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Kahnert, Michael (2011)
Publisher: Tellus B
Journal: Tellus B
Languages: English
Types: Article

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics
A multivariate variational data assimilation scheme for the Multiple-scale Atmospheric Transport and CHemistry (MATCH) model is presented and tested. A spectral, non-separable approach is chosen for modelling the background error constraints. Three different methods are employed for estimating background error covariances, and their analysis performances are compared. Observation operators for aerosol optical parameters are presented for externally mixed particles. The assimilation algorithm is tested in conjunction with different background error covariance matrices by analysing lidar observations of aerosol backscattering coefficient. The assimilation algorithm is shown to produce analysis increments that are consistent with the applied background error statistics. Secondary aerosol species show no signs of chemical relaxation processes in sequential assimilation of lidar observations, thus indicating that the data analysis result is well balanced. However, both primary and secondary aerosol species display emission- and advection-induced relaxations.DOI: 10.1111/j.1600-0889.2008.00377.x
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    • Andersson, C., Langner, J. and Bergstro¨m, R. 2007. Interannual variation and trends in air pollution over Europe due to climate variability during 1958-2001 simulated with a regional CTM coupled to the ERA40 reanalysis. Tellus 59B, 77-98.
    • Benedetti, A. and Fisher, M. 2007. Background error statistics for aerosols. Q. J. R. Meteorol. Soc. 133, 391-405.
    • Berre, L. 2000. Estimation of synoptic and mesoscale forecast error covariances in a limited-area model. Mon. Wea. Rev. 128, 644- 667.
    • Bruggeman, D. A. G. 1935. Berechnung verschiedener physikalischer Konstanten von heterogenen Substanzen. 1. Dielektrizita¨tskonstanten und vinnova Leitfa¨higkeiten der Mischko¨rper aus isotropen Substanzen. Ann. Phys. 24, 636-664.
    • Chy´lek, P., Videen, G., Geldart, D. J. W., Dobbie, J. S. and Tso, H. C. W. 2000. Effective medium approximations for heterogeneous particles. In: Light Scattering by Nonspherical Particles (eds. M. I. Mishchenko, J. W. Hovenier and L. D. Travis, Academic Press, San Diego , 274- 308.
    • Collins, W. D., Rasch, P. J., Eaton, B. E., Khattatov, B. V. and Lamarque, J.-F. 2001. Simulating aerosols using a chemical transport methodology for INDOEX. J. Geophys. Res. 106, 7313-7336.
    • Constantinescu, E. M., Sandu, A., Chai, T. and Carmichael, G. R. 2007a. Ensemble-based chemical data assimilation. I: ceneral approach. Q. J. R. Meteorol. Soc. 133, 1229-1243.
    • Constantinescu, E. M., Sandu, A., Chai, T. and Carmichael, G. R. 2007b. Ensemble-based chemical data assimilation. II: Covariance localization. Q. J. R. Meteorol. Soc. 133, 1245-1256.
    • Courtier, P., The´paut, J.-N. and Hollingsworth, A. 1994. A strategy for operational implementation of 4d-var using an incremental approach. Q. J. R. Meteorol. Soc. 120, 1367-1388.
    • Courtier, P., Andersson, E., Heckley, W., Pailleux, J., Vasiljevic´, D., and co-authors. 1998. The ECMWF implementation of three-dimensional variational assimilation (3D-Var). I: formulation. Q. J. R. Meteorol. Soc. 124, 1783-1807.
    • Daley, R. 1991. Atmospheric Data Analysis. Cambridge University Press, Cambridge, UK .
    • Derber, J. and Bouttier, F. 1999. A reformulation of the background error covariance in the ECMWF global data assimilation system. Tellus 51A, 195-221.
    • Dubovik, O., Holben, B. N., Lapyonok, T., Sinyuk, A., Mishchenko, M. I., and co-authors. 2002. Non-spherical aerosol retrieval method employing light scattering by spheroids. Geophys. Res. Lett. 29, doi: 10.1029/2001GL014506.
    • Elbern, H. and Schmidt, H. 1999. A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling. J. Geophys. Res. 104, 18 583-18 598.
    • Elbern, H. and Schmidt, H. 2001. Ozone episode analysis by fourdimensional variational chemistry data assimilation. J. Geophys. Res. 106, 3569-3590.
