Towards Unsupervised Segmentation of Extreme Weather Events

Abstract

Extreme weather is one of the main mechanisms through which climate change will directly impact human society. Coping with such change as a global community requires markedly improved understanding of how global warming drives extreme weather events. While alternative climate scenarios can be simulated using sophisticated models, identifying extreme weather events in these simulations requires automation due to the vast amounts of complex high-dimensional data produced. Atmospheric dynamics, and hydrodynamic flows more generally, are highly structured and largely organize around a lower dimensional skeleton of coherent structures. Indeed, extreme weather events are a special case of more general hydrodynamic coherent structures. We present a scalable physics-based representation learning method that decomposes spatiotemporal systems into their structurally relevant components, which are captured by latent variables known as local causal states. For complex fluid flows we show our method is capable of capturing known coherent structures, and with promising segmentation results on CAM5.1 water vapor data we outline the path to extreme weather identification from unlabeled climate model simulation data.

Towards the Accurate Automatic Detection of Mesoscale Convective Systems in Remote Sensing Data: From Data Mining to Deep Learning Models and Their Applications

“…Machine learning (ML) and deep learning (DL) methods were shown to be useful tools in problems of pattern recognition, visual object detection and other computer vision tasks [42][43][44][45][46]. In recent studies, successful application of ML methods was shown when identifying extreme weather events in reanalyses data [47][48][49]. Most research groups focus on the identification of synoptic-scale atmospheric phenomena, such as tropical cyclones [50,51] and atmospheric rivers [52], because of clear representation of these events in most observational data and simulated atmospheric dynamics data.…”