Extended Abstract

To co mpare t he ab ilities of 3B 42, C MORPH, a nd GMS5-TBB pr oducts i n differentregions of China, the area of 90ºE-150ºE, 10ºN-50ºN are divided to 5o × 5o grid boxes, andthen TS and ETS are averaged in each grid box (Fig. 1).4. Comparison results of 6h rainfallAlthough GMS5-TBB based rain dataset has higher TS and ETS scores than 3B42 andCMORPH for rainfall of 10, 25 and 50 mm/6h, the 6-h rainfall is not as good as that for the24-h rainfall. Its TS, ETS, and CC for 6-h rainfall become almost half of that for 24-h rainfalltoo (Table 3). The TBB retrieved 6-hourly rainfall has no skill for heavy rainfall.5. ConclusionsThe results show that compared with rain data from both the 3B42 and CMORPH, theGMS5-TBB data show much higher skill in representing the heavy rainfall events. All threesatellite-retrieved r ainfall datasets se em t o g ive q uite reasonable 6 -h a nd 24 -h r ainfalldistributions an d t heir skills d ecrease with the i ncrease i n b oth the latitude and t he rainfallamount in general. But they have different performance skills in reflecting rainfall of differentamounts. The TSs for the 24-h rainfall retrievals for landfall TCs are 0.62, 0.47, 0.35, 0.27,0.17 for GMS5-TBB data, respectively, for 1, 10, 25, 50 and 100 mm/day while they are 0.59,0.33, 0.13, 0.03, and 0.0 for the 3B42, and 0.56, 0.25, 0.07, 0.01, and 0.0 for CMORPH. Itindicates t hat 3B42 a nd CMORPH ha ve a s imilar pe rformance s kill in reflecting t heTCs-related rainfall but the 3B42 show a little better performance in general, which would belikely related to its gauge rain adjustment.The datasets of 3B42 and CMORPH have overestimated the light rainfall (0-1 mm/day)and underestimated the non-light rain (over 1 mm/day). Large differences between the 3B42or CMORPH and GMS5-TBB products are found mainly for the heavy rainfall. Even thoughthe GMS5-TBB data overestimate the light and moderate rainfall and underestimate the heavyrain l ike 3 B42 an d C MORPH, i t g ives m uch i mproved h eavy r ain e stimates w ith almosthalved bias of that from 3B42 and CMORPH products. We also find that the three satelliteproducts evaluated in this study are more accurate for the 24-h rainfall estimates than for the6-h rainfall estimates.Main references:Huffman G. J., R. F. Adler, D. T. Bolvin, G. Gu, E. J. Nelkin, K. P. Bowman, Y. Hong, E. F.Stocker, and D . B . Wolff, 2007 : The T RMM M ultisatellite Precipitation Analysis(TMPA): Qu asi-Global, M ultiyear, C ombined-Sensor P recipitation E stimates a t F ineScales. J. Hydromet., 8, 38–55.Joyce R . J., J. E . Janowiak, P . A . A rkin, a nd P . Xie, 2004 : C MORPH: A method t hatproduces g lobal p recipitation e stimates from pa ssive m icrowave a nd i nfrared data a thigh spatial and temporal resolution. J. Hydromet., 5, 487-503.Sorooshian S., K. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluationof P ERSIANN S ystem S atellite–Based E stimates of T ropical R ainfall. Bull. Am.Meteor. Soc., 81, 2035–2046.-115-