    • Elbern, H., Schmidt, H. and Ebel, A. 1997. Variational data assimilation for tropospheric chemistry modeling. J. Geophys. Res. 102, 15 967- 15 985.
    • Elbern, H., Schmidt, H., Talagrand, O. and Ebel, A. 2000. 4D-variational data assimilation with and adjoint air quality model for emission analysis. Environ. Model. Software 15, 539-548.
    • Elbern, H., Strunk, A., Schmidt, H. and Talagrand, O. 2007. Emission rate and chemical state estimation by 4-dimensional variational inversion. Atmos. Chem. Phys. 7, 3749-3769.
    • Evensen, G. 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn. 53, 343-367.
    • Fisher, M. 2003. Background error covariance modelling. In: Proceedings of the Seminar on Recent Developments in Data Assimilation for Atmosphere and Ocean, 8-12 September 2003, Reading, UK, pp. 45-64.
    • Fisher, M. 2006. Wavelet Jb-a new way to model the statistics of background errors. ECMWF Newslett., ECMWF, Reading, UK, 106, 23-28.
    • Fisher, M. and Lary, D. J. 1995. Lagrangian four-dimensional variational data assimilation of chemical species. Q. J. R. Meteorol. Soc. 121, 1681-1704.
    • Foltescu, V., Pryor, S. C. and Bennet, C. 2005. Sea salt generation, dispersion and removal on the regional scale. Atmos. Environ. 39, 2123-2133.
    • Greiner, W. and Mu¨ller, B. 1994. Quantum Mechanics (Symmetries). Springer, Berlin .
    • Gustafsson, N. 2007. Discussion on '4-D-Var or ensemble Kalman filter?'. Tellus 59A, 774-777.
    • Gustafsson, N., Berre, L., Ho¨rnquist, S., Huang, X.-Y., Lindskog, M., and co-authors. 2001. Three-dimensional variational data assimilation for a limited area model part I: general formulation and the background error constraint. Tellus 53A, 425-446.
    • Gustafsson, O. K. S., Persson, R. and Thorin, E. 2006. Use of lidar measurements of aerosol extinction and backscatter coefficients as a part of assessing data from meteorological forecast models and scattering calculations. In: Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing II. (ed. U. N. Singh), Proceeding of SPIE Vol. 6367, 636710, doi: 10.1117/12.689919 (2006).
    • Hamill, T. M. and Snyder, C. 2000. A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev. 128, 2905-2919.
    • Hollingsworth, A. and Lo¨nnberg, P. 1986. The statistical structure of short-range forecast errors as determined from radiosonde data. part I: the wind field. Tellus 38A, 111-136.
    • Kahnert, F. M. 2003. Numerical methods in electromagnetic scattering theory. J. Quant. Spectrosc. Radiat. Transfer 79-80, 775-824.
    • Kalnay, E., Li, H., Miyoshi, T., Yang, S.-C. and Ballabrera-Poy, J. 2007a. 4-D-Var or ensemble Kalman filter? Tellus 59A, 758-773.
    • Kalnay, E., Li, H., Miyoshi, T., Yang, S.-C. and Ballabrera-Poy, J. 2007b. Response to the discussion on “4-D-Var or ensemble Kalman filter?” by Nils Gustafsson. Tellus 59A, 778-780.
    • Khattatov, B. V., Lamarque, J.-F., Lyjak, L. V., Menard, R., Levelt, and co-authors. 2000. Assimilation of satellite observations of long-lived chemical species in global chemistry transport models. J. Geophys. Res. 105, 29 135-29 144.
    • Kupiainen, K. and Klimont, Z. 2004. Primary emissions of submicron and carbonaceous particles in Europe and the potential for their control, Technical Report IR-04-079, IIASA, Laxenburg, Austria.
    • Kupiainen, K. and Klimont, Z. 2007. Primary emissions of fine carbonaceous particles in Europe. Atmos. Environ. 41, 2156-2170.
    • Levelt, P. F., Khattatov, B. V., Gille, J. C., Brasseur, G. P. and Tie, X. X. 1998. Assimilation of MLS ozone measurements in the global threedimensional transport model ROSE. J. Geophys. Res. 25, 4493-4496.
    • Lindskog, M., Gustafsson, N., Navascue´s, B., Mogensen, K. S., Huang, X.-Y., and co-authors. 2001. Three-dimensional variational data assimilation for a limited area model part II: observation handling and assimilation experiments. Tellus 53A, 447-468.
