Received: 26 May 2014 – Accepted for review: 14 Jul 2014 – Discussion started: 04 Aug 2014
Abstract. In this work, we consider the Bayesian optimization (BO) approach for tuning parameters of complex chaotic systems. Such problems arise, for instance, in tuning the sub-grid scale parameterizations in weather and climate models. For such problems, the tuning procedure is generally based on a performance metric which measures how well the tuned model fits the data. This tuning is often a computationally expensive task. We show that BO, as a tool for finding the extrema of computationally expensive objective functions, is suitable for such tuning tasks. In the experiments, we consider tuning parameters of two systems: a simplified atmospheric model and a low-dimensional chaotic system. We show that BO is able to tune parameters of both the systems with a low number of objective function evaluations and without the need of any gradient information.
How to cite. Abbas, M., Ilin, A., Solonen, A., Hakkarainen, J., Oja, E., and Järvinen, H.: Bayesian optimization for tuning chaotic systems, Nonlin. Processes Geophys. Discuss., 1, 1283–1312, https://doi.org/10.5194/npgd-1-1283-2014, 2014.