Talk by Alison Ramage (University of Strathclyde)
Автор: ENLA Seminar
Загружено: 2022-03-30
Просмотров: 284
Approximating the inverse Hessian in 4D-Var data assimilation
Large-scale variational data assimilation problems are commonly found in applications like numerical weather prediction and oceanographic modelling. The 4D-Var method is frequently used to calculate a forecast model trajectory that best fits the available observations to within the observational error over a period of time. One key challenge is that the state vectors used in realistic applications could contain a very large number of unknowns so, due to memory limitations, in practice it is often impossible to assemble, store or manipulate the matrices involved explicitly. In this talk we present a limited memory approximation to the inverse Hessian, computed using the Lanczos method, based on a multilevel approach. We illustrate one use of this approximation by showing its potential effectiveness as a preconditioner within a Gauss-Newton iteration.
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