Variational data assimilation for very large environmental problemsLawless, A. ORCID: https://orcid.org/0000-0002-3016-6568 (2013) Variational data assimilation for very large environmental problems. In: Cullen, M., Freitag, M.A., Kindermann, S. and Scheichl, R. (eds.) Large Scale Inverse Problems: Computational Methods and Applications in the Earth Sciences. Radon Series on Computational and Applied Mathematics, 2 (13). De Gruyter, Berlin, pp. 55-90. ISBN 9783110282269
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Official URL: http://www.degruyter.com/view/product/182025 Abstract/SummaryVariational data assimilation is commonly used in environmental forecasting to estimate the current state of the system from a model forecast and observational data. The assimilation problem can be written simply in the form of a nonlinear least squares optimization problem. However the practical solution of the problem in large systems requires many careful choices to be made in the implementation. In this article we present the theory of variational data assimilation and then discuss in detail how it is implemented in practice. Current solutions and open questions are discussed.
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