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Tim Bellerby, Martin Todd, Dom Kniveton, and Chris Kidd

Abstract

This paper describes the development of a satellite precipitation algorithm designed to generate rainfall estimates at high spatial and temporal resolutions using a combination of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data and multispectral Geostationary Operational Environmental Satellite (GOES) imagery. Coincident PR measurements were matched with four-band GOES image data to form the training dataset for a neural network. Statistical information derived from multiple GOES pixels was matched with each precipitation measurement to incorporate information on cloud texture and rates of change into the estimation process. The neural network was trained for a region of Brazil and used to produce half-hourly precipitation estimates for the periods 8–31 January and 10–25 February 1999 at a spatial resolution of 0.12 degrees. These products were validated using PR and gauge data. Instantaneous precipitation estimates demonstrated correlations of ∼0.47 with independent validation data, exceeding those of an optimized GOES Precipitation Index method locally calibrated using PR data. A combination of PR and GOES data thus may be used to generate precipitation estimates at high spatial and temporal resolutions with extensive spatial and temporal coverage, independent of any surface instrumentation.

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