Machine learning models show promise for offshore atmospheric flux prediction

Researchers have developed and tested machine learning techniques, specifically Neural Networks and Random Forests, to parameterize turbulent fluxes in offshore environments. Data from three distinct offshore sites were used to train models for momentum and heat flux, with vertical gradients as input. The machine learning models demonstrated performance competitive with, and in some cases superior to, the established COARE-3 model for offshore fluxes, particularly for heat flux. AI

IMPACT This research could lead to more accurate modeling of atmospheric conditions in offshore environments, potentially benefiting weather forecasting and climate studies.

RANK_REASON The cluster contains an academic paper detailing new research findings. [lever_c_demoted from research: ic=1 ai=1.0]

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• California
• CASPER-West FLIP
• COARE-3
• Martha's Vineyard Coastal Observatory
• MVCO
• Neural Networks
• Random Forests

• paper
• other

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