An approach to quantifying the efficiency of a Bayesian filter

Water Resources Research

Volumn 49, Issue 4, 2013, Pages 2164-2173

  Nearing, Grey S ^a   Gupta, Hoshin V ^a   Crow, Wade T ^b   Gong, Wei ^c  

^a UNIVERSITY OF ARIZONA   (United States)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c BEIJING NORMAL UNIVERSITY   (China)

Author keywords

data assimilation; information theory

Indexed keywords

DATA FLOW ANALYSIS; EFFICIENCY; INFORMATION THEORY; SOIL MOISTURE;

BAYESIAN CONDITIONING; DATA ASSIMILATION; DYNAMIC SYSTEM MODELING; ENSEMBLE KALMAN FILTER; LIKELIHOOD FUNCTIONS; OBSERVATION OPERATOR; OBSERVATION SYSTEMS; SIMPLIFYING ASSUMPTIONS;

KALMAN FILTERS;

BAYESIAN ANALYSIS; DATA ASSIMILATION; DATA PROCESSING; EFFICIENCY MEASUREMENT; HYDROLOGICAL MODELING; INFORMATION SYSTEM; KALMAN FILTER; SOIL MOISTURE; SOIL WATER; TOPSOIL; UNCERTAINTY ANALYSIS;

EID: 84877942372     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/wrcr.20177     Document Type: Article

References (31)

