Improved Seasonal Probability Forecasts

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Abstract
A simple statistical model of seasonal variability is used to explore the properties of probability forecasts and their accuracy measures. Two methods of estimating probabilistic information from an ensemble of deterministic forecasts are discussed. The estimators considered are the straightforward nonparametric estimator defined as the relative number of the ensemble members in an event category, and a parametric Gaussian estimator derived from a fitted Gaussian distribution. The parametric Gaussian estimator is superior to the standard nonparametric estimator on seasonal timescales. A statistical skill improvement technique is proposed and applied to a collection of 24-member ensemble seasonal hindcasts of northern winter 700-hPa temperature (T700) and 500-hPa height (Z500). The improvement technique is moderately successful for T700 but fails to improve Brier skill scores of the already relatively reliable raw Z500 probability forecasts. Abstract A simple statistical model of seasonal variability is used to explore the properties of probability forecasts and their accuracy measures. Two methods of estimating probabilistic information from an ensemble of deterministic forecasts are discussed. The estimators considered are the straightforward nonparametric estimator defined as the relative number of the ensemble members in an event category, and a parametric Gaussian estimator derived from a fitted Gaussian distribution. The parametric Gaussian estimator is superior to the standard nonparametric estimator on seasonal timescales. A statistical skill improvement technique is proposed and applied to a collection of 24-member ensemble seasonal hindcasts of northern winter 700-hPa temperature (T700) and 500-hPa height (Z500). The improvement technique is moderately successful for T700 but fails to improve Brier skill scores of the already relatively reliable raw Z500 probability forecasts.