Ensemble Riemannian Data Assimilation over the Wasserstein Space

7 Sep 2020  ·  Sagar K. Tamang, Ardeshir Ebtehaj, Peter J. Van Leeuwen, Dongmian Zou, Gilad Lerman ·

In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Eulerian penalization of error in the Euclidean space, the Wasserstein metric can capture translation and difference between the shapes of square-integrable probability distributions of the background state and observations -- enabling to formally penalize geophysical biases in state-space with non-Gaussian distributions... The new approach is applied to dissipative and chaotic evolutionary dynamics and its potential advantages and limitations are highlighted compared to the classic variational and filtering data assimilation approaches under systematic and random errors. read more

PDF Abstract
No code implementations yet. Submit your code now




  Add Datasets introduced or used in this paper