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Data Assimilation with Numerical model error

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If you have a question about this talk, please contact Mustapha Amrani .

Inverse Problems

Data assimilation addresses the inverse problem of, given a set of uncertain observations and a numerical approximation to a physical system, what set of parameters, especially the initial condition, leads to a forward computation (the ‘analysis’) which best solves this problem. Data assimilation is widely used in meteorological applications. In this talk I will briefly describe the 4D-VAR method for data assimilation, and then show how its results are influenced by using a variety of different numerical schemes with associated numerical model error.

Joint work with Sian Jenkins, Melina Freitag and Nathan Smith (Bath)

This talk is part of the Isaac Newton Institute Seminar Series series.

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