Satellite-Based Precipitation Measurement Using PERSIANN System

Abstract

PERSIANN (Precipitation Estimation fromRemotely Sensed Information using Artificial Neural Networks) is a satellite-based rainfall estimation algorithm. It uses local cloud textures from longwave infrared images of the geostationary en- vironmental satellites to estimate surface rainfall rates based on an artificial neural network algorithm. Model parameters are frequently updated from rainfall estimates provided by low-orbital passive microwave rainfall estimates. The PERSIANN al- gorithm has been evolving since 2000, and has generated near real-time rainfall estimates continuously for global water and energy studies. This paper presents the development of the PERSIANN algorithm in the past 10 years. In addition, the val- idation and merging PERSIANN rainfall with ground-based rainfall measurements for hydrologic applications are also discussed.