Improved Results for Probabilistic Quantitative Precipitation Forecasting

Abstract

As a follow-up to a recent paper by the authors in which various methodologies for probabilistic quantitative precipitation forecasting were compared, it is shown here that the skill scores for linear regression and logistic regression can be improved by the use of alternative methods to obtain the model order and the coefficients of the predictors. Moreover, it is found that an even simpler, and more computationally efficient, methodology, called binning, yields Brier skill scores that are comparable to those of logistic regression. The Brier skill scores for both logistic regression and binning are found to be significantly higher at the 99% confidence level than the ones for linear regression. In response to questions that have arisen concerning the significance test used in the authors' previous study, an alternative method for determining the confidence level is used in this study and it is found that it yields results comparable to those obtained previously, thereby lending support to the conclusion that logistic regression is significantly more skillful than linear regression.