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Data fusion with Gaussian processes for estimation of environmental hazard events

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  • Xiaoyu Xiong
  • Benjamin D. Youngman
  • Theodoros Economou

Abstract

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.

Suggested Citation

  • Xiaoyu Xiong & Benjamin D. Youngman & Theodoros Economou, 2021. "Data fusion with Gaussian processes for estimation of environmental hazard events," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    • Handle: RePEc:wly:envmet:v:32:y:2021:i:3:n:e2660
    DOI: 10.1002/env.2660
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    References listed on IDEAS

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    1. Pulong Ma & Emily L. Kang, 2020. "Spatio‐Temporal data fusion for massive sea surface temperature data from MODIS and AMSR‐E instruments," Environmetrics, John Wiley & Sons, Ltd., vol. 31(2), March.
    2. O. Gilani & V. J. Berrocal & S. A. Batterman, 2019. "Nonstationary spatiotemporal Bayesian data fusion for pollutants in the near‐road environment," Environmetrics, John Wiley & Sons, Ltd., vol. 30(7), November.
    3. Gavin Shaddick & Matthew L. Thomas & Amelia Green & Michael Brauer & Aaron van Donkelaar & Rick Burnett & Howard H. Chang & Aaron Cohen & Rita Van Dingenen & Carlos Dora & Sophie Gumy & Yang Liu & Ran, 2018. "Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(1), pages 231-253, January.
    4. C. J. Wilkie & C. A. Miller & E. M. Scott & R. A. O'Donnell & P. D. Hunter & E. Spyrakos & A. N. Tyler, 2019. "Nonparametric statistical downscaling for the fusion of data of different spatiotemporal support," Environmetrics, John Wiley & Sons, Ltd., vol. 30(3), May.
    5. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    6. Montserrat Fuentes & Adrian E. Raftery, 2005. "Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models," Biometrics, The International Biometric Society, vol. 61(1), pages 36-45, March.
    7. C. Forlani & S. Bhatt & M. Cameletti & E. Krainski & M. Blangiardo, 2020. "A joint Bayesian space–time model to integrate spatially misaligned air pollution data in R‐INLA," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    8. R.M. Boaz & A.B. Lawson & J.L. Pearce, 2019. "Multivariate air pollution prediction modeling with partial missingness," Environmetrics, John Wiley & Sons, Ltd., vol. 30(7), November.
    9. Craig P. S & Goldstein M. & Rougier J. C & Seheult A. H, 2001. "Bayesian Forecasting for Complex Systems Using Computer Simulators," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 717-729, June.
    10. Christopher J. Paciorek, 2012. "Combining spatial information sources while accounting for systematic errors in proxies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(3), pages 429-451, May.
    11. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
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    1. Miguel A. Becerra & Catalina Tobón & Andrés Eduardo Castro-Ospina & Diego H. Peluffo-Ordóñez, 2021. "Information Quality Assessment for Data Fusion Systems," Data, MDPI, vol. 6(6), pages 1-30, June.

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