A neural network, using input from the Era Model and upper air soundings, h
as been developed for the probability of precipitation (PoP) and quantitati
ve precipitation forecast (QPF) for the Dallas-Fort Worth, Texas, area. For
ecasts from two years were verified against a network of 36 rain gauges. Th
e resulting forecasts were remarkably sharp, with over 70% of the PoP forec
asts being less than 5% or greater than 95%. Of the 436 days with forecasts
of less than 5% PoP, no rain occurred on 435 days. On the 111 days with fo
recasts of greater than 95% PoP, rain always occurred. The linear correlati
on between the forecast and observed precipitation amount was 0.95. Equitab
le threat scores for threshold precipitation amounts from 0.05 in. (similar
to 1 mm) to 1 in. (similar to 25 mm) are 0.63 or higher, with maximum valu
es over 0.86. Combining the PoP and QPF products indicates that for very hi
gh PoPs, the correlation between the QPF and observations is higher than fo
r lower pops. In addition, 61 of the 70 observed rains of at least 0.5 in.
(12.7 mm) are associated with PoPs greater than 85%. As a result, the syste
m indicates a potential for more accurate precipitation forecasting.