A large-sample investigation of statistical procedures for radar-based short-term quantitative precipitation forecasting

We present the results of an extensive evaluation of radar-based quantitati ve precipitation forecasting techniques. Using a large data set of radar ob servations from the Tulsa, Oklahoma, WSR-88D radar we evaluate several tech niques, including persistence, advection, and neural-network-based schemes. The scope of our study is limited to very-short-term forecast lead-times o f up to three hours. We consider several spatial resolutions ranging from 4 x 4 km to 32 x 32 km(2). Performance of the schemes is evaluated using sev eral popular criteria that include correlation coefficient, multiplicative bias, and probability of detection. We discuss the effects of average storm intensity and rainfall intensity integration on the predictability limits. The most significant conclusions from the study are: (1) advection is the most important physical process that impacts useful predictions; (2) larger and more intense storms are easier to forecast; and (3) both spatial and t emporal integration significantly extends the predictability limits. (C) 20 00 Elsevier Science B.V. All rights reserved.