New Method of Spatial Extrapolation of Meteorological Fields on the Mesoscale Level Using a Kalman Filter Algorithm for a Four-Dimensional Dynamic–Stochastic Model

V. S. Komarov Institute of Atmospheric Optics of the SB RAS, Tomsk, Russia

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A. V. Lavrinenko Institute of Atmospheric Optics of the SB RAS, Tomsk, Russia

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A. V. Kreminskii Institute of Atmospheric Optics of the SB RAS, Tomsk, Russia

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N. Ya Lomakina Institute of Atmospheric Optics of the SB RAS, Tomsk, Russia

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Yu B. Popov Institute of Atmospheric Optics of the SB RAS, Tomsk, Russia

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A. I. Popova Institute of Atmospheric Optics of the SB RAS, Tomsk, Russia

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Abstract

A new method and an algorithm of spatial extrapolation of mesometeorological fields to a territory uncovered with observations are suggested. The algorithm uses a linear Kalman filter for a four-dimensional dynamic–stochastic model of space–time variations of the atmospheric parameters. The results of statistical estimation of the quality of the algorithm used for spatial extrapolation of mesoscale temperature and wind velocity fields are discussed.

Corresponding author address: V. S. Komarov, Institute of Atmospheric Optics of the SB RAS, 1, Akademicheskii Ave., Tomsk 634055, Russia. Email: popov@iao.ru

Abstract

A new method and an algorithm of spatial extrapolation of mesometeorological fields to a territory uncovered with observations are suggested. The algorithm uses a linear Kalman filter for a four-dimensional dynamic–stochastic model of space–time variations of the atmospheric parameters. The results of statistical estimation of the quality of the algorithm used for spatial extrapolation of mesoscale temperature and wind velocity fields are discussed.

Corresponding author address: V. S. Komarov, Institute of Atmospheric Optics of the SB RAS, 1, Akademicheskii Ave., Tomsk 634055, Russia. Email: popov@iao.ru

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  • Bryukhan, F. F., 1983: Methods of Climatic Processing and Analysis of Aerological Information. Gidrometeoizdat, 112 pp.

  • Cohn, S. E., 1997: An introduction to estimation theory. J. Meteor. Soc. Japan, 75 , 257288.

  • Courtier, P., 1997: Variational methods. J. Meteor. Soc. Japan, 75 , 211218.

  • Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53 , 343367.

  • Gandin, L. S., and Kagan R. L. , 1976: Statictics in Methods of Meteorological Data Interpretation. Gidrometeoizdat, 359 pp.

  • Ghil, M., and Malanotte-Rizzolli P. , 1991: Data assimilation in meteorology and oceanography. Advances in Geophysics, Vol. 33, Academic Press, 41–266.

    • Search Google Scholar
    • Export Citation
  • Gustavsson, N., 1981: A review of methods for objective analysis. Dynamic Meteorology: Data Assimilation Methods, L. Bengtsson, M. Ghil, and E. Källén, Eds., Springer-Verlag, 17–76.

    • Crossref
    • Export Citation
  • Houtekamer, P. L., and Mitchell H. L. , 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126 , 796811.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ide, K. R., Ghil M. , and Lorenc A. C. , 1997: Unified notation for data assimilation: Operational, sequential and variational. J. Meteor. Soc. Japan, 75 , 181189.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Komarov, V. S., Kreminskii A. V. , and Popov Yu B. , 1998: Applications of an integrated forecasting procedure to spatial extrapolation of mesometeorological fields to a territory uncovered with observations. Atmos. Oceanic Opt., 11 , 808819.

    • Search Google Scholar
    • Export Citation
  • Komarov, V. S., Popov Yu B. , Suvorov S. S. , and Kurakov V. A. , 2004a: Dynamic-Stoshastic Methods with Their Application to Meteorology. Publishing House of the Institute of Atmosphere Optics SB RAS, 236 pp.

    • Search Google Scholar
    • Export Citation
  • Komarov, V. S., Il’in S. N. , Kreminskii A. V. , Lomakina N. Ya , Popov Yu B. , Popova A. I. , and Suvorov S. S. , 2004b: Estimation and extrapolation of the atmospheric state parameters on the mesoscale level using a Kalman filter algorithm. J. Atmos. Oceanic Technol., 21 , 488494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Le Dimet, F. X., and Talagrand O. , 1985: Variational algorithms for analysis and assimilation of meteorological observations. Tellus, 38A , 97110.

    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., 1997: Development of an operational variational assimilation scheme. J. Meteor. Soc. Japan, 75 , 339346.

  • Lorenc, A., Rutherford I. , and Larsen G. , 1977: The ECMWF analysis and data assimilation scheme: Analysis of mass and wind field. ECMWF Tech. Rep. 6, 47 pp.

  • Sage, A. P., and Melsa J. L. , 1971: Estimation Theory with Application to Communication and Control. McGraw-Hill, 496 pp.

  • Schlatter, T. W., 2000: Variational assimilation of meteorological observations in the lower atmosphere: A tutorial on how it works. J. Atmos. Sol. Terr. Phys., 62 , 10571070.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • WMO, 1984: Guide to meteorological instrument and observing practices. WMO Rep., 130 pp.

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