Stochastic recurrent neural network for modeling atmospheric regimes

Complex multiscale dynamics of the atmosphere in extratropical latitudes includes various persistent atmospheric regimes with the residence time up to several weeks. Identification, simulation and prediction of such dynamics remains one of the challenging problems. In this work we use a stochastic recurrent neural network (RNN) with specific architecture to address this problem, appealing to RNN’s ability to handle memory effects well. The proposed RNN connects two types of variables: (i) a low-dimensional representation of the physical variables via Principal Component Analysis (PCA), and (ii) Kernel PCA variables which serve to better represent the target atmospheric regimes [1]. The stochastic component of the RNN has a simple form which allows us to analytically write Bayesian log-posterior and log-likelihood functions to train and cross-validate the model given the particular dataset.
Using the observed and climate-model-generated winter geopotential height data in the Northern Hemisphere, we show that the proposed stochastic model is able to reproduce/predict various dynamical properties and distributions of the target regimes in the kernel space, as well as to reconstruct kernel variables from a low-dimensional representation of the original spatio-temporal field.

References
1. Mukhin et al. (2022). Revealing recurrent regimes of mid-latitude atmospheric variability using novel machine learning method. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(11).