Evaluation of PERSIANN system satellite-based estimates of tropical rainfall

PERSIANN, an automated system for Precipitation Estimation from Remotely Se nsed Information using Artificial Neural Networks, has been developed for t he estimation of rainfall from geosynchronous satellite longwave infared im agery (GOES-IR) at a resolution of 0.25 degrees x 0.25 degrees everp half-h our. The accuracy of the rainfall product is improved by adaptively adjusti ng the network parameters using the instantaneous rain-rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI prod uct 2A12), and the random errors are further reduced by accumulation to a r esolution of 1 degrees x 1 degrees daily. The authors' current GOES-IR-TRMM TMI based product, named PERSIANN-GT, was evaluated over the region 30 deg rees S-30 degrees N, 90 degrees E-30 degrees W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resultin g rain-rate estimates agree well with the National Climatic Data Center rad ar-gauge composite data over Florida and Texas (correlation coefficient rho > 0.7). The product also compares well (rho similar to 0.77-0.90) with the monthly World Meteorological Organization gauge measurements for 5 " x 5 " grid locations having high gauge densities. The PERSIANN-GT product was ev aluated further by comparing it with current TRMM products (3A11, 3B31, 3B4 2, 3B43) over the entire study region. The estimates compare well with the TRMM 3B43 1 degrees x 1 degrees monthly product, but the PERSIANN-GT produc ts indicate higher rainfall over the western Pacific Ocean when compared to the adjusted geosynchronous precipitation index-based TRMM 3B42 product.