Work

Physics‐Guided Deep Learning for Modeling Single‐Point Wave Spectra Using Wind Inputs of Two Resolutions

Year: 2025

Type: article

Abstract: Directional Wave Spectra (DWSs) are essential for various applications such as seafaring and ocean engineering. Traditionally, DWSs are modeled with numerical wave models, which, despite their solid physical basis, are often computationally expensive. For any given point in the ocean, once the recent prior wind fields that could affect this point are known, the current DWS at this location can be largely determined. This is because local wave energy is generated by either local winds or recent prior remote winds. This fundamental physical principle can be leveraged for the statistical modeling of single‐point DWSs. This study presents a deep learning approach to directly model single‐point DWS using wind inputs. We utilize wind fields with two different spatial resolutions: specifically, a high‐resolution, small‐area, short‐time local wind field for local wind‐sea spectra, and a low‐resolution, large‐area, long‐time wind field for remotely generated swell spectra. A tailored network architecture and loss function are developed to capture the relevant information from both wind scales. The performance of the proposed model was evaluated using both open ocean spectra from ERA5 and coastal spectra from numerical wave hindcasts. Results indicate that the deep learning model can effectively and efficiently simulate DWSs in both open oceans and coastal regions without relying on predefined spectral shapes, demonstrating its potential for applications in wave forecasting and wave climate studies.

Source: Journal of Geophysical Research Machine Learning and Computation

DOI: 10.1029/2024jh000492

Authors: Tianxiang Gao, Haoyu Jiang

Institutions: Shenzhen University, Shenzhen Research Institute of China University of Geosciences, Qingdao National Laboratory for Marine Science and Technology

Language: English

Topic: Ocean Waves and Remote Sensing

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,500