    • Lorenc, A. C. 1986. Analysis methods for numerical weather prediction. Q. J. R. Meteorol. Soc. 112, 1177-1194.
    • Maxwell-Garnett, J. C. 1904. Colours in metal glasses and in metallic films. Philos. Trans. R. Soc. A 203, 385-420.
    • Me´nard, R. and Chang, L.-P. 2000. Assimilation of stratospheric chemical tracer observations using a Kalman filter. part II: χ 2-validated results and analysis of variance and correlation dynamics. Mon. Wea. Rev. 128, 2672-2686.
    • Me´nard, R., Yang, Y. and Polavarapu, S. 2004, Model error estimation: its application to chemical data assimilation, Proceedings of Workshop on Modelling and Assimilation for the Stratosphere and Tropopause, ECMWF/SPARC, pp. 137-145.
    • Mie, G. 1908. Beitra¨ge zur Optik tru¨ber Medien, speziell kolloidaler Metallo¨sungen. Ann. Phys. 25, 377-445.
    • Mishchenko, M. I., Travis, L. D. and co-authors.Lacis, A. A. 2002. Scattering, Absorption, and Emission of Light by Small Particles. Cambridge University Press, Cambridge.
    • Neusu¨ß, C., Wex, H., Birmili, W., Wiedensohler, A., Koziar, C., and 2002. Characterization and parametrization of atmospheric particle number-, mass-, and chemical-size distributions in central Europe during LACE 98 and MINT. J. Geophys. Res. 107, doi:10.1029/2001JD000514.
    • Pannekoucke, O., Berre, L. and Desroziers, G. 2007. Filtering properties of wavelets for local background-error correlations. Q. J. R. Meteorol. Soc. 133, 363-379.
    • Parrish, D. F. and Derber, J. C. 1992. The National Meteorological Centre's spectral statistical interpolation analysis system. Mon. Wea. Rev. 120, 1747-1763.
    • Polavarapu, S., Ren, S., Rochon, Y., Sankey, D., Ek, N., and co-authors. 2005. Data assimilation with the Canadian middle atmosphere model. Atmos. Ocean 43, 77-100.
    • Rasch, P. J., Collins, W. D. and Eaton, B. E. 2001. Understanding the Indian Ocean experiment (INDOEX) aerosol distributions with an aerosol assimilation. J. Geophys. Res. 106, 7337-7355.
    • Robertson, L. and Langner, J. 1992. Source function estimate by means of variational data assimilation applied to the ETEX-I tracer experiment. Atmos. Environ. 32, 4219-4225.
    • Robertson, L., Langner, J. and Enghardt, M. 1999. An Eulerian limitedarea atmospheric transport model. J. Appl. Meteorol. 38, 190-210.
    • Sandu, A., Liao, W., adn D. K., Henze, G. R. C. and Seinfeld, J. H. 2005. Inverse modeling of aerosol dynamics using adjoints: theoretical and numerical considerations. Aerosol. Sci. Technol. 39, 677-694.
    • Saunders, R. E., Andersson, E., Ja¨rvinen, H., Ge´rard, E., Rohn, M. and co-authors. 1998. Recent improvements to the ECMWF 4DVar data assimilation system. ECMWF Newslett. 81, 2-7.
    • Selesnick, I. W., Baraniuk, R. G. and Kingsbury, N. G. 2005. The dualtree complex wavelet transform. IEEE Signal Process. Mag. 22, 123- 151.
    • Talagrand, O. and Courtier, P. 1987. Variational assimilation of meteorological observations with the adjoint vorticity equation. I: theory. Q. J. R. Meteorol. Soc. 113, 1311-1328.
    • Toon, O. B. and Ackermann, T. P. 1981. Algorithms for the calculation of scattering by stratified spheres. Appl. Opt. 20, 3657-3660.
    • van Loon, M., Builtjes, P. J. H. and Segers, A. J. 2000. Data assimilation of ozone in the atmospheric transport chemistry model LOTOS. Environ. Model. Software 15, 603-609.
    • Wang, K.-Y., Lary, D. J., Shallcross, D. E., Hall, S. M. and Pyle, J. A. 2001. A review on the use of the adjoint method in four-dimensional atmospheric-chemistry data assimilation. Q. J. R. Meteorol. Soc. 127, 2181-2204.
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