A probabilistic view on modelling weather regimes

Abstract

AbstractThe statistical modelling of weather regimes encompasses the definition of a framework involving a dimensionality reduction step to reduce the highly sparse feature space of weather anomaly maps, and unsupervised learning techniques to correctly categorize regimes. Across the literature's methodology, the two stages stick to a recurrent scheme: Empirical Orthogonal Functions or Principal Component Analysis are used to assess the former; a standard K‐Means clustering algorithm maps each datapoint to the closest‐matching regime. However, such a combination has to cope with an overall reduction in the modelling accuracy. In our study, we re‐think both the two steps in favour of a more dynamic methodology which we apply to the last 42‐years winters' geopotential anomaly maps in the North Atlantic‐European zone. The dimensionality reduction is tackled by means of Variational Autoencoders, leading to better compressed feature spaces, enhancing the datapoints separability. Finally, we employ two probabilistic clustering methods based on Gaussian and Dirichlet Process mixture models, enabling a more faithful recognition of weather regimes, and allowing to reproduce their dynamics and transitions.