Machine Ocean - Using Machine Learning on Earth Observations to improve simulation of turbulent behavior in the Earth System
The overarching hypothesis for Machine Ocean is that the present explosion in volume, variety and velocity 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, ice andocean. 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 the flow field or stresses in Navier-Stokes has recently been proposed and developed, 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.