerly representation. NESDIS 2 , GSCAT, and GPROF underestimated in both regimes, but GPROF was closer to the observations during the easterly flow.

Cloud-to-ground lightning and Mesoscale Convective Systems

“…As discussed by some authors (Prigent et al, 2001;Prigent et al, 2005), the temperature difference (T BV -T BH ) at 85 GHz can indicate the region and preferential orientation type (vertical or horizontal) of the ice particles associated with electrical activity. For the region and period of study cited above, only pixels with a Polarization Corrected Temperature (PCT) less than 250 K were selected in order to avoid signals not originating from convective clouds (similar to Spencer et al, 1989;Mohr and Zíper, 1996;Cecil et al, 2002;Biscaro and Morales, 2008). Thus, for each pixel the difference T BV -T BH was determined and the occurrences of lightning were counted from 7.5 minutes before until 7.5 after the time of the scanned line in an elliptical region of 7 km by 5 km centered at the pixel position.…”

Rainfall screening methodology using TRMM data over a river basin

“…The GPROF employs an RNC discrimination algorithm prior to rainfall retrieval using a probabilistic Bayesian approach (Kummerow et al 2011). Nevertheless, RNC discrimination over land regions is still seen as a challenging task (Grecu and Anagnostou 2001, Seto et al 2005, Biscaro and Morales 2008, Kida et al 2009). An authoritative description of RNC using passive radiometers can be found in Indu and Kumar (2014).…”

Rain Rate Retrieval Algorithm for Conical-Scanning Microwave Imagers Aided by Random Forest, RReliefF, and Multivariate Adaptive Regression Splines (RAMARS)

“…There have been on-going research efforts to improve the satellite precipitation estimate using passive microwave (PMW) radiometers for a few decades. The algorithms and validation results related to passive microwave estimate of precipitation can be found in [1], [2], [3], [4], and others. Among many proposed algorithms, the Goddard Profiling Algorithm (GPROF) is well-known and being used as an operational algorithm for many PMW sensors, including the TMI [5][6][7].…”