## L1-regularisation for ill-posed problems in variational data assimilationTools
Freitag, M. A., Nichols, N. and Budd, C. J.
(2010)
Full text not archived in this repository. To link to this article DOI: 10.1002/pamm.201010324 ## Abstract/SummaryWe 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.
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