Evaluation of Deep Neural Operator Models toward Ocean Forecasting

TL;DR

This paper evaluates deep neural operator models for ocean forecasting, demonstrating their ability to predict fluid flows and ocean surface circulation with potential for future applications.

Contribution

It introduces the application of deep neural operator models to ocean dynamics, showing their effectiveness in both idealized and realistic scenarios.

Findings

Models predict idealized periodic eddy shedding.

Models show skill in forecasting realistic ocean surface flows.

Potential for future ocean modeling applications.

Abstract

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.