Research

L1-regularisation for ill-posed problems in variational data assimilation


Reference:

Freitag, M. A., Nichols, N. K. and Budd, C. J., 2010. L1-regularisation for ill-posed problems in variational data assimilation. PAMM - Proceedings in Applied Mathematics and Mechanics, 10 (1), 665 -668.

Related documents:

This repository does not currently have the full-text of this item.
You may be able to access a copy if URLs are provided below. (Contact Author)

Official URL:

http://dx.doi.org/10.1002/pamm.201010324

Abstract

We consider four-dimensional variational data assimilation (4DVar) and show that it can be interpreted as Tikhonov or L2-regularisation, a widely used method for solving ill-posed inverse problems. It is known from image restoration and geophysical problems that an alternative regularisation, namely L1-norm regularisation, recovers sharp edges better than L2-norm regularisation. We apply this idea to 4DVar for problems where shocks and model error are present and give two examples which show that L1-norm regularisation performs much better than the standard L2-norm regularisation in 4DVar.

Details

Item Type Articles
CreatorsFreitag, M. A., Nichols, N. K. and Budd, C. J.
DOI10.1002/pamm.201010324
DepartmentsFaculty of Science > Mathematical Sciences
RefereedNo
StatusPublished
ID Code25297
Additional InformationPAMM Special Issue: 81st Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Karlsruhe 2010; Editor: Prof. Christian Wieners

Export

Actions (login required)

View Item