Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Kahnert, Michael (2011)
Publisher: Tellus B
Journal: Tellus B
Languages: English
Types: Article
Determining size-resolved chemical composition of aerosols is important for modelling the aerosols' direct and indirect climate impact, for source–receptor modelling, and for understanding adverse health effects of particulate pollutants. Obtaining this kind of information from optical remote sensing observations is an ill-posed inverse problem. It can be solved by variational data assimilation in conjunction with an aerosol transport model. One important question is how much information about the particles' physical and chemical properties is contained in the observations. We perform a numerical experiment to test the observability of size-dependent aerosol composition by remote sensing observations. An aerosol transport model is employed to produce a reference and a perturbed aerosol field. The perturbed field is taken as a proxy for a background estimate subject to uncertainties. The reference result represents the ‘true’ state of the system. Optical properties are computed from the reference results and are assimilated into the perturbed model. The assimilation results reveal that inverse modelling of optical observations significantly improves the background estimate. However, the optical observations alone do not contain sufficient information for producing a faithful retrieval of the size-resolved aerosol composition. The total mass mixing ratios, on the other hand, are retrieved with remarkable accuracy.DOI: 10.1111/j.1600-0889.2009.00436.x
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 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.
    • Collins, W. D., Rasch, P. J., Eaton, B. E., Khattatov, B. V. and Lamarque, J.-F. 2001. Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: 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: General approach. Q. J. Roy. 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. Roy. Meteorol. Soc. 133, 1245-1256.
    • Dockery, D., Pope, C., XU, X., Spengler, J., Ware, J., et al, 1993. An association between air-pollution and mortality in 6 United-States cities. N. Engl. J. Med. 329(24), 1753-1759.
    • Dusek, U., Frank, G. P., Hildebrandt, L., Curtius, J., Schneider, J., et al, 2006. Size matters more than chemistry for cloud-nucleation ability of aerosol particles. Science 312, 1375-1378.
    • 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.
    • Eleftheriadis, K., Colbeck, I., Housiadas, C., Lazaridis, M., Mihalopoulos, N., et al, 2006. Size distribution, composition and origin of the submicron aerosol in the marine boundary layer during the eastern mediterranean SUB-AERO experiment. Atmos. Env. 40, 6245-6260.
    • Evensen, G. 2007. Data Assimilation - the Ensemble Kalman Filter. Springer, Berlin.
    • Foltescu, V., Pryor, S. C. and Bennet, C. 2005. Sea salt generation, dispersion and removal on the regional scale. Atmos. Environ. 39, 2123-2133.
    • Forster, P., Ramaswamy, V., Artaxo, P., R. Betts, T. B., Fahey, D., et al, 2007. Changes in atmospheric constituents and in radiative forcing. In: Climate Change 2007: The Physical Science Basis, (eds. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tignor and H. Miller), Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmetal Panel on Climate Change, Cambridge University Press, Cambridge.
    • Gustafsson, N., Berre, L., Ho¨rnquist, S., Huang, X.-Y., Lindskog, M., et al, 2001. Three-dimensional variational data assimilation for a limited area model part I: General formulation and the background error constraint. Tellus 53A, 425-446.
    • Harrison, R. and Yin, J. 2000. Particulate matter in the atmosphere: which particle properties are important for its effects on health? Sci. Total Environ. 249(1-3), 85-101.
    • Hess, M., Koepke, P. and Schult, I. 1998. Optical properties of aerosols and clouds: The software package OPAC. Bull. Am. Met. Soc. 79, 831-844.
    • Jacobson, M. Z. 2001. Global direct radiative forcing due to multicomponent anthropogenic and natural aerosols. J. Geophys. Res. 106, 1551-1568.
    • Jazwinski, A. H. 2007. Stochastic Processes and Filtering Theory. Dover, Mineola.
    • Kahnert, F. M. 2004. Reproducing the optical properties of fine desert dust aerosols using ensembles of simple model particles. J. Quant. Spectrosc. Radiat. Transfer 85, 231-249.
    • Kahnert, M. 2008. Variational data analysis of aerosol species in a regional CTM: Background error covariance constraint and aerosol optical observation operators. Tellus 60B, 753-770.
    • Kahnert, M. and Kylling, A. 2004. Radiance and flux simulations for mineral dust aerosols: Assessing the error due to using spherical or spheroidal model particles. J. Geophys. Res. 109, D09203, doi:10.1029/2003JD004318, errata: doi:10.1029/2004JD005311.
