Abstract
Skill scores defined as measures of relative mean square error—and based on standards of reference representing climatology, persistence, or a linear combination of climatology and persistence—are decomposed. Two decompositions of each skill score are formulated: 1) a decomposition derived by conditioning on the forecasts and 2) a decomposition derived by conditioning on the observations. These general decompositions contain terms consisting of measures of statistical characteristics of the forecasts and/or observations and terms consisting of measures of basic aspects of forecast quality. Properties of the terms in the respective decompositions are examined, and relationships among the various skill scores—and the terms in the respective decompositions—are described.
Hypothetical samples of binary forecasts and observations are used to illustrate the application and interpretation of these decompositions. Limitations on the inferences that can be drawn from comparative verification based on skill scores, as well as from comparisons based on the terms in decompositions of skill scores, are discussed. The relationship between the application of measures of aspects of quality and the application of the sufficiency relation (a statistical relation that embodies the concept of unambiguous superiority) is briefly explored.
The following results can be gleaned from this methodological study. 1) Decompositions of skill scores provide quantitative measures of—and insights into—multiple aspects of the forecasts, the observations, and their relationship. 2) Superiority in terms of overall skill is no guarantor of superiority in terms of other aspects of quality. 3) Sufficiency (i.e., unambiguous superiority) generally cannot be inferred solely on the basis of superiority over a relatively small set of measures of specific aspects of quality.
Neither individual measures of overall performance (e.g., skill scores) nor sets of measures associated with decompositions of such overall measures respect the dimensionality of most verification problems. Nevertheless, the decompositions described here identify parsimonious sets of measures of basic aspects of forecast quality that should prove to be useful in many verification problems encountered in the real world.
