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Automatic generation of large ensembles for air quality forecasting using the Polyphemus system

Automatic generation of large ensembles for air quality forecasting using the Polyphemus system,10.5194/gmd-3-69-2010,Geoscientific Model Development,

Automatic generation of large ensembles for air quality forecasting using the Polyphemus system   (Citations: 2)
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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.
Journal: Geoscientific Model Development , vol. 3, no. 1, pp. 69-85, 2010
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    • ...Garaud and Mallet [2010] built the ensemble with several models randomly generated within the same platform and with perturbed input data...
    • ...2.1. Generation of a Large Ensemble [11] The method employed for the automatic generation of a large ensemble is described by Garaud and Mallet [2010]...
    • ...[12] In this paper, we rely on the same ensemble as Garaud and Mallet [2010]...
    • ...4o f 13 detail by Garaud and Mallet [2010]...

    D. Garaudet al. Automatic calibration of an ensemble for uncertainty estimation and pr...

    • ...[41] The models employed in this application (including the model with which the analyses are generated) are part of the ensemble introduced in Garaud and Mallet [2010]...
    • ...3.1.1. Generation of the Analyses [42] The model with which the analyses are generated (this model is referred to as the fourth reference model (R3) by Garaud and Mallet [2010]) uses the Regional Atmospheric Chemistry Mechanism (RACM) [Stockwell et al., 1997]...
    • ...[47] Out of the simulations of Garaud and Mallet [2010], 19 members are selected for inclusion in the ensemble...
    • ...Following the terminology of Garaud and Mallet [2010], these five models are the six “reference members”, except (R3)...

    Vivien Mallet. Ensemble forecast of analyses: Coupling data assimilation and sequenti...

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