    • Kahnert, M. and Nousiainen, T. 2006. Uncertainties in measured and modelled asymmetry parameters of mineral dust aerosols. J. Quant. Spectrosc. Radiat. Transfer 100, 173-178.
    • Kahnert, M., Nousiainen, T. and Veihelmann, B. 2005. Spherical and spheroidal model particles as an error source in aerosol climate forcing and radiance computations: A case study for feldspar aerosols. J. Geophys. Res. 110. doi: 10.1029/2004JD005558.
    • Kahnert, M., Nousiainen, T. and Ra¨isa¨nen, P. 2007. Mie simulations as an error source in mineral aerosol radiative forcing calculations. Q. J. R. Met. Soc. 133, 299-307.
    • Lohmann, U. and Lesins, G. 2002. Stronger constraints on the anthropogenic indirect aerosol effect. Science 298, 1012-1014.
    • Matta, E., Facchini, M., Decesari, S., Mircea, M., Cavalli, F., et al, 2003. Mass closure on the chemical species in size-segregated atmospheric aerosol collected in an urban area of the Po Valley, Italy. Atmos. Chem. Phys. 3, 623-637.
    • McFiggans, G., Artaxo, P., Baltensperger, U., Coe, H., Facchini, M. C., et al, 2006. The effect of physical and chemical aerosol properties on warm cloud droplet activation. Atmos. Chem. Phys. 6, 2593-2649.
    • Mishchenko, M. I., Cairns, B., Kopp, G., Schueler, C., Fafaul, B. A., et al, 2007. Accurate monitoring of terrestrial aerosols and total solar irradiance: Introducing the Glory mission. Bull. Am. Met. Soc. 88, 677-691.
    • Myhre, G. and Stordal, F. 2001. Global sensitivity experiments of the radiative forcing due to mineral aerosols. J. Geophys. Res. 106, 18193- 18204.
    • Nousiainen, T., Kahnert, M. and Veihelmann, B. 2006. Light scattering modeling of small feldspar aerosol particles using polyhedral prisms and spheroids. J. Quant. Spectrosc. Radiat. Transfer 101, 471-487.
    • Parrish, D. F. and Derber, J. C. 1992. The National Meteorological Centre's spectral statistical interpolation analysis system. Mon. Wea. Rev. 120, 1747-1763.
    • Penner, J. E., Dong, X. and Chen, Y. 2004. Observational evidence of a change in radiative forcing due to the indirect aerosol effect. Nature 427, 231-234.
    • Pope, C. A., Ezzati, M. and Dockery, D. W. 2009. Fine-particulate air pollution and life expectancy in the United States. N. Engl. J. Med. 360(4), 376-386.
    • Robertson, L., Langner, J. and Enghardt, M. 1999. An Eulerian limitedarea atmospheric transport model. J. Appl. Meteorol. 38, 190-210.
    • Rosenfeld, D. 2006. Aerosols, clouds, and climate. Science 312, 1323- 1324.
    • Sandu, A., Liao, W., Henze, D. K., Carmichael G. R. and Seinfeld, J. H. 2005. Inverse modeling of aerosol dynamics using adjoints: Theoretical and numerical considerations. Aerosol Sci. Technol. 39), 677-694.
    • Schulz, F. M., Stamnes, K. and Stamnes, J. J. 1998. Modeling the radiative transfer properties of media containing particles of moderately and highly elongated shape. Geophys. Res. Lett. 25, 4481-4484.
    • Schulz, F. M., Stamnes, K. and Stamnes, J. J. 1999. Shape-dependence of the optical properties in size-shape distributions of randomly oriented prolate spheroids, including highly elongated shapes. J. Geophys. Res. 104, 9413-9421.
    • Sokolik, I. N. and Toon, O. B. 1999. Incorporation of mineralogical composition into models of the radiative properties of mineral aerosols from UV to IR wavelengths. J. Geophys. Res. 104, 9423-9444.
    • Stier, P., Seinfeld, J. H., Kinne, S. and Boucher, O. 2007. Aerosol absorption and radiative forcing. Atmos. Chem. Phys. 7, 5237-5261.
    • Veihelmann, B., Nousiainen, T., Kahnert, M. and van der Zande, W. 2006. Light scattering by small feldspar particles simulated using the Gaussian random sphere geometry. J. Quant. Spectrosc. Radiat. Transfer 100(1-3), 393-405.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from