Deep Learning for Ocean Forecasting: A Comprehensive Review of Methods, Applications, and Datasets

Area of Science:

• Oceanography
• Artificial Intelligence
• Data Science

Background:

• Accurate ocean forecasting is a long-standing scientific challenge.
• Traditional numerical ocean prediction (NOP) faces limitations in representing physical processes and assimilating data.
• Massive oceanographic data streams present opportunities for advanced analytical methods.

Purpose of the Study:

• To provide a comprehensive review of deep-learning-based ocean forecasting (DLOF).
• To explore DLOF model architectures, spatiotemporal scales, and interpretability.
• To assess the feasibility of hybrid theory-driven and data-driven models for ocean prediction.

Main Methods:

• Review of state-of-the-art deep learning models applied to ocean forecasting.
• Evaluation of DLOF using datasets, benchmarks, and cloud computing resources.
• Analysis of hybrid architectures combining numerical and deep learning approaches.

Main Results:

• Deep learning shows significant progress in revolutionizing ocean forecasting.
• DLOF models offer novel ways to extract insights from spatiotemporal oceanographic data.
• Hybrid architectures integrating NOP and DLOF demonstrate feasibility.

Conclusions:

• Deep-learning-based ocean forecasting (DLOF) is a promising complement to traditional methods.
• Further research into DLOF, including interpretability and hybrid models, is crucial.
• The review identifies current limitations and future trends in DLOF research.