Comparison of deterministic and ensemble weather forecasts on ship sailing speed optimization

Ship sailing speed optimization models are constructed based on prediction of ship fuel consumption, whose accuracy is highly influenced by the quality of sea and weather information. In this study, we develop two fusion methods for combining external meteorological data with ship noon report data, including the rhumb line based fusion method and the direct fusion method, and compare them in terms of accuracy in providing meteorological data. Next, we propose a framework based on the better data fusion strategy for comparing the impacts of deterministic and ensemble weather forecasts on ship speed optimization performance, enabling the evaluation of ship fuel consumptions under different speed plans based on weather forecast data available before departure. Results show that speed optimization based on ensemble weather forecasts has greater potential than that based on deterministic weather forecasts to diminish ship fuel consumption and thus to reduce greenhouse gas emissions.