Subseasonal skill score based on the historical performance of each calibrated SubX model and their 3-model ensemble.

Skill is mapped by calendar month. The forecasts lead times are combined over the weeks 2-3 and the weeks 3-4 from the forecast start time (i.e., 14-day long periods respectively 9 to 22 days and 16 to 29 days after the forecast is issued). Forecasts skill scores combine start times by calendar month and across years 1999 to 2016.

These skill scores diagnostics maps give a sense of where and when (issued which months of the year and for which weekly lead times) subseasonal forecasts may have the potential to provide useful information.

**Skill scores definitions:**

**RPSS**: Ranked Probability Skill Scores (RPSS; Epstein (1969); Murphy (1969, 1971); Weigel et al. (2007)) are used to quantify the extent to which the calibrated tercile-category predictions are improved compared to climatological frequencies. RPSS values tend to be small, even for skillful forecasts. The approximate relationship between RPSS and correlation being such that a RPSS value of 0.1 corresponds to a correlation of about 0.44 (Tippett et al. 2010).

**References:**

- Epstein, E.S., 1969: A Scoring System for Probability Forecasts of Ranked Categories. J. Appl. Meteor., 8, 985–987
- Murphy, A.H., 1969: On the “Ranked Probability Score”. J. Appl. Meteor., 8, 988–989
- Murphy, A.H., 1971: A Note on the Ranked Probability Score. J. Appl. Meteor., 10, 155–156
- Tippett, M.K., A.G. Barnston, and T. DelSole, 2010: Comments on “Finite Samples and Uncertainty Estimates for Skill Measures for Seasonal Prediction”. Mon. Wea. Rev., 138, 1487–1493
- Weigel, A.P., M.A. Liniger, and C. Appenzeller, 2007: The Discrete Brier and Ranked Probability Skill Scores. Mon. Wea. Rev., 135, 118–124

**Forecasts Skill Scores:** Global 1˚ Multi-Model Ensemble forecasts skill scores per month of the year over
the period 1999-2016 available here.

Contact help@iri.columbia.edu with any technical questions or problems with this Map Room.