EVALUATION OF HIGH-RESOLUTION SATELLITE RAINFALL DATA OVER SINGAPORE

被引:14
|
作者
Hur, Jina
Raghavan, Srivatsan V. [1 ]
Ngoc Son Nguyen
Liong, Shie-Yui
机构
[1] Natl Univ Singapore, Trop Marine Sci Inst, Singapore, Singapore
关键词
TRMM; TMPA-3B42; GSMaP; extreme precipitation; diurnal cycle; Singapore precipitation; COMBINED PASSIVE MICROWAVE; MARITIME CONTINENT; PRECIPITATION; PRODUCTS; GSMAP;
D O I
10.1016/j.proeng.2016.07.437
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
The uncertainties of two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations in Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of diurnal cycle and extreme precipitation for 10 years from Dec. 2000 to Nov. 2010. The satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the ITCZ is located in Singapore. However, they fail in estimating diurnal cycle in summer. The disagreement in summer can be accounted for by the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. According to analysis of extreme precipitation indices, both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spell over all stations. In particular, the uncertainty of extreme precipitation is higher in GSMaP than in TRMM, possibly due to the several effects such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and swath time of satellite. Such discrepancies between satellite born and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results, this research on quantification of their uncertainty is useful in many respects, especially that the satellite products can stand scrutiny overplaces/stations where there are no good data to be compared against. (C) 2016 Published by Elsevier Ltd.
引用
收藏
页码:158 / 167
页数:10
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