A Bayesian method for improving probabilistic wave forecasts by weighting ensemble members. (October 2016)

• Record Type: Journal Article
• Authors: Harpham, Quillon; Tozer, Nigel; Cleverley, Paul; Wyncoll, David; Cresswell, Doug
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 482
• Page End: 493
• Publication Date: 2016-10
• Subjects: Bayesian -- Ensemble -- Feature types -- Forecast -- Observations -- Waves; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.07.015
• Languages: English
• ISSNs: 1364-8152

Abstract

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.