Gray Swan Factory: Making Extreme Events from Ordinary Cyclones

TL;DR

This paper introduces a method to generate plausible extreme weather events, called gray swans, from existing observational data using gradient descent on weather models, demonstrated with hurricanes.

Contribution

A novel approach to produce gray swan events from observational records by perturbing initial conditions with a differentiable weather prediction model.

Findings

Successfully generated a Sandy-like gray swan from Hurricane Fiona

Perturbations to extratropical states significantly influence storm outcomes

Similar gray swans found for four other Atlantic hurricanes

Abstract

Gray swans, plausible but unobserved extreme events, broaden our understanding of the range of hazards beyond those observed during the short observational record. They are useful for dynamical studies, synthetic training data, emergency planning, infrastructure design, and insurance hazard assessment. We propose a method to produce gray swans from the observational record using gradient descent on a loss function with a differentiable weather prediction model. Minimizing the loss corresponds to perturbed initial conditions that produce a measurable outcome at a future time, subject to constraints, such as the size of the initial perturbations. We illustrate the method by altering hurricane Fiona (2022), which tracked northward over the Atlantic Ocean, to produce a gray-swan outcome similar to hurricane Sandy (2012), which made landfall on the East Coast of the United States after a unique westward turn. The Fiona gray-swan solution, involving small perturbations to reanalysis initial conditions, produces an extratropical cyclone with a Sandy-like track, a warm core, and a minimum sea-level pressure more than 20 hPa lower than Sandy. Perturbations to the extratropical state are more important than to the hurricane, leading to interactive strengthening, and merger, of an upper-level trough and the hurricane. Similar gray swans are found for four other Atlantic hurricanes. A major weakness of this work is that the hurricane core is not resolved by the model used for optimization, and the impact of this is unknown. Furthermore, although these solutions present plausible outcomes, they do not inform on their probability of occurrence.