Orca: Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models

TL;DR

Orca leverages large language models with a novel spatiotemporal encoding to accurately estimate significant wave height from limited observational data, outperforming traditional methods.

Contribution

The paper introduces Orca, a framework that enhances LLMs with spatiotemporal encoding for marine wave height estimation using scarce data.

Findings

Achieves state-of-the-art SWH estimation accuracy.

Effectively utilizes limited buoy observational data.

Demonstrates robustness in marine environment modeling.

Abstract

Significant wave height (SWH) is a vital metric in marine science, and accurate SWH estimation is crucial for various applications, e.g., marine energy development, fishery, early warning systems for potential risks, etc. Traditional SWH estimation methods that are based on numerical models and physical theories are hindered by computational inefficiencies. Recently, machine learning has emerged as an appealing alternative to improve accuracy and reduce computational time. However, due to limited observational technology and high costs, the scarcity of real-world data restricts the potential of machine learning models. To overcome these limitations, we propose an ocean SWH estimation framework, namely Orca. Specifically, Orca enhances the limited spatio-temporal reasoning abilities of classic LLMs with a novel spatiotemporal aware encoding module. By segmenting the limited buoy observational data temporally, encoding the buoys' locations spatially, and designing prompt templates, Orca capitalizes on the robust generalization ability of LLMs to estimate significant wave height effectively with limited data. Experimental results on the Gulf of Mexico demonstrate that Orca achieves state-of-the-art performance in SWH estimation.