Comparison of TMPA-3B42 Versions 6 and 7 Precipitation Products with Gauge-Based Data over India for the Southwest Monsoon Period

Abstract

The upgraded version 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products is available to the user community. In this paper, two successive versions of the TMPA-3B42 research monitoring product, version 6 (V6) and V7, at the daily scale are evaluated over India during the southwest monsoon with gauge-based data for a 13-yr (1998-2010) period. Over typical monsoon rainfall zones, biases are improved by 5%-10% in V7 over the regions of higher rainfall like the west coast, northeastern, and central India. A similar reduced bias is seen in V7 over the rain-shadow region located in southeastern India. In terms of correlation, anomaly correlation, and RMSE, a marginal improvement is seen in V7. Additionally, in all-India summer monsoon rainfall amounts, mean, interannual values, and standard deviation show an overall improvement in V7. Different skill metrics over typical subregions within India show an improvement of the monsoon rainfall representation in V7. Rainfall frequency in different categories also indicates an overall improvement in V7 across all scales and subregions. Over central India regions associated with the monsoon transients, the sign of the bias has changed toward a positive bias. Even if the bias in the frequency of the occurrence of light rain has improved in V7, the values still show a large difference compared to observations. Though both V6 and V7 are able to represent the anomalous dry/wet regions during contrasting monsoon years, V7 shows some improvement in amplitude of those anomalies over V6. In general, V7 has considerably improved over V6 and will continue to be in demand from various sectors of observed rainfall data users.

Evaluation and Comparison of TRMM Multi-Satellite Precipitation Products With Reference to Rain Gauge Observations in Hunza River Basin, Karakoram Range, Northern Pakistan

“…Compared to the 3B42V6 and 3B42RT, the 3B42V7 exhibited improvements in Bias, CC, RMSE, POD, FAR and CSI at all time periods, which was consistent with Overall, the performance of the precipitation estimates derived from gauge adjusted 3B42V7 product were better than that of its predecessor bias adjusted 3B42V6 and unadjusted 3B42RT products in the northern Pakistan. Compared to the 3B42V6 and 3B42RT, the 3B42V7 exhibited improvements in Bias, CC, RMSE, POD, FAR and CSI at all time periods, which was consistent with the findings in Bhutan [33], India [10], Morocco [13], and China [28]. Despite the fact that 3B42V7 showed superiorities over 3B42V6 and 3B42RT, it still exhibits poor agreements with the gauge-based reference data at daily and seasonal time scales as analyzed with lower values of CC.…”

“…It is well reported that the accuracy of satellite-based precipitation products is dependent on precipitation variability, topography, and climate [6][7][8][9][10]. A large number of studies have evaluated the performance of 3V42V6 in many parts of world [11][12][13][14][15].…”

Assessment of TRMM-based TMPA-3B42 and GSMaP precipitation products over India for the peak southwest monsoon season

“…are computed for continuous verification of multisatellite rainfall products. These skill metrics are recently used by Prakash et al (2014aPrakash et al ( , 2015 for the evaluation of different multisatellite rainfall products over the Indian …”

Economic Analysis and Feasibility of Rainwater Harvesting Systems in Urban and Peri-Urban Environments: A Review of the Global Situation with a Special Focus on Australia and Kenya

“…Daily, let alone hourly data, however, is not always available, and for ease of calculation Hajani et al [25] developed a set of regression equations using MAR data to estimate reliability and water savings anywhere in the peri-urban regions of Greater Sydney, Australia. Developments in satellite estimation of rainfall data where there is little or no ground data available [107][108][109][110] may also become a powerful tool in estimating potential yield, water savings and hence financial viability of a RWH system.…”