1. Ahmad, I. A., and, P. E. Lin, (1976), Nonparametric estimation of entropy for absolutely continuous distributions, IEEE Trans. Inf. Theory, 22 (3), 372-375, doi:3 10.1109/tit.1976.1055550.
2. Arnold, C. P., and, C. H. Dey, (1986), Observing-systems simulation experiments - Past, present, and future, Bull. Am. Meteorol. Soc., 67 (6), 687-695, doi:610.1175/1520-0477(1986)1067<0687:OSSEPP>1172.1170.CO;1172.
3. Bellman, R., (2003), Dynamic Programing, Dover, Mineola, New York.
4. Bolten, J. D., W. T. Crow, X. W. Zhan, T. J. Jackson, and, C. A. Reynolds, (2010), Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring, IEEE J. Selec. Top. Appl. Earth Obs. Remote Sens., 3 (1), 57-66, doi: 10.1109/jstars.2009.2037163.
5. Brooks, R. H., and, A. T. Corey, (1964), Hydraulic properties of porous media, Hydrol. Pap. No. 3, Colo. State Univ., Fort Colliins, Colo.
6. Crow, W. T., and, E. Van Loon, (2006), Impact of incorrect model error assumptions on the sequential assimilation of remotely sensed surface soil moisture, J. Hydrometeorol., 7 (3), 421-432, doi:4 10.1175/JHM1499.1171. (Pubitemid 44070412)
7. 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 (2), 137-149, doi:1 10.1016/S0309-1708(1002)00088-X. (Pubitemid 36093690)
8. Crow, W. T., and, R. H. Reichle, (2008), Comparison of adaptive filtering techniques for land surface data assimilation, Water Resour. Res., 44, W08423, doi:084 10.01029/02008wr006883.
9. Dunne, S., and, D. Entekhabi, (2005), An ensemble-based reanalysis approach to land data assimilation, Water Resour. Res., 41, W02013, doi:020 10.01029/02004WR003449.
10. Evensen, G., (2003), The Ensemble Kalman Filter: Theoretical formulation and practical implementation, Ocean Dyn., 53, 343-367, doi:3 10.1007/s10236-10003-10036-10239.
11. Flores, A. N., R. L. Bras, and, D. Entekhabi, (2012), Hydrologic data assimilation with a hillslope-scale resolving model and L-band radar observations: Synthetic experiments with the ensemble Kalman filter, Water Resour. Res., 48, W08509, doi: 10.1029/2011WR011500.
12. Galantowicz, J. F., D. Entekhabi, and, E. G. Njoku, (1999), Tests of sequential data assimilation for retrieving profile soil moisture and temperature from observed L-band radiobrightness, IEEE Trans. Geosci. Remote Sens., 37 (4), 1860-1870, doi:18 10.1109/1836.774699.
13. Gelaro, R., Y. Zhu, and, R. M. Errico, (2007), Examination of various-order adjoint-based approximations of observation impact, Meteorol. Z., 16 (6), 685-692, doi:6 10.1127/0941-2948/2007/0248.
14. Gordon, N. J., D. J. Salmond, and, A. F. M. Smith, (1993), Novel approach to nonlinear non-Gaussian Bayesian state estimation, IEE Proc. F Radar Signal Process., 140 (2), 107-113, doi:1 10.1049/ip-f-1042.1993.0015.
15. Kalman, R. E., (1960), A new approach to linear filtering and prediction problems, Trans. ASME J. Basic Eng., 82 (Series D), 35-45, doi: 10.1115/1111.3662552.
16. Li, J., and, S. Islam, (1999), On the estimation of soil moisture profile and surface fluxes partitioning from sequential assimilation of surface layer soil moisture, J. Hydrol., 220 (1-2), 86-103, doi:1 10.1016/s0022-1694(1099) 00066-00069. (Pubitemid 29341837)
17. Liu, Y. Q., and, H. V. Gupta, (2007), Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework, Water Resour. Res., 43, W07401, doi:074 10.01029/02006wr005756|issn000043-001397.
18. Mahrt, L., and, H. Pan, (1984), A 2-layer model of soil hydrology, Boundary Layer Meteorol., 29 (1), 1-20.
19. 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 (12), 18, doi: 10.1029/2001WR001114.
20. Miller, R. N., E. F. Carter, and, S. T. Blue, (1999), Data assimilation into nonlinear stochastic models, Tellus Ser. A Dyn. Meteorol. Oceanogr., 51 (2), 167-194. (Pubitemid 29335120)
21. Paninski, L., (2003), Estimation of entropy and mutual information, Neural Comput., 15 (6), 1191-1253. (Pubitemid 37049793)
22. Pauwels, V. R. N., N. E. C. Verhoest, G. J. M. De Lannoy, V. Guissard, C. Lucau, and, P. Defourny, (2007), Optimization of a coupled hydrology-crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter, Water Resour. Res., 43, W04421, doi:044 10.01029/02006wr004942.
23. 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 (8), 1708-1718, doi:17 10.1109/1736.942549. (Pubitemid 32933010)
24. Reichle, R. H., D. B. McLaughlin, and, D. Entekhabi, (2002a), Hydrologic data assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130 (1), 103-114, doi:1 10.1175/1520-0493(2002)1130<0103:HDAWTE>1172.1170.CO;1172. (Pubitemid 34838242)
25. Reichle, R. H., J. P. Walker, R. D. Koster, and, P. R. Houser, (2002b), Extended versus ensemble Kalman filtering for land data assimilation, J. Hydrometeorol., 3 (6), 728-740, doi:7 10.1175/1525-7541(2002)1003<0728: EVEKFF>1172.1170.CO;1172. (Pubitemid 36122408)
26. Reichle, R. H., R. D. Koster, P. Liu, S. P. P. Mahanama, E. G. Njoku, and, M. Owe, (2007), Comparison and assimilation of global soil moisture retrievals from the advanced microwave scanning radiometer for the earth observing system (AMSR-E) and the scanning multichannel microwave radiometer (SMMR), J. Geophys. Res., 112, D09108, doi: 10.1029/2006JD008033. (Pubitemid 47009834)
27. Reichle, R. H., W. T. Crow, and, C. L. Keppenne, (2008), An adaptive ensemble Kalman filter for soil moisture data assimilation, Water Resour. Res., 44, W03423, doi:034 10.01029/02007WR006357.
28. Scott, D. W., (2004), Multivariate density estimation and visualization, in Handbook of Computational Statistics: Concepts and Methods, edited by, J. E. Gentle, W. Haerdle, and, Y. Mori, pp. 517-538, Springer, New York.
29. Shannon, C. E., (1948), A mathematical theory of communication, Bell Syst. Tech. J., 27 (3), 379-423.
30. Theil, H., (1967), Economics and Information Theory, Rand McNally, Chicago.
31. Wikle, C. K., and, L. M. Berliner, (2007), A Bayesian tutorial for data assimilation, Phys. D Nonlinear Phenomena, 230 (1-2), 1-16, doi: 10.1016/j.physd.2006.1009.1017. (Pubitemid 46776909)