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Assimilation of Surface No2 and O3 Observations Into the Silam Chemistry Transport Model : Volume 7, Issue 4 (15/08/2014)

By Vira, J.

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Book Id: WPLBN0004009722
Format Type: PDF Article :
File Size: Pages 33
Reproduction Date: 2015

Title: Assimilation of Surface No2 and O3 Observations Into the Silam Chemistry Transport Model : Volume 7, Issue 4 (15/08/2014)  
Author: Vira, J.
Volume: Vol. 7, Issue 4
Language: English
Subject: Science, Geoscientific, Model
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Sofiev, M., & Vira, J. (2014). Assimilation of Surface No2 and O3 Observations Into the Silam Chemistry Transport Model : Volume 7, Issue 4 (15/08/2014). Retrieved from

Description: Finnish Meteorological Institute, Helsinki, Finland. This paper describes assimilation of trace gas observations into the chemistry transport model SILAM using the 3D-Var method. Assimilation results for year 2012 are presented for the prominent photochemical pollutants ozone (O3) and nitrogen dioxide (NO2). Both species are covered by the Airbase observation database, which provides the observational dataset used in this study.

Attention is paid to the background and observation error covariance matrices, which are obtained primarily by iterative application of a posteriori diagnostics. The diagnostics are computed separately for two months representing summer and winter conditions, and further disaggregated by time of day. This allows deriving background and observation error covariance definitions which include both seasonal and diurnal variation. The consistency of the obtained covariance matrices is verified using Χ2 diagnostics.

The analysis scores are computed for a control set of observation stations withheld from assimilation. Compared to a free-running model simulation, the correlation coefficient for daily maximum values is improved from 0.8 to 0.9 for O3 and from 0.53 to 0.63 for NO2.

Assimilation of surface NO2 and O3 observations into the SILAM chemistry transport model

Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne, S., Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation, J. Geophys. Res., 114, D13205, 2009.; Blond, N. and Vautard, R.: Three-dimensional ozone analyses and their use for short-term ozone forecasts, J. Geophys. Res., 109, 1–14, 2004.; Bocquet, M.: Parameter-field estimation for atmospheric dispersion: application to the Chernobyl accident using 4D-Var, Q. J. Roy. Meteor. Soc., 138, 664–681, 2012.; Chai, T., Carmichael, G. R., Tang, Y., Sandu, A., Hardesty, M., Pilewskie, P., Whitlow, S., Browell, E. V., Avery, M. A., Nédélec, P., Merrill, J. T., Thompson, A. M., and Williams, E.: Four-dimensional data assimilation experiments with International Consortium for Atmospheric Research on transport and transformation ozone measurements, J. Geophys. Res., 112, 1–18, 2007.; Gilbert, J. C. and Lemaréchal, C.: Some numerical experiments with variable-storage quasi-Newton algorithms, Math. Program., 45, 407–435, 1989.; Constantinescu, E. M., Sandu, A., Chai, T., and Carmichael, G. R.: Assessment of ensemble-based chemical data assimilation in an idealized setting, Atmos. Environ., 41, 18–36, 2007.; Curier, R. L., Timmermans, R., Calabretta-Jongen, S., Eskes, H., Segers, A., Swart, D., and Schaap, M.: Improving ozone forecasts over Europe by synergistic use of the LOTOS-EUROS chemical transport model and in-situ measurements, Atmos. Environ., 60, 217–226, 2012.; Dee, D. P.: Bias and data assimilation, Q. J. Roy. Meteor. Soc., 131, 3323–3343, 2005.; Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. Roy. Meteor. Soc., 131, 3385–3396, 2005.; EEA: Air Pollution by Ozone Across Europe During Summer 2012, EEA Technical Report, 2013.; Elbern, H. and Schmidt, H.: Ozone episode analysis by four-dimensional variational chemistry data assimilation, J. Geophys. Res., 106, 3569–3590, 2001.; Elbern, H., Strunk, A., Schmidt, H., and Talagrand, O.: Emission rate and chemical state estimation by 4-dimensional variational inversion, Atmos. Chem. Phys., 7, 3749–3769, doi:10.5194/acp-7-3749-2007, 2007.; Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143–10162, 1994.; Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003.; Galperin, M.: The approaches to correct computation of airborne pollution advection, Gidrometeoizdat, XVII, 54–68, 2000.; Gandin, L. S.: Objective Analysis of Meteorological Fields, Gidrometeorologischeskoe Izdatel'stvo, Translated (1965) by Israel Programme for Scientific Translation, Jerusalem, Leningrad, 1963.; Gaubert, B., Coman, A., Foret, G., Meleux, F., Ung, A., Rouil, L., Ionescu, A., Candau, Y., and Beekmann, M.: Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model, Geosci. Model Dev., 7, 283–302, doi:10.5194/gmd-7-283-2014, 2014.; Gery, M. W., Whitten, G. Z., Killus, J. P., and Dodge, M. C.: A photochemical kinetics mechanism for urban and regional scale computer modeling, J. Geophys. Res., 94, 12925–12956, 1989.; Hollingsworth, B. A. and Lönnberg, P.: The statistical structure of short-range forecast errors as determined from radiosonde data, Part I: The wind field, Tellus A, 38, 111–136, 1986.; Huijnen, V., Eskes, H. J., Poupkou, A., Elbern, H., Boersma, K. F., Foret, G., Sofiev, M., Valdebenito, A., Flemming, J., Stein, O., Gross, A., Robertson, L., D'Isidoro, M., Kioutsio


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