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Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics (1994)
| Venue: | J. Geophys. Res |
| Citations: | 797 - 23 self |
BibTeX
@ARTICLE{Evensen94sequentialdata,
author = {Geir Evensen},
title = {Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics},
journal = {J. Geophys. Res},
year = {1994},
volume = {99},
pages = {10143--10162}
}
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Abstract
. A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error covariance equation are avoided because storage and evolution of the error covariance matrix itself are not needed. The results are also better than what is provided by the extended Kalman filter since there is no closure problem and the quality of the forecast error statistics therefore improves. The method should be feasible also for more sophisticated primitive equation models. The computati...
Keyphrases
monte carlo method error statistic extended kalman filter sequential data assimilation nonlinear quasi-geostrophic model error covariance equation closure problem well-known numerical instability sophisticated primitive equation model unbounded error growth ocean model open boundary error covariance matrix approximate error covariance equation forecast error statistic simplified closure new sequential data assimilation method
