Abstract
Abstract PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES–IR) at a resolution of 0.25° × 0.25° every half–hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain–rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° × 1° daily. The authors' current GOES–IR–TRMM TMI based product, named PERSIANN–GT, was evaluated over the region 30°S–30°N, 90°E–30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain–rate estimates agree well with the National Climatic Data Center radar–gauge composite data over Florida and Texas (correlation coefficient r > 0.7). The product al...
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