the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Computation of covariant lyapunov vectors using data assimilation
Shashank Kumar Roy
Amit Apte
Abstract. Computing Lyapunov vectors from partial and noisy observations is a challenging problem. We propose a method using data assimilation to approximate the Lyapunov vectors using the estimate of the underlying trajectory obtained from the filter mean. We then extensively study the sensitivity of these approximate Lyapunov vectors and the corresponding Oseledets' subspaces to the perturbations in the underlying true trajectory. We demonstrate that this sensitivity is consistent with and helps explain the errors in the approximate Lyapunov vectors from the estimated trajectory of the filter. Using the idea of principal angles, we demonstrate that the Oseledets' subspaces defined by the LVs computed from the approximate trajectory are less sensitive than the individual vectors.
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Shashank Kumar Roy and Amit Apte
Status: open (until 10 Jan 2024)
Shashank Kumar Roy and Amit Apte
Model code and software
Mr-Markovian/Reconstruct_CLV_via_enkf: Published version of the code Shashank Kumar Roy https://zenodo.org/record/8396549
Shashank Kumar Roy and Amit Apte
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