Quantitative precipitation forecasts (QPF) are extremely important for many applications, but have remained one of the major challenges in meteorology. Improvements have traditionally been measured using a “threat score,” and by this measure annually averaged performance nationally has improved very slowly from ~0.25 to ~0.30 over many years. In addition to the slow rate of improvement, the measure itself is not as effective as needed in the case of extreme precipitation events.

These, and other drivers, led to the creation by NOAA of the Hydrometeorological Testbed (HMT), which was first implemented in California. Out of this effort has emerged a potential new performance measure for QPF in extreme precipitation events associated with land-falling Pacific winter storms. The new method is presented here, along with results from the very wet HMT-2006 field study. The analysis included sites that received up to 100 inches of rainfall that winter, and a number of events that produced over 5 inches of rain in 24 hours.

Working closely with the providers of the formal QPF for the area from NOAA/NWS, i.e., NCEP's Hydrometeorological Prediction Center (HPC) and the California/Nevada River Forecast center (CNRFC), a data set of forecasts and verification for 17 sites representative of coastal, inland valley and mountain conditions, was collected. A methodology was then developed to determine the probability of detection (POD) and false alarm rates (FAR) for events characterized by 1-3 inches, 3-5 inches and >5 inches of rain in 24 hours at each site, at forecast lead times of 1, 2 and 3 days. These results include analysis involving 16 events that experienced >5 inches of rainfall, of which 2 were predicted to be that extreme at 1 day lead time.