Constrained Random Diffeomorphisms for Data Assimilation
摘要
For ensemble-based data assimilation purposes, there is a definite need for relevant ensemble sampling tools. Indeed, the quality and spreading of these ensembles have deep implications in the quality of the data assimilation, and—until recently—those so-called covariance inflation tools have mostly relied on unsuitable linear Gaussian frameworks. A promising alternative is the generation of ensembles through a stochastic remapping of the physical space.