A short-range quantitative precipitation forecast algorithm using back-propagation neural network approach

A back-propagation neural network (BPNN) was used to establish relationships between the short-range (0-3-h) rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satellite-estimated cloud-top temperature, lightning strike rates, and Nested Grid Model (NGM) outputs. Quantitative precipitation forecasts (QPF) and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage III observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall ⩾25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression.