Preprints
https://doi.org/10.5194/npg-2023-27
https://doi.org/10.5194/npg-2023-27
04 Jan 2024
 | 04 Jan 2024
Status: this preprint is currently under review for the journal NPG.

Multi-level data assimilation for simplified ocean models

Florian Beiser, Håvard Heitlo Holm, Kjetil Olsen Lye, and Jo Eidsvik

Abstract. Multi-level Monte Carlo methods have established as a tool in uncertainty quantification for decreasing the computational costs while maintaining the same statistical accuracy as in single-level Monte Carlo. Lately, there have also been theoretical efforts to use similar ideas to facilitate multi-level data assimilation. By applying a multi-level ensemble Kalman filter for assimilating sparse observations of ocean currents into a simplified ocean model based on the shallow-water equations, we study the practical challenges of applying these method to more complex problems. We present numerical results from a realistic test case where small-scale perturbations lead to chaotic behaviour, and in this context we conduct state estimation and drift trajectories forecasting using multi-level ensembles. This represents a new step on the path of making multi-level data assimilation feasible for real-world oceanographic applications.

Florian Beiser, Håvard Heitlo Holm, Kjetil Olsen Lye, and Jo Eidsvik

Status: open (until 29 Feb 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2023-27', Anonymous Referee #1, 31 Jan 2024 reply
    • AC1: 'Reply on RC1', Florian Beiser, 08 Feb 2024 reply
  • RC2: 'Comment on npg-2023-27', Anonymous Referee #2, 09 Feb 2024 reply
Florian Beiser, Håvard Heitlo Holm, Kjetil Olsen Lye, and Jo Eidsvik

Model code and software

GPU Ocean A. Brodtkorb et al. https://doi.org/10.5281/zenodo.7938844

Florian Beiser, Håvard Heitlo Holm, Kjetil Olsen Lye, and Jo Eidsvik

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Short summary
Efficient search-and-rescue at sea is supported by rapid forecasts of drift trajectories based on a dynamical model and data assimilation of in situ observations. The multi-level approach can accelerate computations by running models on various resolutions. This work explores the practicality of multi-level methods for realistic data assimilation applications by considering a challenging shallow-water case for the ocean dynamics.