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Machine Ocean – Combining Machine Learning and Earth Observations to Improve Simulations of Turbulent Behavior in the Earth System


The overarching hypothesis for Machine Ocean is that “the present explosion in volume, variety and velocity (VVV) of Earth Observation acquisition (as spearheaded by the Copernicus, in particular the Sentinel 1 mission) combined with new methods to harvest information from big data will allow us to gain further insights into, and significantly reduce the uncertainty in, parameterization of momentum transfer between atmosphere and ocean.” Vertical momentum transfer is one of the most important process in the Earth System, influencing the transfer of carbon, oxygen, heat, freshwater and other quantities between the different spheres, yet possibly the hardest process to measure. The transfer occurs on small horizontal and temporal scales, so it is almost always necessary to parameterize in numerical simulations. The use of machine learning methods to directly predict turbulent stress in Reynolds-averaged Navier–Stokes equations has recently been proposed, and developed for simplified setups, but the field of machine learning applications in fluid dynamics in general and for momentum transfer and larger scale atmosphere models in particular, is in its infancy.

Project leader: Cecilie Mauritzen

Started: 2020

Ends: 2024

Category: Øvrige forskningsinstitutter

Sector: Instituttsektor

Budget: 11521000


Address: Oslo