Phase-resolved ocean wave forecast with ensemble-based data assimilation

TL;DR

This paper introduces a novel ensemble-based data assimilation method combining high-order spectral modeling with the ensemble Kalman filter to improve phase-resolved ocean wave forecasts, effectively incorporating measurement data to enhance accuracy and phase retention.

Contribution

The paper develops a coupled HOS-EnKF approach with a special algorithm to handle measurement-region mismatch, significantly improving phase-resolved wave forecast accuracy.

Findings

Achieves higher accuracy than HOS-only methods

Retains wave phase information over long forecast times

Successfully tested with synthetic and real radar data

Abstract

Through ensemble-based data assimilation (DA), we address one of the most notorious difficulties in phase-resolved ocean wave forecast, regarding the deviation of numerical solution from the true surface elevation due to the chaotic nature of and underrepresented physics in the nonlinear wave models. In particular, we develop a coupled approach of the high-order spectral (HOS) method with the ensemble Kalman filter (EnKF), through which the measurement data can be incorporated into the simulation to improve the forecast performance. A unique feature in this coupling is the mismatch between the predictable zone and measurement region, which is accounted for through a special algorithm to modify the analysis equation in EnKF. We test the performance of the new EnKF-HOS method using both synthetic data and real radar measurements. For both cases (though differing in details), it is shown that the new method achieves much higher accuracy than the HOS-only method, and can retain the phase information of an irregular wave field for arbitrarily long forecast time with sequentially assimilated data.