INFRARED SATELLITE PRECIPITATION ESTIMATE USING WAVELET-BASED CLOUD CLASSIFICATION AND RADAR CALIBRATION

被引：3
|
作者：
Mahrooghy, Majid [1 ,2 ]
Anantharaj, Valentine G. [2 ]
Younan, Nicolas H. [1 ,2 ]
Petersen, Walter A. [3 ]
Turk, F. Joseph [4 ]
Aanstoos, James [2 ]
机构：
[1] Mississippi State Univ, Dept Elect Engn, Mississippi State, MS 39762 USA
[2] Mississippi State Univ, Geosyst Res Inst, Mississippi State, MS 39762 USA
[3] NASA Marshall Space Flight Ctr, Huntsville, AL USA
[4] Jet Prop Lab, Pasadena, CA USA
来源：
2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2010年
关键词：
wavelet transforms; neural networks; clustering methods; curve fitting; RAINFALL ESTIMATION; PASSIVE MICROWAVE; NETWORK;
D O I：
10.1109/IGARSS.2010.5649049
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
We have developed a methodology to enhance an infrared-based high resolution rainfall retrieval algorithm by intelligently calibrating the rainfall estimates using space-based observations. Our approach involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) feature extraction using a wavelet-based method; 3) clustering and classification of cloud patches; and 4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The results show that using wavelet features along with other features increase the performance of rainfall estimate in terms of quantitative rain/no rain area estimates. In addition, using lightning data as a feature improves the estimates as well.
引用
收藏
页码：2345 / 2348
页数：4
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