Quantitative precipitation forecasts provide an accumulated precipitation amount for a given

time period, and accurate forecasts depend on the correct prediction of areal coverage,

timing, and intensity of precipitation. These forecasts are important to a variety of people for

many different purposes, so expressing a likelihood of precipitation is also useful. Most

simply, probabilities of precipitation are determined by considering the percentage of

ensemble members forecasting precipitation greater than a specified threshold amount.

Probabilities of precipitation can also be formed from quantitative precipitation forecasts

through statistical post-processing. Past research has shown that there are many ways to

post-process precipitation data, such as by binning the precipitation amounts, applying

statistical calibration, and/or considering the percentage of an area receiving precipitation.

The main goal of this study was to expand upon relationships between quantitative

precipitation forecasts and probabilities of precipitation by developing new approaches that

yield more accurate probabilities of precipitation than methods that are currently more

commonly used. Ensemble forecasts from the 2007 and 2008 NOAA Hazardous Weather

Testbed Spring Experiments were used to provide quantitative precipitation forecasts for

various days. In the study, four main approaches were developed and tested extensively

using Brier scores and other statistics. Brier scores for different approaches were compared

to traditional methods of calculating probabilities of precipitation. It was shown at both 20

km and 4 km grid spacings that new approaches were able to produce statistically significantly better forecasts than a traditional method that relies upon calibration of POP forecasts derived using equal-weighting of ensemble members. A deterministic approach developed during the study was also able to produce forecasts comparable to those of the calibrated traditional method.