Evaluation of Deep Neural Operator Models toward Ocean Forecasting
Ellery Rajagopal Anantha N. S. Babu Tony Ryu Patrick J. Haley Jr. Chris Mirabito Pierre F. J. Lermusiaux
Data-driven, deep-learning modeling frameworks have been recently developed
for forecasting time series data. Such machine learning models may be useful in
multiple domains including the atmospheric and oceanic ones, and in general,
the larger fluids community. The present work investigates the possible
effectiveness of such deep neural operator models for reproducing and
predicting classic fluid flows and simulations of realistic ocean dynamics. We
first briefly evaluate the capabilities of such deep neural operator models
when trained on a simulated two-dimensional fluid flow past a cylinder. We then
investigate their application to forecasting ocean surface circulation in the
Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution
data-assimilative simulations employed for real sea experiments. We confirm
that trained deep neural operator models are capable of predicting idealized
periodic eddy shedding. For realistic ocean surface flows and our preliminary
study, they can predict several of the features and show some skill, providing
potential for future research and applications.