On Energy‐Aware Hybrid Models
2024
Igor Shevchenko | Dan Crisan
Abstract This study proposes deterministic and stochastic energy‐aware hybrid models that should enable simulations of idealized and primitive‐equations Geophysical Fluid Dynamics (GFD) models at low resolutions without compromising on quality compared with high‐resolution runs. Such hybrid models bridge the data‐driven and physics‐driven modeling paradigms by combining regional stability and classical GFD models at low resolution that cannot reproduce high‐resolution reference flow features (large‐scale flows and small‐scale vortices) which are, however, resolved. Hybrid models use an energy‐aware correction of advection velocity and extra forcing compensating for the drift of the low‐resolution model away from the reference phase space. The main advantages of hybrid models are that they allow for physics‐driven flow recombination within the reference energy band, reproduce resolved reference flow features, and produce more accurate ensemble forecasts than their classical GFD counterparts. Hybrid models offer appealing benefits and flexibility to the modeling and forecasting communities, as they are computationally cheap and can use both numerically‐computed flows and observations from different sources. All these suggest that the hybrid approach has the potential to exploit low‐resolution models for long‐term weather forecasts and climate projections thus offering a new cost effective way of GFD modeling. The proposed hybrid approach has been tested on a three‐layer quasi‐geostrophic model for a beta‐plane Gulf Stream flow configuration. The results show that the low‐resolution hybrid model reproduces the reference flow features that are resolved on the coarse grid and also gives a more accurate ensemble forecast than the physics‐driven model.
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