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Logistic models are studied as a tool to convert output from numericalweather forecasting systems (deterministic and ensemble) into probabilityforecasts for binary events. A logistic model obtains by putting thelogarithmic odds ratio equal to a linear combination of the inputs. As anystatistical model, logistic models will suffer from over-fitting if the numberof inputs is comparable to the number of forecast instances. Computationalapproaches to avoid over-fitting by regularisation are discussed, and efficientapproaches for model assessment and selection are presented. A logit version ofthe so called lasso, which is originally a linear tool, is discussed. In lassomodels, less important inputs are identified and discarded, thereby providingan efficient and automatic model reduction procedure. For this reason, lassomodels are particularly appealing for diagnostic purposes.

Generating Probabilities From Numerical Weather Forecasts by Logistic Regression