Author

S. Schemm

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#machine learning Preprint Sep 2026

Evaluating Dynamical Fidelity through Predictive Structure in Physical Representations

A practical framework for evaluating dynamical fidelity through predictive structure in physical representation spaces is introduced and demonstrated in atmospheric forecasting using ERA5 representations of planetary-wave activity and Northern Annular Mode evolution and evaluates Pangu-Weather, GraphCast, and FengWu.

Oskar Bohn Lassen, Joao Paulo de Souza Boger, S. Driscoll et al. · 0 citations

#machine learning Preprint Sep 2026

Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models

The results show that MLWP models can misrepresent the known scale transfer of kinetic energy despite producing skilful weather forecasts.

Jia-Kai Chen, Joel Oskarsson, S. Driscoll et al. · 0 citations

#machine learning Preprint Sep 2026

Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models

Stochastically perturbed weights (SPW) is at present a tuning procedure rather than a plug-and-play recipe, and its main failure mode is a coherent whole-field offset that overdisperses the domain mean, and restricting the noise to coarse scales or perturbing the initial conditions each repair part of it.

Simon Adamov, O. Fuhrer, R. Knutti et al. · 0 citations

#machine learning Preprint Sep 2026

Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling

Machine-learnt corrections can complement numerical weather prediction provided that they operate stably within an evolving numerical model. In this study, we couple the Met Office (UKMO) Unified Model (UM) with distributed reinforcement-learning agents through rank-local tensors. A column-aware deep deterministic poli...

Pritthijit Nath, S. Schemm, Peter Haynes et al. · 0 citations