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

Abstract

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.

Wave height characteristics in the Mediterranean Sea by means of numerical modeling, satellite data, statistical and geometrical techniques

“…The second approach reveals non trivial deviations between the modeled and recorded data that should be essentially taken into account in assimilation or other optimization procedures (Lionello et al 1992(Lionello et al , 1995Breivik and Reistad 1994;Janssen 2000;Kalnay 2002;Abdalla et al 2005a, b;Galanis et al 2006Galanis et al , 2009. In this framework, advances from a new branch of mathematics, information geometry (Amari 1985;Amari and Nagaoka 2000;Arwini andDodson 2007, 2008), are employed in order to optimally estimate the distances between different data sets.…”

“…The former approach is based on the use of a state of the art numerical wave prediction system: the WAM model (WAMDIG 1988;Komen et al 1994;Janssen 2000Janssen , 2004Bidlot et al 2007; Galanis et al 2006Galanis et al , 2009Emmanouil et al 2007). This is one of the most well tested wave models being used today by several operational and research centres.…”

Joint Environmental Data at Five European Offshore Sites for Design of Combined Wind and Wave Energy Devices

“…Assimilation techniques are applied for the correction of initial wind and wave conditions [9,10]. By comparing with measurements, the results from the numerical model are generally correlated to the measurements.…”

Wave height characteristics in the north Atlantic ocean: a new approach based on statistical and geometrical techniques

“…Such systems have been proved successful for the simulation of the general sea state conditions on global or intermediate scale. However, when focusing on local characteristics usually systematic errors appear (see Janssen et al 1987;Chu and Cheng 2007;Makarynskyy 2004Makarynskyy , 2005Greenslade and Young 2005;Galanis et al 2006Galanis et al , 2009Emmanouil et al 2007). This is a multi-parametric problem in which several different issues are involved: The strong dependence of wave models on the corresponding wind input, the inability to capture sub-scale phenomena, the parametrization of certain wave properties especially in areas with complicated coastal formation where overshadowing and inaccurate refraction wave features emerge, as well as the lack of a dense observation network which, as in the case of atmospheric parameters over land, could help on the systematic correction of initial conditions.…”

“…(Kalman 1960;Kalman and Bucy 1961;Rao et al 1997;Galanis and Anadranistakis 2002;Kalnay 2002;Galanis et al 2006Galanis et al , 2009.…”