State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics

Abstract

In a recent methodological paper, we showed how to learn chaotic dynamics along with the state trajectory from sequentially acquired observations, using local ensemble Kalman filters. Here, we more systematically investigate the possibility to use a local ensemble Kalman filter with either covariance localisation or local domains, in order to retrieve the state and a mix of key global and local parameters. Global parameters are meant to represent the surrogate dynamical core, for instance through a neural network, which is reminiscent of data-driven machine learning of dynamics, while the local parameters typically stand for the forcings of the model. Aiming at joint state and parameter estimation, a family of algorithms for covariance and local domain localisation is proposed. In particular, we show how to rigorously update global parameters using a local domain ensemble Kalman filter (EnKF) such as the local ensemble transform Kalman filter (LETKF), an inherently local method. The approach is tested with success on the 40-variable Lorenz model using several of the local EnKF flavors. A two-dimensional illustration based on a multi-layer Lorenz model is finally provided. It uses radiance-like non-local observations. It features both local domains and covariance localisation in order to learn the chaotic dynamics and the local forcings. This paper more generally addresses the key question of online estimation of both global and local model parameters.

References in corpus (8)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
• Using machine learning to correct model error in data assimilation and forecast applications
• Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
• Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation
• Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
• Reservoir Computing as a Tool for Climate Predictability Studies
• Online learning of both state and dynamics using ensemble Kalman filters

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