Improving the sea state forecasts by using local wave observations and the ensembleBMA software

Abstract

Abstract The main goal of this study is to investigate if the publicly available sea state forecasts for the Aran Islands region in the Republic of Ireland can be improved. This improvement is achieved by using the combination of local scale sea state forecasts and Bayesian Model Averaging techniques. The question of a good forecast has been around since the start of forecasting. With current state-of-the-art numerical models, computational power, and vast data availability, we consider whether it is possible to improve model forecasts only by using the combination of publicly available forecasts, free open-source software, and very moderate computational power. It is shown that it is possible to improve the sea state forecast by at least $ 1\% $ , and in some cases up to $ 8\% $ . The reduction of error is between $ 6\% $ and $ 48\% $ . With a more careful and specific selection of training parameters, it is possible to improve the forecast accuracy even more. The possibility of extending this local improvement to the whole coastal area around the island of Ireland is explored. Unfortunately, it is currently impossible, due to a lack of live data buoys in the coastal waters. Nonetheless, it is shown that the proposed process is simple and can be implemented by anyone whose livelihood depends on an accurate sea state forecast. It does not require large computational power, model forecasts are publicly available, and there is minimal to no training in forecasting and statistics required to enable one to perform such improvements for one’s area of interest, provided one has access to live wave data.