On the model uncertainties in Bayesian source reconstruction using an ensemble of weather predictions, the emission inverse modelling system FREAR v1.0, and the Lagrangian transport and dispersion model Flexpart v9.0.2

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On the model uncertainties in Bayesian source reconstruction using an ensemble of weather predictions, the emission inverse modelling system FREAR v1.0, and the Lagrangian transport and dispersion model Flexpart v9.0.2. / De Meutter, Pieter; Hoffman, Ian; Ungar, Kurt.

In: Geoscientific Model Development, Vol. 14, No. 3, 08.03.2021, p. 1237-1252.

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@article{7ca84888f49944eab0c03165df01cbf4,
title = "On the model uncertainties in Bayesian source reconstruction using an ensemble of weather predictions, the emission inverse modelling system FREAR v1.0, and the Lagrangian transport and dispersion model Flexpart v9.0.2",
abstract = "Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing an uncertainty quantification on the inferred source parameters. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.",
keywords = "Bayesian source reconstruction, Atmospheric releases, Emergency response",
author = "{De Meutter}, Pieter and Ian Hoffman and Kurt Ungar",
note = "Score=10",
year = "2021",
month = mar,
day = "8",
doi = "10.5194/gmd-14-1237-2021",
language = "English",
volume = "14",
pages = "1237--1252",
journal = "Geoscientific Model Development",
issn = "1991-959X",
publisher = "Copernicus Publications",
number = "3",

}

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TY - JOUR

T1 - On the model uncertainties in Bayesian source reconstruction using an ensemble of weather predictions, the emission inverse modelling system FREAR v1.0, and the Lagrangian transport and dispersion model Flexpart v9.0.2

AU - De Meutter, Pieter

AU - Hoffman, Ian

AU - Ungar, Kurt

N1 - Score=10

PY - 2021/3/8

Y1 - 2021/3/8

N2 - Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing an uncertainty quantification on the inferred source parameters. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.

AB - Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing an uncertainty quantification on the inferred source parameters. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.

KW - Bayesian source reconstruction

KW - Atmospheric releases

KW - Emergency response

UR - https://ecm.sckcen.be/OTCS/llisapi.dll/open/45963028

U2 - 10.5194/gmd-14-1237-2021

DO - 10.5194/gmd-14-1237-2021

M3 - Article

VL - 14

SP - 1237

EP - 1252

JO - Geoscientific Model Development

JF - Geoscientific Model Development

SN - 1991-959X

IS - 3

ER -

ID: 7190873