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

Skill is mapped by calendar month for weekly lead times. Week 1 = days 2-8, Week 2 = days 9-15, Week 3 = days 16-22, Week 4 = days 23-29 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.