Nonparametric error model for a high resolution satellite rainfall product

被引:25
作者
Gebremichael, Mekonnen [1 ]
Liao, Gong-Yi [2 ]
Yan, Jun [2 ]
机构
[1] Univ Connecticut, Dept Civil & Environm Engn, Unit 2037, Storrs, CT 06269 USA
[2] Univ Connecticut, Dept Stat, Storrs, CT 06269 USA
关键词
TEMPORAL SAMPLING ERRORS; PRECIPITATION ESTIMATION; COMPLEX TERRAIN; UNCERTAINTY; CMORPH;
D O I
10.1029/2010WR009667
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Characterization of the error distribution of satellite rainfall product has important benefits in practical applications such as rainfall-runoff modeling. Most operational satellite rainfall products are, however, still deterministic and lack any estimate of their uncertainty. Given a high resolution satellite rainfall estimate, it is of interest to know the distribution of the actual rainfall. We develop a new nonparametric model that generates the distribution of actual rainfall values for any given satellite rainfall estimate. The model handles the conditional distribution as the mixture of a positive continuous distribution and a point mass at zero. We fitted and validated the model using rain gauge-adjusted ground-based radar rainfall (representing actual rainfall) and CMORPH satellite rainfall estimates (representing high-resolution satellite rainfall), available at a resolution of 0.25 degrees x 0.25 degrees and 3-hourly, over a domain of 6.25 degrees x 6.25 degrees in the southern United States where the radar rainfall products are considered to be of high quality. The modeling approach can be replicated in other regions.
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页数:9
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