Researchers Compare ML and Physics Atmospheric Forcings
The arXiv paper compares **ocean forecasts** driven by `ML-based` and `physics-based` atmospheric forcings, directly contrasting forecast outputs produced under the two forcing approaches. It examines how substituting or augmenting traditional physics-based atmospheric inputs with machine-learning-derived forcings alters ocean forecast behavior and outcomes.
Scoring Rationale
An arXiv research paper that directly compares ML and physics forcing methods, relevant to researchers and operational modelers; notable within modeling communities but not industry-defining.
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