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Rainfall estimation from a combination of TRMM Precipitation Radar and GOES Multi-Spectral Imagery through the use of an artificial neural network
journal contribution
posted on 2023-06-07, 17:19 authored by Tim Bellerby, Martin ToddMartin Todd, Dominic KnivetonDominic Kniveton, Chris KiddThis 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.
History
Publication status
- Published
Journal
Journal of Applied MeteorologyISSN
0894-8763Publisher
Journal of Applied MeteorologyIssue
12Volume
39Page range
2115-2128ISBN
1520-0450Department affiliated with
- Geography Publications
Full text available
- No
Peer reviewed?
- Yes