Improved Seasonal 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.

[1]  H. Storch.  On the Use of “Inflation” in Statistical Downscaling , 1999 .

[2]  N. Rayner,et al.  Version 2.2 of the Global sea-Ice and Sea Surface Temperature Data Set , 1996 .

[3]  H. Storch,et al.  Statistical Analysis in Climate Research , 2000 .

[4]  David P. Rowell,et al.  Assessing Potential Seasonal Predictability with an Ensemble of Multidecadal GCM Simulations , 1998 .

[5]  G. Boer,et al.  A transient climate change simulation with greenhouse gas and aerosol forcing: experimental design and comparison with the instrumental record for the twentieth century , 2000 .

[6]  F. Zwiers,et al.  Climate Predictions with Multimodel Ensembles , 2002 .

[7]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[8]  Zoltan Toth,et al.  Why Do Forecasts for “Near Normal” Often Fail? , 1991 .

[9]  F. Zwiers,et al.  Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2 , 1996 .

[10]  J. Blanchet,et al.  The Canadian Climate Centre Second-Generation General Circulation Model and Its Equilibrium Climate , 1992 .

[11]  R. A. Madden,et al.  Estimates of the Natural Variability of Time-Averaged Sea-Level Pressure , 1976 .

[12]  Francis W. Zwiers,et al.  Seasonal predictions based on two dynamical models , 2001 .

[13]  F. Zwiers,et al.  Skill of seasonal hindcasts as a function of the ensemble size , 2001 .

[14]  C. E. Leith,et al.  The Standard Error of Time-Average Estimates of Climatic Means , 1973 .

[15]  G. Boer,et al.  A transient climate change simulation with greenhouse gas and aerosol forcing: projected climate to the twenty-first century , 2000 .

[16]  A. H. Murphy,et al.  A General Framework for Forecast Verification , 1987 .

[17]  F. Zwiers,et al.  Effects of specifying bottom boundary conditions in an ensemble of atmospheric GCM simulations , 2000 .

[18]  Ross N. Hoffman,et al.  Lagged average forecasting, an alternative to Monte Carlo forecasting , 1983 .

[19]  F. Zwiers,et al.  Skill as a function of time scale in ensembles of seasonal hindcasts , 2001 .

[20]  A. Barnston.  Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score , 1992 .

[21]  A. H. Murphy.  A New Vector Partition of the Probability Score , 1973 .

[22]  G. Boer.  Systematic and Random Error in an Extended-Range Forecasting Experiment , 1993 .

[23]  E. Epstein.  Long-Range Weather Prediction: Limits of Predictability and Beyond , 1988 .

[24]  A. H. Murphy,et al.  The attributes diagram A geometrical framework for assessing the quality of probability forecasts , 1986 .

[25]  Jianfu Pan,et al.  Extended-Range Probability Forecasts Based on Dynamical Model Output , 1998 .