Session 9A.3A Probabilistic quantitative precipitation forecasts

Thursday, 28 June 2007: 11:00 AM
Summit A (The Yarrow Resort Hotel and Conference Center)
Steven A. Amburn, NOAA/NWS, Tulsa, OK; and J. M. Frederick

Presentation PDF (1.2 MB)

To enhance customer service, the National Weather Service Weather Forecast Office in Tulsa, Oklahoma, routinely makes probabilistic rainfall forecasts for arbitrarily selected rainfall amounts (0.10, 0.50, 1.00, 2.00 inches). These probabilistic quantitative precipitation forecasts have been experimental since 2005, and specifically provide the unconditional probability of exceedance for the select rainfall amounts. The forecast method uses the fact that frequency distributions of rainfall amounts typically fit the exponential distribution.

Meteorologists make gridded/areal forecasts for both the probability of precipitation and the quantitative precipitation forecast (QPF). The QPF is in effect the mean of the rainfall distribution expected for the period in question at each 2.5 x 2.5 km grid box. Therefore, the QPF defines a unique probability density function for each grid box for the particular rain event. The exceedance probabilities are then calculated for each grid box across the entire forecast area.

The benefit of this application is to allow users who know their own cost/benefit ratios to use these exceedance probabilities in decision making. A twenty percent probability of 0.25 inches of rain may not stop a farmer from cutting hay. However, a ten percent chance of two inches of rain may be sufficient for an emergency manager or city storm water official to take specific steps to prepare for the possible event.

An explanation of the method will be shown along with comparisons to previous studies, verification scores for a number of rainfall events, and examples of POE forecasts. Tulsa's experimental forecasts are available at However, other NWS Weather Forecast Offices will be testing the application.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner