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Uncertainty quantification of long-range atmospheric transport models : case study

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Abstract
Accurate atmospheric transport model forecasts can help detect violations of the CTBT, and are important for decision support in case of nuclear incidents. An as accurate as possible forecast is desired, but unfortunately the forecast is prone to errors that are difficult to quantify. A collaboration between SCK•CEN, the Royal Meteorological Institute of Belgium and Ghent University tries to quantify the uncertainty of long-range radioactive xenon background forecasts in the context of the CTBT. The FLEXPART model is used, with input data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Results from the FLEXPART dispersion model will be presented and validated with xenon measurements from the International Monitoring System for a test case. Sensitivity tests will be performed to assess the model sensitivity to certain parameters. Finally, an overview of future work will be given, which consists of using the ensemble prediction system (EPS) of ECMWF to assess the uncertainty related to meteorological input.

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Please use this url to cite or link to this publication:

MLA
De Meutter, Pieter, et al. “Uncertainty Quantification of Long-Range Atmospheric Transport Models : Case Study.” CTBT: Science and Technology, Conference Abstracts, 2015.
APA
De Meutter, P., Camps, J., Delcloo, A., Deconninck, B., & Termonia, P. (2015). Uncertainty quantification of long-range atmospheric transport models : case study. CTBT: Science and Technology, Conference Abstracts. Presented at the CTBT: Science and Technology 2015 conference (SnT-2015), Vienna, Austria.
Chicago author-date
De Meutter, Pieter, Johan Camps, Andy Delcloo, Benoit Deconninck, and Piet Termonia. 2015. “Uncertainty Quantification of Long-Range Atmospheric Transport Models : Case Study.” In CTBT: Science and Technology, Conference Abstracts.
Chicago author-date (all authors)
De Meutter, Pieter, Johan Camps, Andy Delcloo, Benoit Deconninck, and Piet Termonia. 2015. “Uncertainty Quantification of Long-Range Atmospheric Transport Models : Case Study.” In CTBT: Science and Technology, Conference Abstracts.
Vancouver
1.
De Meutter P, Camps J, Delcloo A, Deconninck B, Termonia P. Uncertainty quantification of long-range atmospheric transport models : case study. In: CTBT: Science and Technology, Conference abstracts. 2015.
IEEE
[1]
P. De Meutter, J. Camps, A. Delcloo, B. Deconninck, and P. Termonia, “Uncertainty quantification of long-range atmospheric transport models : case study,” in CTBT: Science and Technology, Conference abstracts, Vienna, Austria, 2015.
@inproceedings{7077393,
  abstract     = {{Accurate atmospheric transport model forecasts can help detect violations of the CTBT, and are important for decision support in case of nuclear incidents. An as accurate as possible forecast is desired, but unfortunately the forecast is prone to errors that are difficult to quantify. A collaboration between SCK•CEN, the Royal Meteorological Institute of Belgium and Ghent University tries to quantify the uncertainty of long-range radioactive xenon background forecasts in the context of the CTBT. The FLEXPART model is used, with input data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Results from the FLEXPART dispersion model will be presented and validated with xenon measurements from the International Monitoring System for a test case. Sensitivity tests will be performed to assess the model sensitivity to certain parameters. Finally, an overview of future work will be given, which consists of using the ensemble prediction system (EPS) of ECMWF to assess the uncertainty related to meteorological input.}},
  author       = {{De Meutter, Pieter and Camps, Johan and Delcloo, Andy and Deconninck, Benoit and Termonia, Piet}},
  booktitle    = {{CTBT: Science and Technology, Conference abstracts}},
  language     = {{eng}},
  location     = {{Vienna, Austria}},
  title        = {{Uncertainty quantification of long-range atmospheric transport models : case study}},
  year         = {{2015}},
}