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

• Main Authors: Li, Zhe, Xu, Ronghui, Hu, Jilin, Peng, Zhong, Lu, Xi, Guo, Chenjuan, Yang, Bin
• Format: Report
• Language: unknown
• Published: arXiv 2024
• Subjects: Machine Learning cs.LG; Atmospheric and Oceanic Physics physics.ao-ph; FOS Computer and information sciences; FOS Physical sciences; Orca
• Online Access: https://dx.doi.org/10.48550/arxiv.2407.20053; https://arxiv.org/abs/2407.20053

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 ...