Resolution of sharp fronts in the presence of model error in variational data assimilation
Abstract
We show that data assimilation using four-dimensional variation
(4DVar) can be interpreted as a form of Tikhonov regularisation, a
familiar method for solving ill-posed inverse problems. It is known from
image restoration problems that $L_1$-norm penalty regularisation recovers
sharp edges in the image better than the $L_2$-norm penalty
regularisation. We apply this idea to 4DVar for problems where shocks are
present and give some examples where the $L_1$-norm penalty approach
performs much better than the standard $L_2$-norm regularisation in 4DVar.