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| Titel |
Automatic generation of large ensembles for air quality forecasting using the Polyphemus system |
| VerfasserIn |
D. Garaud, V. Mallet |
| 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 ; 3, no. 1 ; Nr. 3, no. 1 (2010-01-18), S.69-85 |
| Datensatznummer |
250000795
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| Publikation (Nr.) |
 copernicus.org/gmd-3-69-2010.pdf |
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| Zusammenfassung |
| This paper describes a method to automatically generate a large ensemble of
air quality simulations. Such an ensemble may be useful for quantifying
uncertainty, improving forecasts, evaluating risks, identifying process
weaknesses, etc. The objective is to take into account all sources of
uncertainty: input data, physical formulation and numerical formulation. The
leading idea is to build different chemistry-transport models in the same
framework, so that the ensemble generation can be fully controlled. Large
ensembles can be generated with a Monte Carlo simulations that address at the
same time the uncertainties in the input data and in the model formulation.
This is achieved using the Polyphemus system, which is flexible enough to
build various different models. The system offers a wide range of options in
the construction of a model: many physical parameterizations, several
numerical schemes and different input data can be combined. In addition,
input data can be perturbed. In this paper, some 30 alternatives are
available for the generation of a model. For each alternative, the options
are given a probability, based on how reliable they are supposed to be. Each
model of the ensemble is defined by randomly selecting one option per
alternative. In order to decrease the computational load, as many
computations as possible are shared by the models of the ensemble. As an
example, an ensemble of 101 photochemical models is generated and run for the
year 2001 over Europe. The models' performance is quickly reviewed, and the
ensemble structure is analyzed. We found a strong diversity in the results of
the models and a wide spread of the ensemble. It is noteworthy that many
models turn out to be the best model in some regions and some dates. |
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