Multiple time level adjustment for data assimilation
 Publisher: CoAction Publishing
 Journal: Tellus A (issn: 16000870)

Related identifiers: doi: 10.3402/tellusa.v56i1.14390

References
(36)
Anderson, J. L. 1996. A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate 9, 15181530.
Anderson, J. L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev. 129, 28842903.
Anderson, J. L. 2003. A local least squares framework for ensemble filtering. Mon. Wea. Rev. 131, 634642.
Anderson, J. L. and Anderson, S. L. 1999. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev. 127, 27412758.
Asselin, R. 1972. Frequency filter for time integrations. Mon. Wea. Rev. 100, 487490.
Bishop, C. H., Etherton, B. J. and Majumdar, S. 2001. Adaptive sampling with the ensemble transform Kalman filter, part I. Mon. Wea. Rev. 129, 420436.
Burgers, G., van Leeuwen, P. J. and Evensen, G. 1998. Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev. 126, 1719 1724.
Durran, D. R. 1999. Numerical Methods for Wave Equations in Geophysical Fluid Dynamics. Springer, New York, 465 pp.
Evensen, G. 1994. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99, 10 14310 162.
Fukumori, I. 2002. A partitioned Kalman filter and smoother. Mon. Wea. Rev. 130, 13701383.

Similar Research Results
(3)

Metrics
No metrics available

 Download from


Cite this publication