Editorial Type:
Article
Article Type:
Research Article

Estimation of Three-Dimensional Error Covariances. Part III: Height–Wind Forecast Error Correlation and Related Geostrophy

Qin Xu NOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Li Wei Cooperative Institute for Mesoscale Meteorological Studies and University of Oklahoma, Norman, Oklahoma

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Print Publication:
01 Apr 2002
DOI:
https://doi.org/10.1175/1520-0493(2002)130<1052:EOTDEC>2.0.CO;2
Page(s):
1052–1062
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Abstract

The method of statistical analysis of wind innovation (observation minus forecast) vectors is extended and applied to the innovation data collected over North America for a 3-month period from the Navy Operational Global Atmospheric Prediction System to estimate the height–wind forecast error correlation and to evaluate the related geostrophy. Both single-level and multilevel analyses are performed. The single-level analysis shows that the geostrophy is well satisfied in the middle troposphere but is not well satisfied in the boundary layer and around the tropopause. The multilevel analysis indicates that the cross correlation between height and tangential wind forecast errors at different vertical levels is not small and thus should not be neglected.

Corresponding author address: Dr. Qin Xu, National Severe Storms Laboratory, 1313 Halley Circle, Norman, OK 73069. Email: qin.xu@nssl.noaa.gov

Abstract

The method of statistical analysis of wind innovation (observation minus forecast) vectors is extended and applied to the innovation data collected over North America for a 3-month period from the Navy Operational Global Atmospheric Prediction System to estimate the height–wind forecast error correlation and to evaluate the related geostrophy. Both single-level and multilevel analyses are performed. The single-level analysis shows that the geostrophy is well satisfied in the middle troposphere but is not well satisfied in the boundary layer and around the tropopause. The multilevel analysis indicates that the cross correlation between height and tangential wind forecast errors at different vertical levels is not small and thus should not be neglected.

Corresponding author address: Dr. Qin Xu, National Severe Storms Laboratory, 1313 Halley Circle, Norman, OK 73069. Email: qin.xu@nssl.noaa.gov

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  • Bartello, P., and H. L. Mitchell, 1992: A continuous three-dimensional model of short-range forecast error covariances. Tellus, 44A , 217–235.

    • Search Google Scholar
    • Export Citation

  • Bergman, K. H., 1979: Multivariate analysis of temperature and winds using optimal interpolation. Mon. Wea. Rev., 107 , 1423–1444.

  • Berre, L., 2000: Estimation of synoptic and mesoscale forecast error covariances in a limited-area model. Mon. Wea. Rev., 128 , 644–667.

    • Search Google Scholar
    • Export Citation

  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.

  • Daley, R., and E. Barker, 2000: NRL Atmospheric Variational Data Assimilation System (NAVDAS) Source Book 2000. NRL Publ. NRL/PU/7530-00-418, 153 pp.

    • Search Google Scholar
    • Export Citation

  • Derber, J., and F. Bouttier, 1999: A reformulation of the background error covariance in the ECMWF global data assimilation system. Tellus, 51A , 195–221.

    • Search Google Scholar
    • Export Citation

  • Gandin, L. S., 1965: Objective Analysis of Meteorological Fields (in Russian). Israel Program for Scientific Translation, 242 pp.

  • Hogan, T. F., and T. E. Rosmond, 1991: The description of the Navy Operational Global Atmospheric Prediction System's spectral forecast model. Mon. Wea. Rev., 119 , 1786–1815.

    • Search Google Scholar
    • Export Citation

  • Hollingsworth, A., and P. Lönnberg, 1986: The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus, 38A , 111–136.

    • Search Google Scholar
    • Export Citation

  • Lönnberg, P., and A. Hollingsworth, 1986: The statistical structure of short-range forecast errors as determined from radiosonde data. Part II: The covariance of height and wind errors. Tellus, 38A , 137–161.

    • Search Google Scholar
    • Export Citation

  • Lorenc, A., 1981: A global three-dimensional multivariate statistical analysis system. Mon. Wea. Rev., 109 , 701–721.

  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120 , 1747–1763.

    • Search Google Scholar
    • Export Citation

  • Polavarapu, S. M., 1995: Divergent wind analysis in the oceanic boundary layer. Tellus, 47A , 221–239.

  • Rutherford, I. D., 1972: Data assimilation by statistical interpolation of forecast error fields. J. Atmos. Sci., 29 , 809–815.

  • Thiebaux, H. J., H. L. Mitchell, and D. W. Shantz, 1986: Horizontal structure of hemispheric forecast error correlations for geopotential and temperature. Mon. Wea. Rev., 114 , 1048–1066.

    • Search Google Scholar
    • Export Citation

  • Xu, Q., and L. Wei, 2001: Estimation of three-dimensional error covariances. Part II: Analysis of wind innovation vectors. Mon. Wea. Rev., 129 , 2939–2954.

    • Search Google Scholar
    • Export Citation

  • Xu, Q., L. Wei, A. Van Tuyl, and E. H. Barker, 2001: Estimation of three-dimensional error covariances. Part I: Analysis of height innovation vectors. Mon. Wea. Rev., 129 , 2126–2135.

    • Search Google Scholar
    • Export Citation
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