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| Titel |
Post-processing through linear regression |
| VerfasserIn |
B. Schaeybroeck, S. Vannitsem |
| 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 ; 18, no. 2 ; Nr. 18, no. 2 (2011-03-07), S.147-160 |
| Datensatznummer |
250013890
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| Publikation (Nr.) |
 copernicus.org/npg-18-147-2011.pdf |
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| Zusammenfassung |
Various post-processing techniques are compared for both deterministic and
ensemble forecasts, all based on linear regression between
forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related
to the effective correction of forecast error, the optimal variability of
the corrected forecast and multicollinearity. The regression
schemes under consideration include the ordinary least-square
(OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total
least-square method, a new geometric-mean regression (GM), a recently introduced
error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.
These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors.
For short forecast lead times, the number and choice of
predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At
intermediate lead times, linear regression is unable to provide
corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times
the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred. |
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