Editorial Type:
Article
Article Type:
Research Article

Correcting for Surface Pressure Background Bias in Ensemble-Based Analyses

Seung-Jong Baek University of Maryland, College Park, College Park, Maryland

Search for other papers by Seung-Jong Baek in
Current site
Google Scholar
PubMed Close
,
Istvan Szunyogh University of Maryland, College Park, College Park, Maryland

Search for other papers by Istvan Szunyogh in
Current site
Google Scholar
PubMed Close
,
Brian R. Hunt University of Maryland, College Park, College Park, Maryland

Search for other papers by Brian R. Hunt in
Current site
Google Scholar
PubMed Close
, and
Edward Ott University of Maryland, College Park, College Park, Maryland

Search for other papers by Edward Ott in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jul 2009
Collections:
Mathematical Advances in Data Assimilation (MADA)
DOI:
https://doi.org/10.1175/2008MWR2787.1
Page(s):
2349–2364
Restricted access

Abstract

Model error is the component of the forecast error that is due to the difference between the dynamics of the atmosphere and the dynamics of the numerical prediction model. The systematic, slowly varying part of the model error is called model bias. This paper evaluates three different ensemble-based strategies to account for the surface pressure model bias in the analysis scheme. These strategies are based on modifying the observation operator for the surface pressure observations by the addition of a bias-correction term. One estimates the correction term adaptively, while another uses the hydrostatic balance equation to obtain the correction term. The third strategy combines an adaptively estimated correction term and the hydrostatic-balance-based correction term. Numerical experiments are carried out in an idealized setting, where the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model is integrated at resolution T62L28 to simulate the evolution of the atmosphere and the T30L7 resolution Simplified Parameterization Primitive Equation Dynamics (SPEEDY) model is used for data assimilation. The results suggest that the adaptive bias-correction term is effective in correcting the bias in the data-rich regions, while the hydrostatic-balance-based approach is effective in data-sparse regions. The adaptive bias-correction approach also has the benefit that it leads to a significant improvement of the temperature and wind analysis at the higher model levels. The best results are obtained when the two bias-correction approaches are combined.

* Current affiliation: Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada.

+ Current affiliation: Department of Atmospheric Sciences, Texas A&M University, College Station, Texas.

Corresponding author address: Istvan Szunyogh, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77843-3150. Email: szunyogh@ariel.met.tamu.edu

This article included in the Mathematical Advances in Data Assimilation (MADA) special collection.

Abstract

Model error is the component of the forecast error that is due to the difference between the dynamics of the atmosphere and the dynamics of the numerical prediction model. The systematic, slowly varying part of the model error is called model bias. This paper evaluates three different ensemble-based strategies to account for the surface pressure model bias in the analysis scheme. These strategies are based on modifying the observation operator for the surface pressure observations by the addition of a bias-correction term. One estimates the correction term adaptively, while another uses the hydrostatic balance equation to obtain the correction term. The third strategy combines an adaptively estimated correction term and the hydrostatic-balance-based correction term. Numerical experiments are carried out in an idealized setting, where the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model is integrated at resolution T62L28 to simulate the evolution of the atmosphere and the T30L7 resolution Simplified Parameterization Primitive Equation Dynamics (SPEEDY) model is used for data assimilation. The results suggest that the adaptive bias-correction term is effective in correcting the bias in the data-rich regions, while the hydrostatic-balance-based approach is effective in data-sparse regions. The adaptive bias-correction approach also has the benefit that it leads to a significant improvement of the temperature and wind analysis at the higher model levels. The best results are obtained when the two bias-correction approaches are combined.

* Current affiliation: Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada.

+ Current affiliation: Department of Atmospheric Sciences, Texas A&M University, College Station, Texas.

Corresponding author address: Istvan Szunyogh, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77843-3150. Email: szunyogh@ariel.met.tamu.edu

This article included in the Mathematical Advances in Data Assimilation (MADA) special collection.

Save
Cover Monthly Weather Review
Monthly Weather Review

  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129 , 2884–2903.

  • Baek, S. J., B. R. Hunt, E. Kalnay, E. Ott, and I. Szunyogh, 2006: Local ensemble Kalman filtering in the presence of model bias. Tellus, 58A , 293–306.

    • Search Google Scholar
    • Export Citation

  • Bell, M. J., M. J. Martin, and N. K. Nichols, 2004: Assimilation of data into an ocean model with systematic errors near the equator. Quart. J. Roy. Meteor. Soc., 130 , 873–893.

    • Search Google Scholar
    • Export Citation

  • Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129 , 420–436.

