Rainfall estimation from a combination of TRMM precipitation radar and GOES multispectral satellite imagery through the use of an artificial neural network

This paper describes the development of a satellite precipitation algorithm designed to generate rainfall estimates at high spatial and temporal resol utions using a combination of Tropical Rainfall Measuring Mission (TRMM) pr ecipitation radar (PR) data and multispectral Geostationary Operational Env ironmental Satellite (GOES) imagery. Coincident PR measurements were matche d with four-band GOES image data to form the training dataset for a neural network. Statistical information derived from multiple GOES pixels was matc hed with each precipitation measurement to incorporate information on cloud texture and rates of change into the estimation process. The neural networ k was trained for a region of Brazil and used to produce half-hourly precip itation 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 correla tions of similar to0.47 with independent validation data, exceeding those o f an optimized GOES Precipitation Index method locally calibrated using PR data. A combination of PR and GOES data thus may be used to generate precip itation estimates at high spatial and temporal resolutions with extensive s patial and temporal coverage, independent of any surface instrumentation.