Preprints
https://doi.org/10.5194/npg-2021-28
https://doi.org/10.5194/npg-2021-28

  07 Sep 2021

07 Sep 2021

Review status: a revised version of this preprint was accepted for the journal NPG and is expected to appear here in due course.

Ensemble Riemannian Data Assimilation: Towards High-dimensional Implementation

Sagar Kumar Tamang1, Ardeshir Ebtehaj1, Peter Jan van Leeuwen2, Gilad Lerman3, and Efi Foufoula-Georgiou4 Sagar Kumar 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
  • 3School of Mathematics, University of Minnesota-Twin Cities, Twin Cities, Minnesota, USA
  • 4Department of Civil and Environmental Engineering and Department of Earth System Science, University of California Irvine, Irvine, California, USA

Abstract. This paper presents the results of the Ensemble Riemannian Data Assimilation for relatively high-dimensional nonlinear dynamical systems, focusing on the chaotic Lorenz-96 model and a two-layer quasi-geostrophic (QG) model of atmospheric circulation. The analysis state in this approach is inferred from a joint distribution that optimally couples the background probability distribution and the likelihood function, enabling formal treatment of systematic biases without any Gaussian assumptions. Despite the risk of the curse of dimensionality in the computation of the coupling distribution, comparisons with the classic implementation of the particle filter and the stochastic ensemble Kalman filter demonstrate that with the same ensemble size, the presented methodology could improve the predictability of dynamical systems. In particular, under systematic errors, the root mean squared error of the analysis state can be reduced by 20 % (30 %) in Lorenz-96 (QG) model.

Sagar Kumar Tamang et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-28', Anonymous Referee #1, 11 Oct 2021
    • AC1: 'Reply on RC1', Sagar Tamang, 03 Dec 2021
  • RC2: 'Comment on npg-2021-28', Anonymous Referee #2, 13 Nov 2021
    • AC2: 'Reply on RC2', Sagar Tamang, 03 Dec 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-28', Anonymous Referee #1, 11 Oct 2021
    • AC1: 'Reply on RC1', Sagar Tamang, 03 Dec 2021
  • RC2: 'Comment on npg-2021-28', Anonymous Referee #2, 13 Nov 2021
    • AC2: 'Reply on RC2', Sagar Tamang, 03 Dec 2021

Sagar Kumar Tamang et al.

Sagar Kumar Tamang et al.

Viewed

Total article views: 829 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
746 74 9 829 2 2
  • HTML: 746
  • PDF: 74
  • XML: 9
  • Total: 829
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 07 Sep 2021)
Cumulative views and downloads (calculated since 07 Sep 2021)

Viewed (geographical distribution)

Total article views: 778 (including HTML, PDF, and XML) Thereof 778 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Jan 2022
Download
Short summary
The outputs from Earth System models are optimally combined with satellite observations to produce accurate forecasts through a process called data assimilation. Many existing data assimilation methodologies have some assumptions regarding the shape of the probability distributions of model output and observations which results in forecast inaccuracies. In this paper, we test the effectiveness of a newly proposed methodology that relaxes such assumptions on high-dimensional models.