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| Titel |
Empirical correction techniques: analysis and applications to chaotically driven low-order atmospheric models |
| VerfasserIn |
I. Trpevski, L. Basnarkov, D. Smilkov, L. Kocarev |
| 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 ; 20, no. 2 ; Nr. 20, no. 2 (2013-03-07), S.199-206 |
| Datensatznummer |
250018958
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| Publikation (Nr.) |
 copernicus.org/npg-20-199-2013.pdf |
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| Zusammenfassung |
| Contemporary tools for reducing model error in weather and climate
forecasting models include empirical correction techniques. In this paper we
explore the use of such techniques on low-order atmospheric models. We first
present an iterative linear regression method for model correction that works
efficiently when the reference truth is sampled at large time intervals, which
is typical for real world applications. Furthermore we investigate two
recently proposed empirical correction techniques on Lorenz models with
constant forcing while the reference truth is given by a Lorenz system driven
with chaotic forcing. Both methods indicate that the largest increase in
predictability comes from correction terms that are close to the average value
of the chaotic forcing. |
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