Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations
Weather and climate forecasts are inherently uncertain due to chaotic dynamics, imperfect initial conditions, and incomplete representation of the underlying physical processes. Operational ensemble forecasts aim to represent these uncertainties through forecast spread, yet many approaches yield underdispersive estimates, with spread that grows too slowly relative to forecast error. Using the two-scale Lorenz 1996 system as a widely used, controlled testbed, we design a systematic approach to disentangle intrinsic variability, initial-condition perturbations, and stochastic model uncertainty. We compare multiple ensemble configurations and parameterization strategies, including existing d...
| Authors | Birgit Kühbacher, Daan Crommelin, Niki Kilbertus |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 29 June 2026 |
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