Key points are not available for this paper at this time.
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 p > 0.7). The product also compares well (p ~ 0.77–0.90) with the monthly World Meteorological Organization gauge measurements for 5° × 5° grid locations having high gauge densities. The PERSIANN-GT product was evaluated further by comparing it with current TRMM products (3A11, 3B31, 3B42, 3B43) over the entire study region. The estimates compare well with the TRMM 3B43 1° × 5 1° monthly product, but the PERSIANN-GT products indicate higher rainfall over the western Pacific Ocean when compared to the adjusted geosynchronous precipitation index–based TRMM 3B42 product.
Sorooshian et al. (Fri,) studied this question.