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

  • Hacker, J. P., , and D. Rostkier-Edelstein, 2007: PBL state estimation with surface observation, a column model, and an ensemble filter. Mon. Wea. Rev., 135, 29582972.

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

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

    • Search Google Scholar
    • Export Citation
  • Komarov, V. S., , A. V. Lavrinenko, , A. V. Kreminskii, , N. Ya. Lomakina, , Yu. B. Popov, , and A. I. Popova, 2007: New method of spatial extrapolation of meteorological fields on the mesoscale level using a Kalman filter algorithm for a four-dimensional dynamic-stochastic model. J. Atmos. Oceanic Technol., 24, 182193.

    • Search Google Scholar
    • Export Citation
  • Lavrinenko, A. V., 2006: Investigation of dynamic-stochastic algorithm for ultra-short-term forecast of meteorological fields. J. Atmos. Oceanic Opt.,19, 919921.

    • Search Google Scholar
    • Export Citation
  • Lavrinenko, A. V., , V. S. Komarov, , and Yu. B. Popov, 2005: Technique of ultra-short-term forecast of atmospheric parameters on the basis of the Kalman filtering algorithm and a 2D dynamic-stochastic model. J. Atmos. Oceanic Opt.,18, 344348.

    • Search Google Scholar
    • Export Citation
  • Rémy, S., , and T. Bergot, 2010: Ensemble Kalman filter data assimilation in a 1D numerical model used for fog forecasting. Mon. Wea. Rev., 138, 17921810.

    • Search Google Scholar
    • Export Citation
  • Rostkier-Edelstein, D., , and J. P. Hacker, 2010: The roles of surface observation ensemble assimilation and model complexity for nowcasting of PBL profiles: A factor separation analysis. Wea. Forecasting, 25, 16701690.

    • Search Google Scholar
    • Export Citation
  • Rostkier-Edelstein, D., , and J. P. Hacker, 2013: Impact of flow dependence, column covariance, and forecast model type on surface-observation assimilation for probabilistic PBL profiles nowcasts. Wea. Forecasting, 28, 29–54.

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

  • Schroeder, A. J., , D. R. Stauffer, , N. L. Seaman, , A. Deng, , A. M. Gibbs, , G. K. Hunter, , and G. S. Young, 2006: An automated high-resolution, rapidly relocatable meteorological nowcasting and forecast system. Mon. Wea. Rev., 134, 12371265.

    • Search Google Scholar
    • Export Citation
  • Stauffer, D. R., , A. Deng, , A. M. Gibbs, , G. K. Hunter, , G. S. Young, , A. J. Schroeder, , and N. L. Seaman, 2004: An automated Humvee-operated meteorological nowcast-forecast system for the U.S. Army. Preprints, Workshop on Mesoscale and Microscale Meteorological Modeling for Military Applications, Jackson, MS, Jackson State University, AHPCRC. [Available online at http://brule.rwic.und.edu/~tilley/AHPCRC_Workshop.html.]

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 20 20 7
PDF Downloads 5 5 3

Application of a Dynamic-Stochastic Approach to Short-Term Forecasting of the Atmospheric Boundary Layer

View More View Less
  • 1 Institute of Atmospheric Optics of the Siberian Branch of the Russian Academy of Sciences, Tomsk, Russia
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

A two-dimensional, dynamic-stochastic model presented in this study is used for short-term forecasting of vertical profiles of air temperature and wind velocity orthogonal components in the atmospheric boundary layer (ABL). The technique of using a two-dimensional dynamic-stochastic model involves preliminary estimation of its coefficients using the Kalman filter (KF) algorithm and observations at only one measuring station. The results obtained can be useful for aviation meteorology, mobile meteorological systems deployed in regions uncovered or rarely covered by meteorological observations, and devices with limited computational resources. In addition, they can be useful for wind-power and pollutant dispersion applications. Two cases of experiments with real observations using a radiometer and sodar (Doppler radar) deployed in the region of Tomsk, Russia, and data of more frequent (4 times a day) radiosonde observations in the region of Omsk (station 28698) are examined. The forecast period of numerical weather prediction (NWP) for all cases considered in this study ranged from 0.5 to 6 h. The results obtained demonstrate higher forecast quality in comparison with the persistence forecast.

Corresponding author address: Andrey Lavrinenko, V. E. Zuev Institute of Atmospheric Optics, 1, Academician Zuev Sq., 634021 Tomsk, Russia. E-mail: and-rey80@yandex.ru

Abstract

A two-dimensional, dynamic-stochastic model presented in this study is used for short-term forecasting of vertical profiles of air temperature and wind velocity orthogonal components in the atmospheric boundary layer (ABL). The technique of using a two-dimensional dynamic-stochastic model involves preliminary estimation of its coefficients using the Kalman filter (KF) algorithm and observations at only one measuring station. The results obtained can be useful for aviation meteorology, mobile meteorological systems deployed in regions uncovered or rarely covered by meteorological observations, and devices with limited computational resources. In addition, they can be useful for wind-power and pollutant dispersion applications. Two cases of experiments with real observations using a radiometer and sodar (Doppler radar) deployed in the region of Tomsk, Russia, and data of more frequent (4 times a day) radiosonde observations in the region of Omsk (station 28698) are examined. The forecast period of numerical weather prediction (NWP) for all cases considered in this study ranged from 0.5 to 6 h. The results obtained demonstrate higher forecast quality in comparison with the persistence forecast.

Corresponding author address: Andrey Lavrinenko, V. E. Zuev Institute of Atmospheric Optics, 1, Academician Zuev Sq., 634021 Tomsk, Russia. E-mail: and-rey80@yandex.ru
Save