Extreme event prediction improves with local instability sensing
Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events
Machine Learning
Summary
Predicting rare, extreme events in chaotic systems is tough because these events depend on short-term instabilities that are not common in usual data. The authors developed a method that uses a group of slightly different simulations to detect these local instabilities without complex calculations. By feeding this information into machine learning models, the method improves the prediction of rare events, even with limited training data. They tested their approach on both simple chaotic systems and more complex fluid models, showing better accuracy compared to traditional methods.
What this means in practice
- •For weather forecasting teams: Enhance rare extreme weather event predictions using local instability signals from limited simulation data.
- •For climate model developers: Improve data efficiency of climate model emulators for better representation of rare events with fewer simulation runs.
Tested on simulated data.
Authors
Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis, Themistoklis P. Sapsis
Abstract
Extreme events in chaotic systems are difficult to learn from short trajectories because they are controlled by transient finite-time instability rather than by frequently observed bulk dynamics. We propose a mechanism-aware conditioning plug-in framework that turns a nudged coarse ensemble into a non-intrusive sensor of local instability geometry. In the small-noise regime, the ensemble covariance aggregates the same finite-time deformation kernels that govern local instability, providing a Jacobian-free proxy for the local amplification structure around a synchronized coarse trajectory. A small FiLM module injects statistics of this ensemble geometry into an otherwise unchanged backbone while leaving the coarse simulator unchanged. We demonstrate this interface in two distinct pipelines: a Transformer-style residual-attention corrector for a controlled low-dimensional chaotic system and a probabilistic recurrent STORN corrector for topographic two-layer quasi-geostrophic (QG) flow. In the low-dimensional benchmark, ensemble covariance directions co-activate with OTD modes and FiLM conditioning improves 99th-percentile exceedance-frequency errors over an identical no-context Transformer baseline. In QG, a fixed ensemble-conditioned FiLM-STORN model trained on only \(50\) time units substantially improves long-horizon rare-event statistics in the data-limited regime, including density-tail errors, exceedance frequencies, and spatial exceedance-area distributions relative to an unconditioned STORN trained on the same data; on averaged high-threshold exceedance diagnostics, it also outperforms the baseline STORN trained with $20$ times more high-resolution data. These results show that local instability geometry is not merely interpretable post hoc, but an actionable conditioning signal for data-efficient rare-event emulation.