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Volume 9, issue 11
Geosci. Model Dev., 9, 3933–3959, 2016
https://doi.org/10.5194/gmd-9-3933-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 9, 3933–3959, 2016
https://doi.org/10.5194/gmd-9-3933-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model experiment description paper 08 Nov 2016

Model experiment description paper | 08 Nov 2016

Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0

Emanuele Emili et al.

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Beekmann, M. and Derognat, C.: Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the Atmospheric Pollution Over the Paris Area (ESQUIF) campaign, J. Geophys. Res., 108, 8559, https://doi.org/10.1029/2003JD003391, 2003.
Belo Pereira, M. and Berre, L.: The Use of an Ensemble Approach to Study the Background Error Covariances in a Global NWP Model, Mon. Weather Rev., 134, 2466–2489, 2006.
Bocquet, M.: Localization and the iterative ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 142, 1075–1089, https://doi.org/10.1002/qj.2711, 2016.
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013, 2013.
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This paper analyses methods to assimilate chemical measurements in air quality models. We developed a reduced-order atmospheric chemistry model, which was used to compare results from different assimilation algorithms. Using an ensemble variational method (4DEnVar), we exploited the dynamical information provided by hourly measurements of chemical concentrations to diagnose model biases and improve next-day forecasts for several species of interest for air quality.
This paper analyses methods to assimilate chemical measurements in air quality models. We...
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