Machine learning weather models struggle with energy flow and error growth patterns
Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models
Machine Learning
Summary
The study looks at how well several advanced machine learning weather models handle the flow of energy at different scales in the atmosphere and how errors spread out over time. The authors find that while some models mimic certain energy patterns well, others do not capture important ways energy moves between scales. All models have trouble showing the quick early spread of small errors known as the butterfly effect. This means that even though the machine learning models can predict weather well, they don't fully represent how energy and errors behave in the real atmosphere.
What this means in practice
- •For weather prediction teams: Improve weather forecast reliability by identifying where machine learning models misrepresent atmospheric energy flows and error growth.
- •For climate model developers: Use insights about scale interactions and energy spectra to enhance probabilistic climate simulations using machine learning components.
Authors
Jiakai Chen, Joel Oskarsson, Simon Driscoll, Sebastian Schemm
Abstract
This study analyses kinetic energy (KE) spectra, difference kinetic energy (DKE) spectra, and signatures of KE transfer across spatial scales in four state-of-the-art probabilistic machine learning weather prediction (MLWP) models: NeuralGCM-ENS, FourCastNet 3, AIFS-ENS, and GenCast. Results are compared with those from the physics-based numerical weather prediction model IFS-ENS. While NeuralGCM-ENS successfully reproduces the expected upscale transfer of KE, noise injection at its encoder stage underestimates mesoscale KE. Conversely, AIFS-ENS, GenCast, and FourCastNet 3 produce realistic KE spectral magnitudes but do not capture the expected upscale transfer of KE. In particular, AIFS-ENS and GenCast, which employ spatially uncorrelated stochastic perturbations, exhibit enhanced accumulation of KE at high wavenumbers. All examined models exhibit upscale error growth, reflected by the progressive shift of the DKE spectral peak toward larger wavelengths over time. However, the MLWP models struggle to reproduce the rapid initial growth of ensemble spread at small spatial scales associated with the butterfly effect. The results show that MLWP models can misrepresent the known scale transfer of kinetic energy despite producing skilful weather forecasts.