Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications

Abstract

In this study, the recent work of Gottschalck et al. and Ebert et al. is extended by assessing the suitability of two Tropical Rainfall Measuring Mission (TRMM)-based precipitation products for hydrological land data assimilation applications. The two products are NASA's gauge-corrected TRMM 3B42 Version 6 (3B42), and the satellite-only NOAA Climate Prediction Center (CPC) morphing technique (CMORPH). The two products were evaluated against ground-based rain gauge-only and gauge-corrected Doppler radar measurements. The analyses were performed at multiple time scales, ranging from annual to diurnal, for the period March 2003 through February 2006. The analyses show that at annual or seasonal time scales, TRMM 3B42 has much lower biases and RMS errors than CMORPH. CMORPH shows season-dependent biases, with overestimation in summer and underestimation in winter. This leads to 50% higher RMS errors in CMORPH's area-averaged daily precipitation than TRMM 3B42. At shorter time scales (5 days or less), CMORPH has slightly less uncertainty, and about 10%-20% higher probability of detection of rain events than TRMM 3B42. In addition, the satellite estimates detect more high-intensity events, causing a remarkable shift in precipitation spectrum. Summertime diurnal cycles in the United States are well captured by both products, although the 8-km CMORPH seems to capture more diurnal features than the 0.25°C MORPH or 3B42 products. CMORPH tends to overestimate the amplitude of the diurnal cycles, particularly in the central United States. Possible causes for the discrepancies between these products are discussed.

Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China

“…Thus this approach is applied here to analyze the error components of three TMPA products (RTV7-UC, RTV7-C, and V7) across four different climate regions in China. A threshold of 1.0 mm/day is used to determine the occurrence of rainfall event for any given day, as suggested by many previous studies [7,16,26,38,39].…”

“…Recently, these two quasi-global satellite precipitation products have been widely utilized in various hydrological and meteorological applications in China [7][8][9][10][11][12][13]. Over the years, there have been many efforts to compare and validate available satellite precipitation estimates at global, regional, or basin scales [9,10,[14][15][16][17][18][19][20][21][22][23][24][25]. An essential point when trying to evaluate the data accuracy is to know where the retrieval errors are coming from.…”

An assessment of the performance of global rainfall estimates without ground-based observations

“…Ebert et al, 2007;Sapiano and Arkin, 2009;Tian et al, 2007;Stampoulis and Anagnostou, 2012) have investigated error associated with remotely sensed precipitation products by comparing their estimates with those collected by ground-based observations assuming they represent the zero-error rainfall. However, the physical characteristics of precipitation, particularly at finer spatial and temporal resolutions, necessitate frequent, systematic and sufficiently dense validation measurements -requirements that are often not met within data-scarce regions of Africa, Asia and South America.…”

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

“…Therefore, validation of high-resolution MPEs and quantification of their errors are essential before utilizing them further for operational or research applications. Thus far, a great deal of effort has been put into evaluating the MPEs in different climatic conditions (GlobalAdler et al (2001); Turk et al (2008); Australia, United States of America (USA) and northwestern Europe - Ebert et al (2007); Africa and South America - Dinku et al (2010); India - Prakash et al (2014); Sunilkumar et al (2015); IranGhajarnia et al (2015); China -Chen et al (2015), and references therein) and seasons (Tian et al, 2007;Kidd et al, 2012;Sunilkumar et al, 2015). Though several studies exist on the evaluation of monthly to seasonal rainfall in the literature, only a few studies focused on validating the rainfall at daily and sub-daily scales (Sapiano and Arkin, 2009;Sohn et al, 2010;Habib et al, 2012;Kidd et al, 2012;Mehran and AghaKouchak, 2014).…”