Articles | Volume 28, issue 4
Research article
23 Dec 2021
Research article |  | 23 Dec 2021

Inferring the instability of a dynamical system from the skill of data assimilation exercises

Yumeng Chen, Alberto Carrassi, and Valerio Lucarini

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Cited articles

Albarakati, A., Budišić, M., Crocker, R., Glass-Klaiber, J., Iams, S., Maclean, J., Marshall, N., Roberts, C., and Van Vleck, E. S.: Model and data reduction for data assimilation: Particle filters employing projected forecasts and data with application to a shallow water model, Comput. Math. Appl., in press,, 2021. a, b
Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms, and applications, SIAM, Philadelphia, United States, ISBN: 978-1-61197-453-9, 2016. a
Auerbach, D., Cvitanović, P., Eckmann, J.-P., Gunaratne, G., and Procaccia, I.: Exploring chaotic motion through periodic orbits, Phys. Rev. Lett., 58, 2387–2389,, 1987. a
Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data assimilation and the unstable subspace, Tellus A, 69, 1304504,, 2017. a, b, c, d, e
Bocquet, M., Raanes, P. N., and Hannart, A.: Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation, Nonlin. Processes Geophys., 22, 645–662,, 2015. a, b
Short summary
Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate forecast. These properties are often used to inform the design of data assimilation (DA), a method used to estimate the exact initial conditions. However, obtaining the instability properties is burdensome for complex problems, both numerically and analytically. Here, we suggest a different viewpoint. We show that the skill of DA can be used to infer the instability properties of a dynamical system.