Akademik Veri Yönetim Sistemi
SCACR2023: 10th Short Course/Conference on Applied Coastal Research, İstanbul, Türkiye, 4 - 06 Eylül 2023, ss.96
Knowledge of wave climate has become crucial for all
marine activities, e.g. coastal and offshore structure
design, naval architecture and marine renewable energy
exploitation. For this application, it is necessary to
dispose of accurate hindcast wave data. Accurate
hindcast of significant wave height (SWH) allows us to
ensure sustainable and economic development of coastal
and offshore structures and to ensure an accurate
projection of change and trend in SWH.
The performance of 3rd generation spectral wave models
has been evaluated for the Black Sea through several
studies (Amarouche et al., 2021a; Soran et al., 2022). the
results revealed that the accuracy of the wave model for
estimating SWH varies depending on the sea location
and the wind climate of the area concerned by the
simulation (Amarouche et al., 2021b). This variation may
depend on the dominance of the swell compared to the
wind sea in each location. Thus, the spatial variation in
the model accuracy may depend on the precision of the
wind input (Çakmak et al., 2019).
The calibration of wave models often allowed an
improvement in the prediction of SWHs. However,
varying amounts of bias can be observed depending on
the geographical area and local wind conditions. The bias
variation between the different locations also depends on
the swell and wind sea contribution rate. There are
currently several methods proposed for wave bias
correction. Those evaluated by Parker and Hill (2017) are
among these methods. Recently, a deep learning-based
method was proposed for ocean wave correction (Sun et
al., 2022). Thus ANN models are applied for Bias
Correction of Operational Storm Surge Forecasts by
(Tedesco et al., 2023).