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| Titel |
Understanding and forecasting polar stratospheric variability with statistical models |
| VerfasserIn |
C. Blume, K. Matthes |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 12, no. 13 ; Nr. 12, no. 13 (2012-07-02), S.5691-5701 |
| Datensatznummer |
250011293
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| Publikation (Nr.) |
 copernicus.org/acp-12-5691-2012.pdf |
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| Zusammenfassung |
| The variability of the north-polar stratospheric vortex is a prominent
aspect of the middle atmosphere. This work investigates a wide class
of statistical models with respect to their ability to model
geopotential and temperature anomalies, representing variability in
the polar stratosphere. Four partly nonstationary, nonlinear models
are assessed: linear discriminant analysis (LDA); a cluster method
based on finite elements (FEM-VARX); a neural network, namely the
multi-layer perceptron (MLP); and support vector regression
(SVR). These methods model time series by incorporating all
significant external factors simultaneously, including ENSO, QBO, the
solar cycle, volcanoes, to then quantify their statistical importance.
We show that variability in reanalysis data from 1980 to 2005 is
successfully modeled. The period from 2005 to 2011 can be hindcasted
to a certain extent, where MLP performs significantly better than the
remaining models. However, variability remains that cannot be
statistically hindcasted within the current framework, such as the
unexpected major warming in January 2009. Finally, the statistical
model with the best generalization performance is used to predict a
winter 2011/12 with warm and weak vortex conditions. A vortex
breakdown is predicted for late January, early February 2012. |
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