Abstract: AbstractWhile the formulation of most data assimilation schemes assumes an unbiased observation model error, in real applications model error with nontrivial biases is unavoidable. A practical example is errors in the ...

Abstract: Filtering sparsely turbulent signals from nature is a central problem of contemporary data assimilation. Here, sparsely observed turbulent signals from nature are generated by solutions of two-layer quasigeostrophic models ...

Abstract: his paper presents a fast reduced filtering strategy for assimilating multiscale systems in the presence of observations of only the macroscopic (or large scale) variables. This reduced filtering strategy introduces model ...

Abstract: data-driven method for improving the correlation estimation in serial ensemble Kalman filters is introduced. The method finds a linear map that transforms, at each assimilation cycle, the poorly estimated sample correlation ...

Abstract: AbstractThis paper demonstrates the efficacy of data-driven localization mappings for assimilating satellite-like observations in a dynamical system of intermediate complexity. In particular, a sparse network of synthetic ...