Talk by Alison Ramage (University of Strathclyde)

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.

Видео Talk by Alison Ramage (University of Strathclyde) канала ENLA Seminar