Global precipitation estimation from satellite imagery using artificial neural networks

INTRODUCTION Precipitation is the key hydrologic variable linking the atmosphere with land-surface processes, and playing a dominant role in both weather and climate. The Global Water and Energy Cycle Experiment (GEWEX), recognizing the strategic role of precipitation data in improving climate research, strongly emphasized the need to achieve global measurement of precipitation with sufficient accuracy to enable the investigation of regional to global water and energy distribution. Additionally, many other international research programs have also placed high priority on the development of reliable global precipitation observation. During the past few decades, satellite-sensor technology has facilitated the development of innovative approaches to global precipitation observations. Clearly, satellite-based technologies have the potential to provide improved precipitation estimates for large portions of the world where gauge observations are limited. Recently many satellite-based precipitation algorithms have been developed (Ba and Gruber, 2001; Huffman et al ., 2002; Joyce et al ., 2004; Negri et al ., 2002; Sorooshian et al ., 2000; Tapiador 2002; Turk et al ., 2002; Vicente et al ., 1998; Weng et al ., 2003). These algorithms generate precipitation products consisting of higher spatial and temporal resolution with potential to be used in hydrologic research and water-resources applications. Evaluation of recently developed precipitation products over various regions is ongoing (Ebert, 2004; Kidd, 2004; Janowiak, 2004). In this chapter, we will introduce one near-global precipitation product generated from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) algorithm.