This threshold technique initially sets the forecast to the lowest amount category (< 0.1 inch). If the probability of rainfall >=0.1 inch is 27% or higher, then the amount forecast is increased to the 0.1-0.49 inch category. The forecast is increased further if the probabilities of 0.5, 1, and 2 inches exceed 20%, 15%, and 9%, respectively. The thresholds were derived by finding the values that yielded a bias of ~1.5 within the data sample used to develop the probability equations. They yield categorical amount forecasts that capture a reasonably large portion of the heavier 1- and 2-inch rainfall events without grossly overforecasting the areal extent of the heavy rainfall.

An alternative approach, utilizing a neural network developed by the back-propagation algorithm, is being tested. The predictors presented to the network included all those appearing in the probability equations, plus the probabilities themselves. We conducted an experiment to determine if a neural-network (NN) approach could yield an improvement over this threshholding technique. The accuracy of both approaches was compared within the developmental (training) data sample, containing data from the period 1996-1998, and another sample from 1999. Results of the comparison will be included in the preprint and shown at the conference.

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