    • Search Google Scholar
    • Export Citation

  • Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126 , 1719–1724.

    • Search Google Scholar
    • Export Citation

  • Carton, J. A., G. Chepurin, and X. Cao, 2000: A Simple Ocean Data Assimilation analysis of the global upper ocean 1950–95. Part I: Methodology. J. Phys. Oceanogr., 30 , 294–309.

    • Search Google Scholar
    • Export Citation

  • Chapman, S., and R. S. Lindzen, 1970: Atmospheric Tides. Gorden and Breach, 200 pp.

  • Chepurin, G. A., J. A. Carton, and D. Dee, 2005: Forecast model bias correction in ocean data assimilation. Mon. Wea. Rev., 133 , 1328–1342.

    • Search Google Scholar
    • Export Citation

  • Dee, D. P., and A. M. Da Silva, 1998: Data assimilation in the presence of forecast bias. Quart. J. Roy. Meteor. Soc., 124 , 269–295.

    • Search Google Scholar
    • Export Citation

  • Dee, D. P., and R. Todling, 2000: Data assimilation in the presence of forecast bias: The GEOS moisture analysis. Mon. Wea. Rev., 128 , 3268–3282.

    • Search Google Scholar
    • Export Citation

  • Derber, J. C., 1989: A variational continuous assimilation technique. Mon. Wea. Rev., 117 , 2437–2446.

  • Drécourt, J., H. Madsen, and D. Rosbjerg, 2006: Bias aware Kalman filters: Comparison and improvements. Adv. Water Resour., 29 , 707–718.

    • Search Google Scholar
    • Export Citation

  • Friedland, B., 1969: Treatment of bias in recursive filtering. IEEE Trans. Automat. Contrib., 14 , 359–367.

  • Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129 , 2776–2790.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126 , 796–811.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129 , 123–137.

    • Search Google Scholar
    • Export Citation

  • Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230 , 112–126.

    • Search Google Scholar
    • Export Citation

  • Keppenne, C. L., M. M. Rienecker, N. P. Kurkowski, and D. A. Adamec, 2005: Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction. Nonlinear Processes Geophys., 12 , 491–503.

    • Search Google Scholar
    • Export Citation

  • Lamarque, J. F., and Coauthors, 2004: Application of a bias estimator for the improved assimilation of Measurements of Pollution in the Troposphere (MOPITT) carbon monoxide retrievals. J. Geophys. Res., 109 , D16304. doi:10.1029/2003JD004466.

    • Search Google Scholar
    • Export Citation

  • Lorenz, E. N., and K. A. Emanuel, 1998: Optimal sites for supplementary weather observations: Simulation with a small model. J. Atmos. Sci., 55 , 399–414.

    • Search Google Scholar
    • Export Citation

  • Martin, M. J., M. J. Bell, and N. K. Nichols, 2002: Estimation of systematic error in an equatorial ocean model using data assimilation. Int. J. Numer. Methods Fluids, 40 , 435–444.

    • Search Google Scholar
    • Export Citation

  • Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A , 415–428.

  • Szunyogh, I., E. J. Kostelich, G. Gyarmati, D. J. Patil, and B. R. Hunt, 2005: Assessing a local ensemble Kalman filter: Perfect model experiments with the NCEP global model. Tellus, 57A , 528–545.

    • Search Google Scholar
    • Export Citation

  • Szunyogh, I., E. J. Kostelich, G. Gyarmati, E. Kalnay, B. R. Hunt, E. Ott, and J. A. Yorke, 2008: A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus, 60A , 113–130.

    • Search Google Scholar
    • Export Citation

  • Toth, Z., and M. Peña, 2007: Data assimilation and numerical forecasting with imperfect models: The mapping paradigm. Physica D, 230 , 146–158.

    • Search Google Scholar
    • Export Citation

  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130 , 1913–1924.

    • Search Google Scholar
    • Export Citation

  • Whitaker, J. S., G. P. Compo, X. Wei, and T. M. Hamill, 2004: Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev., 132 , 1190–1200.

    • Search Google Scholar
    • Export Citation

  • Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136 , 463–482.

    • Search Google Scholar
    • Export Citation

  • Zupanski, D., and M. Zupanski, 2006: Model error estimation employing an ensemble data assimilation approach. Mon. Wea. Rev., 134 , 1337–1354.

    • Search Google Scholar
    • Export Citation

  • Zupanski, M., 2005: Maximum likelihood ensemble filter: Theoretical aspects. Mon. Wea. Rev., 133 , 1710–1726.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 9879 8509 438
PDF Downloads 249 64 4