Preprints
https://doi.org/10.5194/npg-2021-11
https://doi.org/10.5194/npg-2021-11

  09 Mar 2021

09 Mar 2021

Review status: this preprint is currently under review for the journal NPG.

Ensemble Riemannian Data Assimilation over the Wasserstein Space

Sagar K. Tamang1, Ardeshir Ebtehaj1, Peter J. van Leeuwen2, Dongmian Zou3, and Gilad Lerman4 Sagar K. Tamang et al.
  • 1Department of Civil, Environmental and Geo-Engineering and Saint Anthony Falls Laboratory, University of Minnesota-Twin Cities, Twin Cities, Minnesota, USA
  • 2Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
  • 3Duke Kunshan University, Kunshan, China
  • 4School of Mathematics, University of Minnesota-Twin Cities, Twin Cities, Minnesota, USA

Abstract. 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.

Sagar K. Tamang et al.

Status: open (until 04 May 2021)

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Sagar K. Tamang et al.

Sagar K. Tamang et al.

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