Phase-resolved ocean wave forecast with simultaneous current estimation through data assimilation

TL;DR

This paper introduces an advanced data assimilation method that simultaneously estimates ocean currents and forecasts surface waves, improving accuracy over previous models by integrating iterative ensemble Kalman filtering with a high-order spectral approach.

Contribution

The paper develops the IEnKF-HOS-C method, enabling simultaneous estimation of ocean currents and wave states, enhancing forecast accuracy and applicability to real data.

Findings

Accurately estimates ocean current fields in synthetic tests.

Improves wave forecast accuracy compared to previous methods.

Successfully recovers current speed from real radar data.

Abstract

In Wang & Pan (J. Fluid Mech., vol. 918, A19, 2021), the authors developed the first ensemble-based data assimilation (DA) capability for the reconstruction and forecast of ocean surface waves, namely the EnKF-HOS method coupling an ensemble Kalman filter (EnKF) and the high-order spectral (HOS) method. In this work, we continue to enrich the method by allowing it to simultaneously estimate the ocean current field, which is in general not known a priori and can (slowly) vary in both space and time. To achieve this goal, we incorporate the effect of ocean current (as unknown parameters) on waves to build the HOS-C method as the forward prediction model, and obtain a simultaneous estimation of (current) parameters and (wave) states via an iterative EnKF (IEnKF) method that is necessary to handle the complexity in this DA problem. The new algorithm, named IEnKF-HOS-C method, is first tested in synthetic problems with various forms (steady/unsteady, uniform/non-uniform) of current. It is shown that the IEnKF-HOS-C method is able to not only estimate the current field accurately, but also boost the prediction accuracy of the wave field (even) relative to the state-of-the-art EnKF-HOS method. Finally, using real data from a shipborne radar, we show that the IEnKF-HOS-C method successfully recovers the current speed that matches the in situ measurement by a floating buoy.