Resolution of sharp fronts in the presence of model error in variational data assimilation
نویسندگان
چکیده
We show that data assimilation using four-dimensional variation (4DVar) can be interpreted as a form of Tikhonov regularisation, a very familiar method for solving ill-posed inverse problems. It is known from image restoration problems that L1-norm penalty regularisation recovers sharp edges in the image more accurately than Tikhonov, or L2-norm, penalty regularisation. We apply this idea to 4DVar for problems where shocks are present and give some examples where the L1-norm penalty approach performs much better than the standard L2-norm regularisation in 4DVar.
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