Article

Seeking for the rational basis of the Median Model: The optimal combination of multi-model ensemble results

Atmospheric Chemistry and Physics (Impact Factor: 4.88). 12/2007; 7(24). DOI: 10.5194/acpd-7-5701-2007
Source: DOAJ

ABSTRACT In this paper we present an approach for the statistical analysis of multi-model ensemble results. The models considered here are operational long-range transport and dispersion models, also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides. We first introduce the theoretical basis (with its roots sinking into the Bayes theorem) and then apply this approach to the analysis of model results obtained during the ETEX-1 exercise. We recover some interesting results, supporting the heuristic approach called "median model", originally introduced in Galmarini et al. (2004a, b). This approach also provides a way to systematically reduce (and quantify) model uncertainties, thus supporting the decision-making process and/or regulatory-purpose activities in a very effective manner.

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Available from: Angelo Riccio, Sep 05, 2015
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    • "Different sophisticated approaches exist, such as four-dimensional variational data assimilation (4DVAR) (Elbern et al., 2007; Zhang et al., 2008) and Ensemble Kalman Filtering (EnKF) (Eben et al., 2005; Barbu et al., 2009; Tang et al., 2011). Also, increasing use is made of ensemble median-based approaches (Riccio et al., 2007). While these techniques are potentially very powerful, they are also highly computationintensive , requiring either the implementation of a model adjoint, or the simultaneous integration of several tens of model ensemble members. "
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    ABSTRACT: We evaluate a Kalman Filter (KF) based adaptive regression method for the correction of deterministic air quality forecasts. In this method, corrected forecast concentrations are obtained by linear regression, using the free model forecast values as predictors, and estimating the regression coefficients dynamically by means of the KF technique. Basically, this method exploits the information regarding the mismatch between the deterministic forecast and observations of the prior period to calculate regression coefficients for the correction of the next forecast step. We considered model output generated by the deterministic regional air quality model AURORA over northern Belgium for the year 2007, together with observed values at a few tens of stations. It was found that, for daily mean PM10 concentrations, and averaged over the monitoring stations, the correction scheme reduced the root mean square error from 15.9 to 10.5 μg m−3, largely thanks to the bias reduction from 8.8 to 0.5 μg m−3. The correlation coefficient increased from 0.65 to 0.73. For daily maximum O3 concentrations, the root mean square error was reduced from 25.9 to 17.2 μg m−3, the bias from 7.9 to 0.2 μg m−3, and the correlation coefficient increased from 0.60 to 0.79. We also implemented a non-adaptive linear regression scheme to the same data. It was found that the adaptive regression method outperformed this simpler scheme consistently, demonstrating the relevance of the dynamic KF-based method for use in the correction of deterministic air quality forecasts.
    Atmospheric Environment 04/2012; 50:381-384. DOI:10.1016/j.atmosenv.2012.01.032 · 3.28 Impact Factor
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    • "For point (i) a direct comparison between individual models skills and ensemble mean model skills can be carried out using routine air quality observations. The better performance of the ensemble average or median has been shown in several recent studies for air quality (Delle Monache and Stull, 2003; Pagowski et al., 2005; McKeen et al., 2007; Van Loon et al., 2007; Schaap et al., submitted for publication) as well as for transport of passive tracers (Galmarini et al., 2004; Riccio et al., 2008). The evaluation of the relation between the spread of values obtained in the model simulations and the actual uncertainty in air quality simulation has received focus in a few studies (Delle Monache et al., 2006; Vautard et al., 2006). "
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    ABSTRACT: Recently several regional air quality projects were carried out to support the negotiation under the Clean Air For Europe (CAFE) programme by predicting the impact of emission control policies with an ensemble of models. Within these projects, CITYDELTA and EURODELTA, the fate of air quality at the scale of European cities or that of the European continent was studied using several models. In this article we focus on the results of EURODELTA. The predictive skill of the ensemble of models is described for ozone, nitrogen dioxide and secondary inorganic compounds, and the uncertainty in air quality modelling is examined through the model ensemble spread of concentrations.For ozone daily maxima the ensemble spread origin differs from one region to another. In the neighbourhood of cities or in mountainous areas the spread of predicted values does not span the range of observed data, due to poorly resolved emissions or complex-terrain meteorology. By contrast in Atlantic and North Sea coastal areas the spread of predicted values is found to be larger than the observations. This is attributed to large differences in the boundary conditions used in the different models. For NO2 daily averages the ensemble spread is generally too small compared with observations. This is because models miss highest values occurring in stagnant meteorology in stable boundary layers near cities. For secondary particulate matter compounds the simulated concentration spread is more balanced, observations falling nearly equiprobably within the ensemble, and the spread originates both from meteorology and aerosol chemistry and thermodynamics.
    Atmospheric Environment 10/2009; 43(31-43):4822-4832. DOI:10.1016/j.atmosenv.2008.09.083 · 3.28 Impact Factor
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