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Predicting snow velocity in large chute flows under different environmental conditions

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  • Jonathan Rougier
  • Martin Kern

Abstract

Observations, model evaluations and expert judgements are combined to make predictions of snow velocity in large chute experiments. Different experimental variables, namely the environmental conditions snow density and snow surface temperature, affect all aspects of this inference. We show how the effect of these two variables can be incorporated in our judgements regarding the uncertain parameters of the physical model, the discrepancy between the physical model and reality and the observation error. We adopt a Bayes linear approach to avoid the necessity of fully probabilistic belief specifications and demonstrate visual tools for statistical validation. Our results represent an important first step in improving the specification of uncertainty in model-based avalanche hazard mapping. Copyright (c) 2010 Royal Statistical Society.

Suggested Citation

  • Jonathan Rougier & Martin Kern, 2010. "Predicting snow velocity in large chute flows under different environmental conditions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 737-760.
  • Handle: RePEc:bla:jorssc:v:59:y:2010:i:5:p:737-760
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    1. 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.
    2. Goldstein, Michael & Rougier, Jonathan, 2006. "Bayes Linear Calibrated Prediction for Complex Systems," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1132-1143, September.
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