Abstract
Uncertainties in satellite rainfall estimation may derive from both the local rainfall characteristics and its subpixel variability. To study this issue, Micro Rain Radars and a rain gauge network were deployed within a 9-km satellite pixel in the semi-arid Xilingol grassland of China in summer 2009. The authors characterized the subpixel variability with the coefficient of variation (CV) and evaluated the satellite rainfall estimation for this semi-arid area. The results showed that rainfall events with a high CV were mostly convective with a small amount of rainfall. Spatially inhomogeneous rainfall was most likely to occur at the edges of small clouds producing rain. The performance of the TRMM (Tropical Rainfall Measuring Mission) 3B42V7 product for daily rainfall was better than that of the CMORPH (Climate Prediction Center morphing technique) and PERSIANN (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks) products, although the TRMM product tended to overestimate rainfall in a lake area of the semi-arid grassland.摘要卫星估雨精度的不确定性受到当地降雨类型和像元内降雨非均匀性影响, 而结合这两个关键因素开展半干旱草原卫星估雨的研究有限. 2009年夏, 我们在中国锡林郭勒半干旱草原用多部微雨雷达和雨量计构建了9 km卫星像元降雨观测网, 观测了像元内降雨非均匀性(空间变异系数CV), 并评估了卫星估雨精度. 结果表明: (1) CV值受像元内平均降雨量, 降雨类型, 降雨云面积及移向等影响, 如高CV值的降雨过程大多为平均降雨量小, 对流性降雨过程, 降雨云边缘像元CV值较高; (2) TRMM 3B42V7 卫星估雨产品适用性较好, CMORPH和PERSIANN次之, 但TRMM 3B42V7易在半干旱草原湖泊处高估降雨.
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