Editorial Type:
Article
Article Type:
Research Article

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

Francisco J. Tapiador School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom

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Chris Kidd School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom

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Vincenzo Levizzani Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy

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Frank S. Marzano Department of Electric Engineering, University of L'Aquila, L'Aquila, Italy

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Print Publication:
01 Apr 2004
DOI:
https://doi.org/10.1175/1520-0450(2004)043<0576:ANNFTT>2.0.CO;2
Page(s):
576–594
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Abstract

The purpose of this paper is to evaluate a new operational procedure to produce half-hourly rainfall estimates at 0.1° 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 ∼0.6 correlations with PMW data (∼0.2 with gauge), and average correlations of up to 0.7 and 0.6 are obtained for 0.5° and 0.1° monthly accumulated estimates, respectively.

Corresponding author address: Dr. Francisco J. Tapiador, School of Geography, Earth and Environmental Sci., University of Birmingham, B15 2TT Birmingham, United Kingdom. f.tapiador@bham.ac.uk

Abstract

The purpose of this paper is to evaluate a new operational procedure to produce half-hourly rainfall estimates at 0.1° 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 ∼0.6 correlations with PMW data (∼0.2 with gauge), and average correlations of up to 0.7 and 0.6 are obtained for 0.5° and 0.1° monthly accumulated estimates, respectively.

Corresponding author address: Dr. Francisco J. Tapiador, School of Geography, Earth and Environmental Sci., University of Birmingham, B15 2TT Birmingham, United Kingdom. f.tapiador@bham.ac.uk

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