An Ensemble-Based Three-Dimensional Variational Assimilation Method for Land Data Assimilation

Atmospheric and Oceanic Science Letters

Volumn 2, Issue 3, 2009, Pages 125-129

  Xiang Jun, Tian ^a   Zheng Hui, Xie ^a  

^a INSTITUTE OF ATMOSPHERIC PHYSICS   (China)

Author keywords

En3 DVar; EnKF; land data assimilation; POD

Indexed keywords

DATA ASSIMILATION; DECOMPOSITION ANALYSIS; ENSEMBLE FORECASTING; KALMAN FILTER; LAND SURFACE; MONTE CARLO ANALYSIS; THREE-DIMENSIONAL MODELING;

EID: 80052270101     PISSN: 16742834     EISSN: 23766123     Source Type: Journal    
DOI: 10.1080/16742834.2009.11446792     Document Type: Article

References (21)

1. Bergthorsson, P., B. Doos, S. Frykland, et al., 1955: Routine fore-casting with the barotropics model, Tellus Ser. A, 7, 329— 340.
2. Bonan, G. B., K. W. Oleson, M. S. Vertenstein, et al., 2002: The land surface climatology of the Community Land Model coupled to the NCAR Community Climate Model, J. Climate, 15, 3123–3139.
3. Courtier, P., 1997: Dual formulation of four-dimensional variational assimilation, Quart. J. Roy. Meteor Soc., 123, 2249–2261, doi: 10.1256/smsqj.54413.
4. Cressman, G. P. 1959: An operational objective analysis system, Mon. Wea. Rev, 87, 367–374, doi: 10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2.
5. De Lannoy, G. J. M., P. R. Houser, V. R. N. Pauwels, et al., 2007: State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency, Water Resour. Res., 43, W06401, doi: 10/1029/2006WR005100.
6. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143–10162, doi: 10.1029/4JC00572.
7. Evensen, G., 2003: The Ensemble Kalman Filter: Theoretical formulation and practical implementation, Ocean Dyn., 53, 343–367.
8. Evensen, G., 2007: Data Assimilation: The Ensemble Kalman Filter, Springer, Berlin, Heidelberg, 279pp.
9. Gandin, L. S., 1963: Objective Analysis of Meteorological Fields, translated from Russian in 1965 by Israel Program for Scientific Translations, Jerusalem, 242pp.
10. Kalnay, E., 2005: Atmospheric Modeling, Data Assimilation and Predictability (in Chinese), translated by Z. Pu, F. Yang, B. Deng, et al., China Meteorological Press, Beijing, 300pp.
11. Ly, H. V., and H. T. Tran, 2001: Modeling and control of physical processes using proper orthogonal decomposition, Mathematical Computer Modeling, 33, 223–236.
12. Ly, H. V., and H. T. Tran, 2002: Proper orthogonal decomposition for flow calculations and optimal control in a horizontal CVD reactor, Quart. Appl. Mathematics, 60(3), 631–656.
13. Miller, R. N., M. Ghil, and F. Gauthiez, 1994: Advanced data as-similation in strongly nonlinear dynamical systems, Atmos. Sci., 51, 1037–1056, doi: 10.1175/1520-0469(1994)051<1037:ADAISN>2.0.CO;2.
14. Mu, M., and J. Wang, 2003: A method for adjoint variational data assimilation with physical “on-off” processes, Atmos. Sci., 60, 2010–2018.
15. Panofsky, H., 1949: Objective weather-map analysis, Appl. Meteor., 6, 386–392.
16. Tian, X., and Z. Xie, 2008: A land surface soil moisture data as-similation framework in consideration of the model sub-grid-scale heterogeneity and soil water thawing and freezing, Sci. China Ser. D, 51(7), 992–1000, doi: 10.1007/s11430-008-0069-5.
17. Tian, X., Z. Xie, and A. Dai, 2008a: An ensemble-based explicit four-dimensional variational assimilation method, J. Geophys. Res., 113, D21124, doi: 10.1029/2008JD010358.
18. Tian, X., Z. Xie, and A. Dai, 2008b: A land surface soil moisture data assimilation system based on the dual-UKF method and the Community Land Model, J. Geophys. Res., 113, D14127, doi: 10.1029/2007JDO09650.
19. Wang, J., and J. Li, 2009: A four-dimensional scheme based on singular value decomposition (4-DSVD) for chaoticattractor-theory-oriented data assimilation, J. Geophys. Res., 114, D02114, doi: 10.1029/2008JD010916.
20. Yang, K., T. Watanabe, T. Koike, et al., 2007: Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget, J. Meteor Soc. Japan, 85A, 229–242.
21. Zheng, X., 2003: Data assimilation, an important and challenging research topic for environmental statisticians, in: Sustainable Environments: A Statistical Analysis, Oxford University Press, New York, 198–207.