Section 6.2 Relative Skill of IFS Models

• Section 6.2.1 ECMWF headline scores
• Section 6.2.2 Anomaly Correlation Coefficient
• Section 6.2.3 Equitable Threat Score
• Section 6.2.4 Relative error differences between models

Relative Skill of IFS Models

It is important to have measures of both forecast skill and relative differences in error of ensemble control, ensemble mean, and individual ensemble members.  This allows the forecaster to assess the strength of one product over another and the way this varies through the forecast period.  The Anomaly Correlation Coefficient (ACC) and the Equitable Threat Score (ETS) show the comparative performances on a global scale.   The relative differences in error with lead-time show comparative errors with lead-time.  Relative weights can be assigned to the ensemble at different lead-times to better use the strengths of each in a more structured way.

On average, HRES is the most accurate single-run realisation of the broadscale weather patterns.  But any individual HRES forecast may not be the most skilful compared to ENS member forecasts, and in isolation it cannot provide an estimate of forecast uncertainty or confidence.