In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Euclidean distance used in classic data assimilation methodologies, the Wasserstein metric can capture the translation and difference between the shapes of square-integrable probability distributions of the background state and observations. This enables us 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 ensemble data assimilation approaches under systematic errors.
|Original language||English (US)|
|Number of pages||15|
|Journal||Nonlinear Processes in Geophysics|
|State||Published - Jul 6 2021|
Bibliographical noteFunding Information:
Financial support. This research has been supported by the Na-
This research has been supported by the National Aeronautics and Space Administration (Terrestrial Hydrology Program - THP; grant no. 80NSSC18K1528), the New (Early Career) Investigator Program (NIP; grant no. 80NSSC18K0742), the European Research Council, H2020 European Research Council (CUNDA (grant no. 694509)), and the National Science Foundation (grant no. DMS1830418).
© 2021 Sagar K. Tamang et al.