Developing an Offshore Machine Learning Surface Layer Scheme
Turbulent fluxes between the surface and the atmosphere are typically parameterized using empirically fit relationships. Here we test machine learning techniques for fitting the relationship for the offshore environment. To do that, data from three offshore sites are used: the Martha's Vineyard Coastal Observatory (MVCO) air-sea interaction tower, the FINO1 research platform, and the CASPER-West FLIP research vessel deployed off the coast of California. Two machine learning methods were employed: Neural Networks (NN) and Random Forests (RF). Because the observational sites had towers with measurements at different levels, the vertical differences were input as gradients. Models were built...
| Authors | Susan Dettling, Sue Ellen Haupt, Thomas Brummet et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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