A Bayesian method for improving probabilistic wave forecasts by weighting ensemble members. (October 2016) - Harpham, Quillon; Tozer, Nigel; Cleverley, Paul; Wyncoll, David; Cresswell, Doug
Abstract: New innovations are emerging which offer opportunities to improve forecasts of wave conditions. These include probabilistic modelling results, such as those based on an ensemble of multiple predictions which can provide a measure of the uncertainty, and new sources of observational data such as GNSS reflectometry and FerryBoxes, which can be combined with an increased availability of more traditional static sensors. This paper outlines an application of the Bayesian statistical methodology which combines these innovations. The method modifies the probabilities of ensemble wave forecasts based on recent past performance of individual members against a set of observations from various data source types. Each data source is harvested and mapped against a set of spatio-temporal feature types and then used to post-process ensemble model output. A prototype user interface is given with a set of experimental results testing the methodology for a use case covering the English Channel. Highlights: Novel data sources such as GNSS reflectometry and ferryboxes are incorporated. Data is characterised against a set of spatio-temporal feature types. Model output ensemble member weights are updated using a Bayesian data incorporation method. Results are portrayed using an example web interface. The method is evaluated using a pilot application in the English Channel.