Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter

Journal of Hydrometeorology

Volumn 7, Issue 3, 2006, Pages 534-547

  Pan, Ming ^a   Wood, Eric F ^a  

^a Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ASSIMILATION; KALMAN FILTER; PRECIPITATION ASSESSMENT; WATER BUDGET;

GREAT PLAINS; NORTH AMERICA;

EID: 33746087111     PISSN: 1525755X     EISSN: None     Source Type: Journal    
DOI: 10.1175/JHM495.1     Document Type: Article

References (38)

1. Abdulla, F. A., D. P. Lettenmaier, E. F. Wood, and J. A. Smith, 1996: Application of a macroscale hydrologic model to estimate the water balance of the Arkansas-Red River basin. J. Geophys. Res., 101 (D3), 7449-7459.
2. Anderson, B. D. O., and J. B. Moore, 1979: Optimal Filtering. Vol. 1, Prentice-Hall, 357 pp.
3. Betts, A. K., and J. H. Ball, 2003: Evaluation of the ERA-40 surface water budget and surface temperature for the Mackenzie River basin. J. Hydrometeor., 4, 1194-1211.
4. Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724.
5. Crow, W. T., 2003: Correcting land surface model predictions for the impact of temporally sparse rainfall rate measurements using an ensemble Kalman filter and surface brightness temperature observations. J. Hydrometeor., 4, 960-973.
6. 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 Res., 26, 137-149.
7. Crow, W. T., and R. Bindlish, 2004: The impact of incorrect model error assumptions on the assimilation of remotely sensed surface soil moisture. Proc. Second Int. CAHMDA Workshop on the Terrestrial Water Cycle: Modelling and Data Assimilation across Catchment Scales, Princeton, NJ, Princeton University, 145-148.
8. Dunne, S., and D. Entekhabi, 2005: Estimation of soil moisture fields using multi-scale observations in an ensemble smoother-based four-dimensional land data assimilation system. Preprints, 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., CD-ROM, 1.10.
9. European Centre for Medium-Range Weather Forecasts, cited 2002: The ERA-40 Archive. [Available online at http://www.ecmwf.int/research/era/ index.html.]
10. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 143-10 162.
11. Geeter, J. D., H. V. Brussel, and J. D. Schutter, 1997: A smoothly constrained Kalman filter. IEEE Trans. Pattern Anal. Mach. Intell., 19, 1171-1177.
12. Gelb, A., 1974: Applied Optimal Estimation. MIT Press, 374 pp.
13. Ho, Y. C., and R. C. K. Lee, 1964: A Bayesian approach to problems in stochastic estimation and control. IEEE Trans. Autom. Control, 9, 333-339.
14. Jackson, T. J., A. Hsu, and P. O'Neill, 2002: Surface soil moisture retrieval and mapping using high frequency microwave satellite observation in the southern Great Plains. J. Hydrometeor., 3, 688-699.
15. Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. J. Basic Eng., 82, 35-45.
16. Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re-analysis Project. Bull. Amer. Meteor. Soc., 77, 437-471.
17. Kitanidis, P. K., and R. L. Bras, 1980: Real-time forecasting with a conceptual hydrologic model: 1. Analysis of uncertainty. Water Resour. Res., 16, 1025-1033.
18. Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for GCMs. J. Geophys. Res., 99 (D7), 14 415-14 428.
19. Liang, X., D. P. Lettenmaier, and E. F. Wood, 1996: One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model. J. Geophys. Res., 101 (D16), 21 403-21 422.
20. Liang, X., E. F. Wood, and D. P. Lettenmaier, 1999: Modeling ground heat flux in land surface parameterization schemes. J. Geophys. Res., 104 (D8), 9581-9600.
21. Maurer, E. P., G. M. O'Donnell, D. P. Lettenmaier, and J. O. Roads, 2001: Evaluation of the land surface water budget in NCEP/NCAR and NCEP/ DOE reanalyses using an off-line hydrologic model. J. Geophys. Res., 106 (D16), 17 841-17 862.
22. Maurer, E. P., A. Wood, D. P. Lettenmaier, and B. Nijssen, 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 3237-3251.
23. Maybeck, P. S., 1979: Stochastic Models, Estimation and Control. Vol. 1, Academic Press, 289 pp.
24. McLaughlin, D. B., 2002: An integrated approach to hydrologic data assimilation: Interpolation, smoothing, and filtering. Adv. Water Resour., 25, 1275-1286.
25. McLaughlin, D. B., and E. F. Wood, 1988: A distributed parameter approach for evaluating the accuracy of groundwater model predictions: 1. Theory. Water Resour. Res., 24, 1037-1047.
26. Nijssen, B., G. M. O'Donnell, D. P. Lettenmaier, D. Lohmann, and E. F. Wood, 2001: Predicting the discharge of global rivers. J. Climate, 14, 3307-3323.
27. Pan, M., 2004: Generation of ensembles of spatial hydrologic fields. Eos, Trans. Amer. Geophys. Union, 85 (Fall Meeting Suppl.), Abstract H23C-1142.
28. Peters-Lidard, C. D., E. Blackburn, X. Liang, and E. F. Wood, 1998: The effect of soil thermal conductivity parameterization on surface energy fluxes and temperatures. J. Atmos. Sci., 55, 1209-1224.
29. Reichle, R. H., D. B. McLaughlin, and D. Entekhabi, 2001: Variational data assimilation of microwave radiobrightness observations for land surface hydrology applications. IEEE Trans. Geosci. Remote Sens., 39, 1708-1718.
30. Reichle, R. H., D. B. McLaughlin, and D. Entekhabi, 2002: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Wea. Rev., 130, 103-114.
31. Roads, J., and Coauthors, 2003: GCIP water and energy budget synthesis (WEBS). J. Geophys. Res., 108, 8609, doi:10.1029/2002JD002583.
32. Rodriguez-Iturbe, I., and J. Mejia, 1974: Design of rainfall networks in time and space. Water Resour. Res., 10, 713-728.
33. Ropeleski, C. F., and E. S. Yarosh, 1998: The observed mean annual cycle of moisture budgets over the central United States (1973-92). J. Climate, 11, 2180-2190.
34. Simon, D., and T. L. Chia, 2002: Kalman filtering with state equality constraints. IEEE Trans. Aerosp. Electron. Syst., 38, 128-136.
35. Sorenson, H. W., 1966: Kalman filtering techniques. Advances in Control Systems: Theory and Applications, C. T. Leondes, Ed., Vol. 3, Academic Press, 219-292.
36. Wallis, J., R. Dennis, P. Lettenmaier, and E. F. Wood, 1991: A daily hydroclimatological dataset for the continental United States. Water Resour. Res., 27, 1657-1664.
37. Wood, E. F., and I. Rodriguez-Iturbe, 1975: A Bayesian approach to analyzing uncertainty among flood frequency models. Water Resour. Res., 11, 839-843.
38. Ziegler, A. D., J. Sheffield, E. P. Maurer, B. Nijssen, E. F. Wood, and D. P. Lettenmaier, 2003: Detection of intensification in global- and continental-scale hydrological cycles: Temporal scale of evaluation. J. Climate, 16, 535-547.