Ensemble Riemannian data assimilation over the Wasserstein space

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Tamang, SK; Ebtehaj, A; Van Leeuwen, PJ; Zou, D; Lerman, G

Published Date

  • July 6, 2021

Published In

Volume / Issue

  • 28 / 3

Start / End Page

  • 295 - 309

Electronic International Standard Serial Number (EISSN)

  • 1607-7946

International Standard Serial Number (ISSN)

  • 1023-5809

Digital Object Identifier (DOI)

  • 10.5194/npg-28-295-2021

Citation Source

  • Scopus