Unsupervised discovery of extreme weather events using universal representations of emergent organization.

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice.

A large-scale dataset for training deep learning segmentation and tracking of extreme weather.

As Earth's climate continues to undergo changes, it is imperative to gain understanding of how high-impact, extreme weather events will change. Researchers are increasingly relying on data-driven, learning-based approaches for the detection and tracking of extreme weather events. While several attempts to generate datasets of hand-labeled weather or climate have been made, a significant challenge has been to gather a sufficient number of expert-annotated samples.