Satellite Rainfall Uncertainty Estimation Using an Artificial Neural Network

Abstract

Abstract This paper describes a neural network–based approach to estimate the conditional distribution function (cdf) of rainfall with respect to multidimensional satellite-derived input data. The methodology [Conditional Histogram of Precipitation (CHIP)] employs a histogram-based approximation of the cdf. In addition to the conditional expected rainfall rate, it provides conditional probabilities for that rate falling within each of a fixed set of intervals or bins. A test algorithm based on the CHIP approach was calibrated against Goddard profiling algorithm (GPROF) rainfall data for June–August 2002 and then used to produce a 30-min, 0.5° rainfall product from global (60°N–60°S) composite geostationary thermal infrared imagery for June–August 2003. Estimated rainfall rates and conditional probabilities were validated against 2003 GPROF data. The CHIP methodology provides the means to extend existing probabilistic and ensemble satellite rainfall error models, conditioned on a single, scalar, satellite rainfall predictor or upon scalar rainfall-rate outputs, to make full use of multidimensional input data.