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| Titel |
Generalization of information-based concepts in forecast verification |
| VerfasserIn |
J. Tödter, B. Ahrens |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250065097
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| Zusammenfassung |
| This work deals with information-theoretical methods in probabilistic forecast verification.
Recent findings concerning the Ignorance Score are shortly reviewed, then the generalization
to continuous forecasts is shown. For ensemble forecasts, the presented measures can be
calculated exactly.
The Brier Score (BS) and its generalizations to the multi-categorical Ranked Probability
Score (RPS) and to the Continuous Ranked Probability Score (CRPS) are the prominent
verification measures for probabilistic forecasts. Particularly, their decompositions into
measures quantifying the reliability, resolution and uncertainty of the forecasts are
attractive.
Information theory sets up the natural framework for forecast verification. Recently, it has
been shown that the BS is a second-order approximation of the information-based Ignorance
Score (IGN), which also contains easily interpretable components and can also be generalized
to a ranked version (RIGN).
Here, the IGN, its generalizations and decompositions are systematically discussed in
analogy to the variants of the BS. Additionally, a Continuous Ranked IGN (CRIGN) is
introduced in analogy to the CRPS. The applicability and usefulness of the conceptually
appealing CRIGN is illustrated, together with an algorithm to evaluate its components
reliability, resolution, and uncertainty for ensemble-generated forecasts. This is also directly
applicable to the more traditional CRPS. |
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