Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review-Reference-Cited by-同舟云学术

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Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review

Published:2013 Issue:2 Volume:91 Page:79-99
ISSN:0026-1165
Container-title:Journal of the Meteorological Society of Japan. Ser. II
language:en
Short-container-title:Journal of the Meteorological Society of Japan

Author:

RUIZ Juan Jose¹,PULIDO Manuel²,MIYOSHI Takemasa³

Affiliation:

1. Department of Physics, FACENA, Universidad Nacional del Nordeste, Corrientes, Argentina, CIMA, UMI-IFAECI/CNRS, CONICET, Buenos Aires, Argentina

2. Department of Physics, FACENA, Universidad Nacional del Nordeste, Corrientes, Argentina. IMIT, UMI-IFAECI/CNRS, CONICET, Argentina

3. Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA

Publisher

Meteorological Society of Japan

Subject

Atmospheric Science

Link

https://www.jstage.jst.go.jp/article/jmsj/91/2/91_2013-201/_pdf

Reference80 articles.

1. Aksoy, A., F. Zhang, and J. Nielsen-Gammon, 2006: Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model. Mon. Wea. Rev., 134, 2951-2969.

2. Ambadan, J. T., and Y. Tang, 2011: Sigma-point particle filter for parameter estimation in a multiplicative noise environment. J. Adv. Model. Earth Syst., 3, M12005, doi:10.1029/2011MS000065.

3. Anderson, J., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.

4. Anderson, J., and S. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741-2758.

5. Annan, J. D., and J. C. Hargreaves, 2007: Efficient estimation and ensemble generation in climate modelling filter. Phil. Trans. R. Soc. A, 365, 2077-2088.

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