Comparison of Methodologies for Probabilistic Quantitative Precipitation Forecasting*

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Abstract
Twenty-four-hour probabilistic quantitative precipitation forecasts (PQPFs) for accumulations exceeding thresholds of 0.01, 0.05, and 0.10 in. are produced for 154 meteorological stations over the eastern and central regions of the United States. Comparisons of skill are made among forecasts generated using five different linear and nonlinear statistical methodologies, namely, linear regression, discriminant analysis, logistic regression, neural networks, and a classifier system. The predictors for the different statistical models were selected from a large pool of analyzed and predicted variables generated by the Nested Grid Model (NGM) during the four cool seasons (December–March) from 1992/93 to 1995/96. Because linear regression is the current method used by the National Weather Service, it is chosen as the benchmark by which the other methodologies are compared. The results indicate that logistic regression performs best among all methodologies. Most notable is that it performs significantly... Abstract Twenty-four-hour probabilistic quantitative precipitation forecasts (PQPFs) for accumulations exceeding thresholds of 0.01, 0.05, and 0.10 in. are produced for 154 meteorological stations over the eastern and central regions of the United States. Comparisons of skill are made among forecasts generated using five different linear and nonlinear statistical methodologies, namely, linear regression, discriminant analysis, logistic regression, neural networks, and a classifier system. The predictors for the different statistical models were selected from a large pool of analyzed and predicted variables generated by the Nested Grid Model (NGM) during the four cool seasons (December–March) from 1992/93 to 1995/96. Because linear regression is the current method used by the National Weather Service, it is chosen as the benchmark by which the other methodologies are compared. The results indicate that logistic regression performs best among all methodologies. Most notable is that it performs significantly...