Forecasting Turbulent Modes with Nonparametric Diffusion Models: Learning from noisy data

Abstract

In this paper, we apply a recently developed nonparametric modeling approach, the "diffusion forecast", to predict the time-evolution of Fourier modes of turbulent dynamical systems. While the diffusion forecasting method assumes the availability of a noise-free training data set observing the full state space of the dynamics, in real applications we often have only partial observations which are corrupted by noise. To alleviate these practical issues, following the theory of embedology, the diffusion model is built using the delay-embedding coordinates of the data. We show that this delay embedding biases the geometry of the data in a way which extracts the most stable component of the dynamics and reduces the influence of independent additive observation noise. The resulting diffusion forecast model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and when the observation noise vanishes. As in any standard forecasting problem, the forecasting skill depends crucially on the accuracy of the initial conditions. We introduce a novel Bayesian method for filtering the discrete-time noisy observations which works with the diffusion forecast to determine the forecast initial densities. Numerically, we compare this nonparametric approach with standard stochastic parametric models on a wide-range of well-studied turbulent modes, including the Lorenz-96 model in weakly chaotic to fully turbulent regimes and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. We show that when the only available data is the low-dimensional set of noisy modes that are being modeled, the diffusion forecast is indeed competitive to the perfect model.

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• Nonparametric forecasting of low-dimensional dynamical systems
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• Parametric Reduced Models for the Nonlinear Schrödinger Equation
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• Manifold learning with bi-stochastic kernels
• Manifold Learning with Contracting Observers for Data-driven Time-series Analysis
• Semiparametric forecasting and filtering: correcting low-dimensional model error in parametric models
• Diffusion forecasting model with basis functions from QR decomposition
• Parameter estimation with data-driven nonparametric likelihood functions
• Dimension reduction for systems with slow relaxation
• Correcting biased observation model error in data assimilation