metadata
license: mit
tags:
- extreme
- instabilities
ExtremeEventSensor
This dataset accompanies the NeurIPS 2026 paper Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events.
TLDR: Small ensembles of perturbed, low-cost simulations reveal locally unstable directions in the dynamics. We use that information to guide learned corrections to coarse trajectories.
What’s included
- LowDim: Trajectories from a three-dimensional nonlinear system exhibiting bursts, including reference, coarse, nudged, and perturbed ensemble simulations.
- QG: Data from a two-layer quasigeostrophic model, including upper and lower streamfunction with additional topography. Includes fields from reference, coarse, nudged, and ensemble simulations.
For a short demonstration, see the blog post. Full details about methods and examples are in the paper, and the accompanying code is on GitHub.