Assessing the ensemble spread-error relationship

Traditionally, this ability has been assessed by correlating the realized forecast error with the ensemble's dispersion. This paper revisits the limitations of the skill–spread correlation, but uses aspects of the correlation to introduce two metrics to assess an EPS's capacity to provide a reliable likelihood of its own error.

Using a perfect EPS, skill–spread correlation is shown to be limited by its dependence on how "skill" and "spread" are defined and, perhaps most fatally, by its inability to distill the skill–spread reliability from the stability properties of the physical system being modeled. Building from this, it is argued there are two aspects of an ensemble's dispersion that should be assessed. First, is there enough variability in the dispersion to justify the expense of the EPS?