Abstract
A methodology to enhance satellite precipitation estimation using unsupervised dimensionality reduction (UDR) techniques is developed. This enhanced technique is an extension to the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched using wavelet features combined with dimensionality reduction. Cloud-top brightness temperature measurements from the Geostationary Operational Environmental Satellite (GOES)-12 are used for precipitation estimation at 4 km × 4 km spatial resolutions every 30 min. The study area in the continental U.S. covers parts of Louisiana, Arkansas, Kansas, Tennessee, Mississippi, and Alabama. Based on quantitative measures, root mean square error and Heidke skill score (HSS), the results show that the UDR techniques can improve the precipitation estimation accuracy. In addition, the independent component analysis is shown to have better performance than other UDR techniques; and in some cases, it achieves 10% improvement in the HSS.
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