Abstract
This paper studies a direct approach to smoothing by sampling the posterior distribution in four dimensional data assimilation. The methodology is based on a hybrid Monte Carlo approach and can be applied to non-linear models, non-linear observation operators, and non-Gaussian probability distributions. The generated ensemble is used to construct both the analysis state (the minimum variance estimator) and the analysis error covariance matrix. Numerical tests performed with the Lorenz-96 model and with both linear and quadratic observation operators illustrate the usefulness and performance of the approach.
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Acknowledgements
This work was supported by AFOSR DDDAS program through the award AFOSR FA9550–12–1–0293–DEF managed by Dr. Frederica Darema.
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Attia, A., Rao, V., Sandu, A. (2015). A Sampling Approach for Four Dimensional Data Assimilation. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_20
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DOI: https://doi.org/10.1007/978-3-319-25138-7_20
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