Local Ocean Wave Field Estimation Using a Deep Generative Model of Wave Buoys

Estimating oceanic wave fields from sparse observations has been a long-standing challenge in oceanography and an important environmental metric desired for maritime operations. The requirement for frequent real-time updates of the wave field within the local area poses difficulties for data assimilation approaches, as they can be computationally complex and rely on external atmospheric forcing. The relationship between the wave field and local sparse observations is embedded in reanalysis or hindcast data. We propose a data-driven deep-learning model capable of estimating the local wave field using sparsely distributed floating wave buoys. This novel model simultaneously produces wave height, period, and direction, along with their respective uncertainties. In a year-long test period within a local fjord region characterized by complex wave patterns influenced by intricate geography, the proposed model demonstrates remarkable accuracy and efficiency in estimating wave fields. This study demonstrates the promising potential of data-driven deep-learning models as an alternative to rapidly estimating the wave field.