| description abstract | Sophisticated wave models like the Wave Model (WAM) and Simulating Waves Nearshore (SWAN)/WAVEWATCH are used nowadays along with atmospheric models to produce forecasts of ocean wave conditions. These models are generally run operationally on large ocean-scale domains. In many coastal areas, on the other hand, operational forecasting is not performed for a variety of reasons, yet the need for wave forecasts remains. To address such cases, the production of forecasts through the use of artificial neural networks and buoy measurements is explored. A modeling strategy that predicts wave heights up to 24 h on the basis of judiciously selected measurements over the previous 7 days was examined. A detailed investigation of this strategy using data from six National Data Buoy Center (NDBC) buoys with diverse geographical and statistical properties demonstrates that 6-h forecasts can be obtained with a high level of fidelity, and forecasts up to 12 h showed a correlation of 67% or better relative to a full year of data. One limitation observed was the inability of the artificial neural network model to correctly predict the magnitude of the highest waves; although the occurrence of high waves was predicted, the peaks were underestimated. The inclusion of several years of data and the judicious selection of the training set, especially the inclusion of extreme events, were shown to be crucial for the model to recognize interannual variability and provide more reliable forecasts. Real-time simulations performed for April 2005 demonstrate the efficiency of this technology for operational forecasting. | |
