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Impact of a Time-dependent Background Error Covariance Matrix on Air Quality Analysis : Volume 5, Issue 5 (06/09/2012)

By Jaumouillé, E.

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Book Id: WPLBN0004009285
Format Type: PDF Article :
File Size: Pages 16
Reproduction Date: 2015

Title: Impact of a Time-dependent Background Error Covariance Matrix on Air Quality Analysis : Volume 5, Issue 5 (06/09/2012)  
Author: Jaumouillé, E.
Volume: Vol. 5, Issue 5
Language: English
Subject: Science, Geoscientific, Model
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2012
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Cariolle, D., Piacentini, A., Peuch, V., Massart, S., & Jaumouillé, E. (2012). Impact of a Time-dependent Background Error Covariance Matrix on Air Quality Analysis : Volume 5, Issue 5 (06/09/2012). Retrieved from http://worldebookfair.com/


Description
Description: CERFACS/CNRS-URA 1875, 31057 Toulouse, France. In this article we study the influence of different characteristics of our assimilation system on surface ozone analyses over Europe. Emphasis is placed on the evaluation of the background error covariance matrix (BECM). Data assimilation systems require a BECM in order to obtain an optimal representation of the physical state. A posteriori diagnostics are an efficient way to check the consistency of the used BECM. In this study we derived a diagnostic to estimate the BECM. On the other hand, an increasingly used approach to obtain such a covariance matrix is to estimate it from an ensemble of perturbed assimilation experiments. We applied this method, combined with variational assimilation, while analysing the surface ozone distribution over Europe. We first show that the resulting covariance matrix is strongly time (hourly and seasonally) and space dependent. We then built several configurations of the background error covariance matrix with none, one or two of its components derived from the ensemble estimation. We used each of these configurations to produce surface ozone analyses. All the analyses are compared between themselves and compared to assimilated data or data from independent validation stations. The configurations are very well correlated with the validation stations, but with varying regional and seasonal characteristics. The largest correlation is obtained with the experiments using time- and space-dependent correlation of the background errors. Results show that our assimilation process is efficient in bringing the model assimilations closer to the observations than the direct simulation, but we cannot conclude which BECM configuration is the best. The impact of the background error covariances configuration on four-days forecasts is also studied. Although mostly positive, the impact depends on the season and lasts longer during the winter season.

Summary
Impact of a time-dependent background error covariance matrix on air quality analysis

Excerpt
Airbase: The European air quality database (version 5),\\ http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-3, \\last access: 22 February 2012, 2011.; Barret, B., Ricaud, P., Mari, C., Attié, J.-L., Bousserez, N., Josse, B., Le Flochmoën, E., Livesey, N. J., Massart, S., Peuch, V.-H., Piacentini, A., Sauvage, B., Thouret, V., and Cammas, J.-P.: Transport pathways of CO in the African upper troposphere during the monsoon season: a study based upon the assimilation of spaceborne observations, Atmos. Chem. Phys., 8, 3231–3246, doi:10.5194/acp-8-3231-2008, 2008.; Belo P}ereira, M. and Berre, L.: The use of an Ensemble approach to study the Background Error Covariances in a Global \uppercase{NWP model, Mon. Wea. Rev., 134, 2466–2489, 2006.; Blond, N. and Vautard, R.: Three-dimensional ozone analyses and their use for short-term ozone forecasts, J. Geophys. Res, 109, D17303, doi:10.1029/2004JD004515, 2004.; Blond, N., Bel, L., and Vautard, R.: Three-dimensional ozone data analysis with an air quality model over the Paris area, J. Geophys. Res, 108, 1993–1996, 2003.; Buehner, M.: Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting, Q. J. Roy. Meteorol. Soc., 131, 1013–1043, 2005.; Buis, S., Piacentini, A., and D{é}clat, D.: PALM: a computational framework for assembling high-performance computing applications, Concurrency and computation: practice and experience, 18, 231–245, 2006.; Claeyman, M., Attié, J.-L., El Amraoui, L., Cariolle, D., Peuch, V.-H., Teyssèdre, H., Josse, B., Ricaud, P., Massart, S., Piacentini, A., Cammas, J.-P., Livesey, N. J., Pumphrey, H. C., and Edwards, D. P.: A linear CO chemistry parameterization in a chemistry-transport model: evaluation and application to data assimilation, Atmos. Chem. Phys., 10, 6097–6115, doi:10.5194/acp-10-6097-2010, 2010.; Coman, A., Foret, G., Beekmann, M., Eremenko, M., Dufour, G., Gaubert, B., Ung, A., Schmechtig, C., Flaud, J.-M., and Bergametti, G.: Assimilation of IASI partial tropospheric columns with an Ensemble Kalman Filter over Europe, Atmos. Chem. Phys., 12, 2513–2532, doi:10.5194/acp-12-2513-2012, 2012.; Constantinescu, E., Sandu, A., Chai, T., and Carmichael, G.: Ensemble-based chemical data assimilation. I: General approach, Q. J. Roy. Meteorol. Soc, 133, 1229–1243, 2007.; Daley, R.: Atmospheric data analysis, Cambridge atmospheric and space sciences series, Cambridge University Press, 457, 1991.; Delmas, R., M{é}gie, G., and Peuch, V.: Physique et chimie de l'atmosph{è}re, Belin, 608, 2005.; Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. Roy. Meteorol. Soc., 131, 3385–3396, 2007.; Elbern, H. and Schmidt, H.: Ozone episode analysis by four-dimensional variational chemistry data assimilation, J. Geophys. Res., 106, 3569–3590, 2001.; Desroziers, G., Berre, L., Pannekoucke, O., Stefanescu, S., Brousseau, P., Auger, L., Chapnik, B., and Raynaud, L.: Flow-dependent error covariances from variational assimilation ensembles on global and regional domains, Hirlam Technical ReportY, 68, 2–22, 2008.; El Amraoui, L., Attié, J.-L., Semane, N., Claeyman, M., Peuch, V.-H., Warner, J., Ricaud, P., Cammas, J.-P., Piacentini, A., Josse, B., Cariolle, D., Massart, S., and Bencherif, H.: Midlatitude stratosphere – troposphere exchange as diagnosed by MLS O3 and MOPITT CO assimilated fields, Atmos. Chem. Phys., 10, 2175–

 

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