A Neural Networks–Based Fusion Technique to Estimate Half-Hourly Rainfall Estimates at 0.1° Resolution from Satellite Passive Microwave and Infrared Data

The purpose of this paper is to evaluate a new operational procedure to produce half-hourly rainfall estimates at 0.1degrees spatial resolution. Rainfall is estimated using a neural networks (NN)-based approach utilizing passive microwave (PMW) and infrared satellite measurements. Several neural networks are tested, from multilayer perceptron to adaptative resonance theory architectures. The NN analytical selection process is explained. Half-hourly rain gauge data over Andalusia, Spain, are used for validation purposes. Several interpolation procedures are tested to transform point to areal measurements, including the maximum entropy estimation method. Rainfall estimations are also compared with Geostationary Operational Environmental Satellite precipitation index and histogram-matching results. Half-hourly rainfall estimates give similar to0.6 correlations with PMW data (similar to0.2 with gauge), and average correlations of up to 0.7 and 0.6 are obtained for 0.5degrees and 0.1degrees monthly accumulated estimates, respectively.