Optimal ensemble size of ensemble Kalman filter in sequential soil moisture data assimilation

Geophysical Research Letters

Volumn 42, Issue 16, 2015, Pages 6710-6715

  Yin, Jifu ^a,b,c   Zhan, Xiwu ^b   Zheng, Youfei ^a   Hain, Christopher R ^b,c   Liu, Jicheng ^b,c   Fang, Li ^b,c  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

^c UNIVERSITY OF MARYLAND   (United States)

Author keywords

data assimilation; ensemble Kalman filter; ensemble size

Indexed keywords

SOIL MOISTURE; SOIL SURVEYS; WEATHER FORECASTING;

DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; ENSEMBLE SIZE; LAND SURFACE MODELING; LIMITED SENSITIVITY; MATHEMATICAL DERIVATION; MAXIMUM EFFICIENCY; OPTIMAL ENSEMBLE;

KALMAN FILTERS;

DATA ASSIMILATION; ENSEMBLE FORECASTING; KALMAN FILTER; LAND SURFACE; NUMERICAL MODEL; OPTIMIZATION; SOIL MOISTURE; WEATHER FORECASTING;

EID: 84941739439     PISSN: 00948276     EISSN: 19448007     Source Type: Journal    
DOI: 10.1002/2015GL063366     Document Type: Article

References (16)

1. Bennett, A. F., and, W. P. Budgell, (1987), Ocean data assimilation and the Kalman Filter: Spatial regularity, J. Phys. Oceanogr., 17 (10), 1583-1601.
2. Bennett, A. F., and, W. P. Budgell, (1989), The Kalman smoother for a linear quasi-geostrophic model of ocean circulation, Dyn. Atmos. Oceans, 13 (3-4), 219-267.
3. Burgers, G., P. J. V. Leeuwen, and, G. Evensen, (1998), Analysis scheme in the ensemble Kalman filter, Mon. Weather Rev., 126, 1719-1724.
4. Cohn, S. E., and, D. F. Parrish, (1991), The behavior of forecast error covariances for a Kalman filter in two dimensions, Mon. Weather Rev., 119, 1757-1785.
5. Dee, D. P., (1991), Simplification of the Kalman filter for meteorological data assimilation, Q. J. R. Meteorol. Soc., 117, 365-384.
6. Evensen, E., (1992), Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model, J. Geophys. Res., 97, 17,905-17,924, doi: 10.1029/92JC01972.
7. Flores, A. N., D. Entekhabi, and, R. L. Bras, (2010), Reproducibility of soil moisture ensembles when representing soil parameter uncertainty and correlation using a Latin Hypercube-based approach, Water Resour. Res., 46, W04506, doi: 10.1029/2009WR008155.
8. Kalman, R. E., (1960), A new approach to linear filtering and prediction problems, Trans. ASME J. Basic Eng., 82 (Series D), 35-45.
9. Kumar, S. V., R. H. Riechle, C. D. Peters-Lidard, R. D. Koster, X. Zhan, W. T. Crow, J. B. Eylander, and, P. R. Houser, (2008), A land surface data assimilation framework using the land information system: Description and applications, Adv. Water Resour., 31, 1419-1432.
10. Lorenc, A. C., (1981), A global three-dimensional multivariate statistical interpolation scheme, Mon. Weather Rev., 109, 701-721.
11. Madsen, H., and, R. Cañizares, (1999), Comparison of extended and ensemble Kalman filters for data assimilation in coastal area modeling, Int. J. Numer. Methods Fluids, 31, 961-981.
12. Miller, R. N., (1986), Toward the application of the Kalman filter to regional open ocean modeling, J. Phys. Oceanogr., 16, 72-86.
13. Reichle, R. H., and, R. D. Koster, (2005), Global assimilation of satellite surface soil moisture retrievals into the NASA catchment land surface model, Geophys. Res. Lett., 32, L02404, doi: 10.1029/2004GL021700.
14. Reichle, R. H., D. B. Mclaughlin, and, D. Entekhabi, (2002), Hydrologic data assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130, 103-114.
15. Yin, J., X. Zhan, Y. Zheng, J. Liu, C. R. Hain, and, L. Fang, (2014), Impact of quality control of satellite soil moisture data on their assimilation into land surface model, Geophys. Res. Lett., 41, 7159-7166, doi: 10.1002/2014GL060659.
16. Yin, J., X. Zhan, Y. Zheng, J. Liu, L. Fang, and, C. R. Hain, (2015), Enhancing model skill by assimilating SMOPS blended soil moisture product into Noah land surface model, J. Hydrometeorol., 16, 917-931, doi: 10.1175/JHM-D-14-0070.1.