Articles | Volume 22, issue 24
https://doi.org/10.5194/acp-22-15793-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-15793-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Combining short-range dispersion simulations with fine-scale meteorological ensembles: probabilistic indicators and evaluation during a 85Kr field campaign
Youness El-Ouartassy
CORRESPONDING AUTHOR
CNRM, University of Toulouse, Météo-France, CNRS, 31057, Toulouse, France
Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSE-SANTE/SESUC/BMCA, 92260, Fontenay-aux-Roses, France
Irène Korsakissok
Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSE-SANTE/SESUC/BMCA, 92260, Fontenay-aux-Roses, France
Matthieu Plu
CNRM, University of Toulouse, Météo-France, CNRS, 31057, Toulouse, France
Olivier Connan
Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSE-ENV/SRTE/LRC, 50130, Cherbourg-En-Cotentin, France
Laurent Descamps
CNRM, University of Toulouse, Météo-France, CNRS, 31057, Toulouse, France
Laure Raynaud
CNRM, University of Toulouse, Météo-France, CNRS, 31057, Toulouse, France
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
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Past volcanic eruptions that spread out ash over large areas, like Eyjafjallajökull in 2010, forced the cancellation of thousands of flights and had huge economic consequences.
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Numerical weather prediction involves numerically solving the mathematical equations, which describe the geophysical flow, by transforming them so that they can be computed. Through this transformation, it appears that the equations actually solved by the machine are then a modified version of the original equations, introducing an error that contributes to the model error. This work helps to characterize the covariance of the model error that is due to this modification of the equations.
Christian Keil, Lucie Chabert, Olivier Nuissier, and Laure Raynaud
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During strong synoptic control, which dominates the weather on 80 % of the days in the 2-month HyMeX-SOP1 period, the domain-integrated precipitation predictability assessed with the normalized ensemble standard deviation is above average, the wet bias is smaller and the forecast quality is generally better. In contrast, the spatial forecast quality of the most intense precipitation in the afternoon, as quantified with its 95th percentile, is superior during weakly forced synoptic regimes.
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Short summary
This work investigates the potential value of using fine-scale meteorological ensembles to represent the inherent meteorological uncertainties in atmospheric dispersion model outputs. Probabilistic scores were used to evaluate the probabilistic performance of dispersion ensembles, using an original dataset of new continuous 85Kr air concentration measurements and a well-known source term. The results show that the ensemble dispersion simulations perform better than deterministic ones.
This work investigates the potential value of using fine-scale meteorological ensembles to...
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