On Using ``Climatology'' as a Reference Strategy in the Brier and Ranked Probability Skill Scores

By Simon Mason. 2004

145 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/235c8bac0bb4ec53afe63c2576eadaccd43d4aeb

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In Global and Planetary Change

Citations in the corpus

Generalization of the Ignorance Score: Continuous Ranked Version and Its Decomposition (2012, background): “It sets up a reasonable reference for skill scores (Mason 2004). b. Multicategory predictand Often, continuous variables, like precipitation or temperature, are discretized by applying an appropriate number of thresholds, resulting in a predictand with K categories.”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, background): “Although all these measures are thoroughly described in Thornes and Stephenson (2001), Jolliffe and Stephenson (2003), Doblas-Reyes et al. (2003) and Mason (2004) , a brief description can be found in Appendix A.”
El Niño: Catastrophe or Opportunity (2005, background): “One can estimate the level of significance for RPSS via a Monte Carlo resampling of the forecasts, but the RPSS is a stringent metric; forecasts drawn at random relative to the observed sequence of years typically yield a RPSS worse than that of climatological forecasts (Mason 2004).”
Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction (2004, background): “Random probability forecasts score worse than climatology and can yield large negative RPSS values, because confident but incorrect forecasts are penalized acutely in (3) (e.g., Goddard et al. 2003; Mason 2004).”
The discrete brier and ranked probability skill scores (2007, background): “Indeed, the RPSS is negatively biased (Richardson 2001; Kumar et al. 2001; Mason 2004).”
Sampling Uncertainty and Confidence Intervals for the Brier Score and Brier Skill Score (2008, methodology): “Because it involves a ratio of sample estimators, the Brier skill score is a biased estimator of SSMSE (Mason 2004).”