A Bayesian method for improving probabilistic wave forecasts by weighting ensemble members
| Autor: | Nigel Tozer, David Wyncoll, Doug Cresswell, Quillon Harpham, Paul Cleverley |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Environmental Engineering
010504 meteorology & atmospheric sciences Ensemble forecasting Computer science business.industry Ecological Modeling 0208 environmental biotechnology Bayesian probability Probabilistic logic 02 engineering and technology computer.software_genre Machine learning 01 natural sciences Ensemble learning 020801 environmental engineering Weighting GNSS reflectometry Set (abstract data type) Feature (machine learning) Data mining Artificial intelligence business computer Software 0105 earth and related environmental sciences |
| Zdroj: | Environmental Modelling & Software. 84:482-493 |
| ISSN: | 1364-8152 |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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