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| Titel |
Model error in weather forecasting |
| VerfasserIn |
D. Orrell, L. Smith, J. Barkmeijer, T. N. Palmer |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 8, no. 6 ; Nr. 8, no. 6, S.357-371 |
| Datensatznummer |
250005882
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| Publikation (Nr.) |
 copernicus.org/npg-8-357-2001.pdf |
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| Zusammenfassung |
| Operational
forecasting is hampered both by the rapid divergence of nearby initial
conditions and by error in the underlying model. Interest in chaos has
fuelled much work on the first of these two issues; this paper focuses on
the second. A new approach to quantifying state-dependent model error, the
local model drift, is derived and deployed both in examples and in
operational numerical weather prediction models. A simple law is derived
to relate model error to likely shadowing performance (how long the model
can stay close to the observations). Imperfect model experiments are used
to contrast the performance of truncated models relative to a high
resolution run, and the operational model relative to the analysis. In
both cases the component of forecast error due to state-dependent model
error tends to grow as the square-root of forecast time, and provides a
major source of error out to three days. These initial results suggest
that model error plays a major role and calls for further research in
quantifying both the local model drift and expected shadowing times. |
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