Deriving spatial wave data from a network of buoys and ships
Raphaël E.G. Mounet, Jiaxin Chen, Ulrik Dam Nielsen, Astrid H. Brodtkorb, Ajit C. Pillai, Ian Ashton, Edward C. C. Steele
Abstract
The real-time provision of high-quality estimates of the ocean wave parameters at appropriate spatial resolutions are essential for the sustainable operations of marine structures. Machine learning affords considerable opportunity for providing additional value from sensor networks, fusing metocean data collected by various platforms. Exploiting the ship-as-a-wave-buoy concept, this article proposes the integration of vessel-based observations into a wave-nowcasting framework. Surrogate models are trained using a high-fidelity physics-based nearshore wave model to learn the spatial correlations between grid points within a computational domain. The performance of these different models are evaluated in a case study to assess how well wave parameters estimated through the spectral analysis of ship motions can perform as inputs to the surrogate system, to replace or complement traditional wave buoy measurements. The benchmark study identifies the advantages and limitations inherent in the methodology incorporating ship-based wave estimates to improve the reliability and availability of regional sea state information.