A new methodology for the extension of the impact of data assimilation on ocean wave prediction

It is a common fact that the majority of today's wave assimilation platforms have a limited, in time, ability of affecting the final wave prediction, especially that of long-period forecasting systems. This is mainly due to the fact that after “closing” the assimilation window, i.e., the time that the available observations are assimilated into the wave model, the latter continues to run without any external information. Therefore, if a systematic divergence from the observations occurs, only a limited portion of the forecasting period will be improved. A way of dealing with this drawback is proposed in this study: A combination of two different statistical tools—Kolmogorov–Zurbenko and Kalman filters—is employed so as to eliminate any systematic error of (a first run of) the wave model results. Then, the obtained forecasts are used as artificial observations that can be assimilated to a follow-up model simulation inside the forecasting period. The method was successfully applied to an open sea area (Pacific Ocean) for significant wave height forecasts using the wave model WAM and six different buoys as observational stations. The results were encouraging and led to the extension of the assimilation impact to the entire forecasting period as well as to a significant reduction of the forecast bias.