Data fusion with Gaussian processes for estimation of environmental hazard events

• Published in: Environmetrics
• Main Authors: Xiong, X, Youngman, BD, Economou, T
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley / International Environmetrics Society (TIES) 2020
• Subjects: Data integration; change-of-support; Gaussian processes; spatial interpolation; model validation; European windstorm; natural hazards; North Atlantic
• Online Access: http://hdl.handle.net/10871/122933; https://doi.org/10.1002/env.2660

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record The data that support the findings of this study are openly available at https://wisc.climate.copernicus.eu/wisc/#/ help/products#stormtrack_download, WISC (2019). Environmental hazard events such as extra-tropical cyclones or windstorms that develop in the North Atlantic can cause severe societal damage. Environmental hazard is quantified by the hazard footprint, a spatial area describing potential damage. However, environmental hazards are never directly observed, so estimation of the footprint for any given event is primarily reliant on station observations (e.g., wind speed in the case of a windstorm event) and physical model hindcasts. Both data sources are indirect measurements of the true footprint, and here we present a general statistical framework to combine the two data sources for estimating the underlying footprint. The proposed framework extends current data fusion approaches by allowing structured Gaussian process discrepancy between physical model and the true footprint, while retaining the elegance of how the "change of support" problem is dealt with. Simulation is used to assess the practical feasibility and efficacy of the framework, which is then illustrated using data on windstorm Imogen Natural Environment Research Council (NERC)