Date of Award
Master of Science
Geological and Atmospheric Sciences
William A. Gallus
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.
Christopher John Schaffer
Schaffer, Christopher John, "Improving probabilistic ensemble forecasts of convection through the application of QPF-POP relationships" (2010). Graduate Theses and Dissertations. 11758.