• Arulampalam, M. S., S. Maskell, N. Gordon, and T. Clapp, 2002: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process., 50 , 174188.

    • Search Google Scholar
    • Export Citation
  • Bonan, G. B., 1996: A land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: Technical description and user’s guide. NCAR Tech. Note NCAR/TN-4171STR, 150 pp. [Available online at http://www.cgd.ucar.edu/tss/lsm/availability/technote.tar.Z.].

  • Bonan, G. B., K. W. Oleson, M. Vertenstein, S. Levis, X. B. Zeng, Y. J. Dai, R. E. Dickinson, and Z. L. Yang, 2002: The land surface climatology of the community land model coupled to the NCAR community climate model. J. Climate, 15 , 31233149.

    • Search Google Scholar
    • Export Citation
  • Choudhury, B. J., T. J. Schmugge, A. Chang, and R. W. Newton, 1979: Effect of surface-roughness on the microwave emission from soils. J. Geophys. Res., 84 , 56995706.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., and E. F. Wood, 2003: The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using ensemble Kalman filtering: A case study based on ESTAR measurements during SGP97. Adv. Water Resour., 26 , 137149.

    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., and Coauthors, 2004: The Hydrosphere State (Hydros) Satellite Mission: An Earth System Pathfinder for global mapping of soil moisture and land freeze/thaw. IEEE Trans. Geosci. Remote Sens., 42 , 21842195.

    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear QG model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 , 1014310162.

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

  • Gorenburg, I. P., D. McLaughlin, and D. Entekhabi, 2001: Scale-recursive estimation of precipitation at the TOGA COARE site. Adv. Water Resour., 24 , 941953.

    • Search Google Scholar
    • Export Citation
  • Gupta, V. K., and E. C. Waymire, 1993: A statistical analysis of mesoscale rainfall as a random cascade. J. Appl. Meteor., 32 , 251267.

    • Search Google Scholar
    • Export Citation
  • Hawk, K. L., and P. Eagleson, 1992: Climatology of station storm rainfall in the continental U.S.: Parameters of the Bartlett-Lewis and Poisson Rectangular Pulses models. Tech. Rep. 336, Department of Civil Engineering, Massachusetts Institute of Technology, 35 pp.

  • Jackson, T. J., and T. J. Schmugge, 1991: Vegetation effects on the microwave emission of soils. Remote Sens. Environ., 36 , 203212.

  • Jackson, T. J., D. M. Le Vine, A. Y. Hsu, A. Oldak, P. J. Starks, C. T. Swift, J. D. Isham, and M. Haken, 1999: Soil moisture mapping at regional scales using microwave radiometry: The Southern Great Plains Hydrology Experiment. IEEE Trans. Geosci. Remote Sens., 37 , 21362151.

    • Search Google Scholar
    • Export Citation
  • Jazwinsky, A. H., 1970: Stochastic Processes and Filtering Theory. Academic Press, 376 pp.

  • Margulis, S. A., and D. Entekhabi, 2001: Temporal disaggregation of satellite-derived monthly precipitation estimates and the resulting propagation of error in partitioning of water at the land surface. Hydrol. Earth Syst. Sci., 5 , 2738.

    • Search Google Scholar
    • Export Citation
  • Margulis, S. A., D. McLaughlin, D. Entekhabi, and S. Dunne, 2002: Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment. Water Resour. Res.,38, 1299, doi:10.1029/2001WR001114.

    • Search Google Scholar
    • Export Citation
  • Njoku, E. G., W. J. Wilson, S. H. Yueh, S. J. Dinardo, F. K. Li, T. J. Jackson, V. Lakshmi, and J. Bolten, 2002: Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99. IEEE Trans. Geosci. Remote Sens., 40 , 26592673.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., and R. D. Koster, 2003: Assessing the impact of horizontal error correlations in background fields on soil moisture estimation. J. Hydrometeor., 4 , 12291242.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., D. B. McLaughlin, and D. Entekhabi, 2002: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Wea. Rev., 130 , 103114.

    • Search Google Scholar
    • Export Citation
  • Rodriguez-Iturbe, I., D. Entekhabi, and R. L. Bras, 1991: Nonlinear dynamics of soil moisture at climate scales. 1: Stochastic analysis. Water Resour. Res., 27 , 18991906.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131 , 14851490.

    • Search Google Scholar
    • Export Citation
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Assessing the Performance of the Ensemble Kalman Filter for Land Surface Data Assimilation

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  • 1 Ralph Parsons Laboratory, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
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Abstract

The ensemble Kalman filter provides an easy-to-use, flexible, and efficient option for data assimilation problems. One of its attractive features in land surface applications is its ability to provide distributional information about variables, such as soil moisture, that can be highly skewed or even bimodal. The ensemble Kalman filter relies on normality approximations that improve its efficiency but can also compromise the accuracy of its distributional estimates. The effects of these approximations can be evaluated by comparing the conditional marginal distributions and moments estimated by the ensemble Kalman filter with those obtained from a sequential importance resampling (SIR) particle filter, which gives exact solutions for large ensemble sizes. Comparisons for two land surface examples indicate that the ensemble Kalman filter is generally able to reproduce nonnormal soil moisture behavior, including the skewness that occurs when the soil is either very wet or very dry. Its conditional mean estimates are very close to those generated by the SIR filter. Its higher-order conditional moments are somewhat less accurate than the means. Overall, the ensemble Kalman filter appears to provide a good approximation for nonlinear, nonnormal land surface problems, despite its dependence on normality assumptions.

Corresponding author address: Dennis McLaughlin, Bldg. 48-329, 15 Vassar Street, Cambridge, MA 02139. Email: dennism@mit.edu

Abstract

The ensemble Kalman filter provides an easy-to-use, flexible, and efficient option for data assimilation problems. One of its attractive features in land surface applications is its ability to provide distributional information about variables, such as soil moisture, that can be highly skewed or even bimodal. The ensemble Kalman filter relies on normality approximations that improve its efficiency but can also compromise the accuracy of its distributional estimates. The effects of these approximations can be evaluated by comparing the conditional marginal distributions and moments estimated by the ensemble Kalman filter with those obtained from a sequential importance resampling (SIR) particle filter, which gives exact solutions for large ensemble sizes. Comparisons for two land surface examples indicate that the ensemble Kalman filter is generally able to reproduce nonnormal soil moisture behavior, including the skewness that occurs when the soil is either very wet or very dry. Its conditional mean estimates are very close to those generated by the SIR filter. Its higher-order conditional moments are somewhat less accurate than the means. Overall, the ensemble Kalman filter appears to provide a good approximation for nonlinear, nonnormal land surface problems, despite its dependence on normality assumptions.

Corresponding author address: Dennis McLaughlin, Bldg. 48-329, 15 Vassar Street, Cambridge, MA 02139. Email: dennism@mit.edu

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