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| Titel |
Correction of approximation errors with Random Forests applied to modelling of cloud droplet formation |
| VerfasserIn |
A. Lipponen, V. Kolehmainen, S. Romakkaniemi, H. Kokkola |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 6, no. 6 ; Nr. 6, no. 6 (2013-12-16), S.2087-2098 |
| Datensatznummer |
250085022
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| Publikation (Nr.) |
 copernicus.org/gmd-6-2087-2013.pdf |
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| Zusammenfassung |
| In atmospheric models, due to their computational time or resource
limitations, physical processes have to be simulated using reduced (i.e.
simplified) models. The use of a reduced model, however, induces errors to
the simulation results. These errors are referred to as approximation errors.
In this paper, we propose a novel approach to correct these approximation
errors. We model the approximation error as an additive noise process in the
simulation model and employ the Random Forest (RF) regression algorithm for
constructing a computationally low cost predictor for the approximation
error. In this way, the overall simulation problem is decomposed into two
separate and computationally efficient simulation problems: solution of the
reduced model and prediction of the approximation error realisation. The
approach is tested for handling approximation errors due to a reduced coarse
sectional representation of aerosol size distribution in a cloud droplet
formation calculation as well as for compensating the uncertainty caused by
the aerosol activation parameterization itself. The results show a
significant improvement in the accuracy of the simulation compared to the
conventional simulation with a reduced model. The proposed approach is rather
general and extension of it to different parameterizations or reduced process
models that are coupled to geoscientific models is a straightforward task.
Another major benefit of this method is that it can be applied to physical
processes that are dependent on a large number of variables making them
difficult to be parameterized by traditional methods. |
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