Articles | Volume 24, issue 8
https://doi.org/10.5194/nhess-24-2793-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-2793-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Probabilistic short-range forecasts of high-precipitation events: optimal decision thresholds and predictability limits
François Bouttier
CORRESPONDING AUTHOR
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Hugo Marchal
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
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Heavy precipitation (HP) constitutes a major meteorological threat in the western Mediterranean. Every year, recurrent events affect the area with fatal consequences. Despite this being a well-known issue, open questions still remain. The understanding of the underlying mechanisms and the modeling representation of the events must be improved. In this article we present the most recent lessons learned from the Hydrological Cycle in the Mediterranean Experiment (HyMeX).
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Short summary
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This study focuses on the heavy precipitation event of 14 and 15 October 2018, which caused deadly flash floods in the Aude basin in south-western France.
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Short summary
Weather prediction uncertainties can be described as sets of possible scenarios – a technique called ensemble prediction. Our machine learning technique translates them into more easily interpretable scenarios for various users, balancing the detection of high precipitation with false alarms. Key parameters are precipitation intensity and space and time scales of interest. We show that the approach can be used to facilitate warnings of extreme precipitation.
Weather prediction uncertainties can be described as sets of possible scenarios – a technique...
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