Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks

Authors

Kévin Dedieu
Clotilde Augros (ORCID: https://orcid.org/0000-0003-0738-5516)
Olivier Caumont (ORCID: https://orcid.org/0000-0002-6470-2023)
Vincent Forcadell
Jordi Figueras i Ventura (ORCID: https://orcid.org/0000-0002-4949-422X)
Olivier Laurantin
Cloé David
Maxandre Ouradou
Hassan Al-Sakka

Institutions

Centre National de la Recherche Scientifique (FR)
Université Fédérale de Toulouse Midi-Pyrénées (FR)
Leonardo Film (Germany) (DE)
Météo-France (FR)
DGA Techniques aérospatiales (FR)

Publication Details

Journal: Atmospheric measurement techniques
Published: 2024-11-26
DOI: https://doi.org/10.5194/amt-17-6707-2024
Citations: 5
Primary Topic: Meteorological Phenomena and Simulations
Type: article
Field-Weighted Citation Impact: 1.88

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article

Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks

Kévin Dedieu, Clotilde Augros, Olivier Caumont, Vincent Forcadell et al.

Nov 26, 2024

5 citations

Atmospheric measurement techniques

Meteorological Phenomena and Simulations

1.88

article

Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks

Kévin Dedieu, Clotilde Augros, Olivier Caumont, Vincent Forcadell, Jordi Figueras i Ventura, Olivier Laurantin, Cloé David, Maxandre Ouradou, Hassan Al-Sakka

November 26, 2024article en

5 citations

Abstract

No abstract available for this paper.

Atmospheric measurement techniquesVol. 17(22)

Centre National de la Recherche Scientifique (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Leonardo Film (Germany) (DE), Météo-France (FR), DGA Techniques aérospatiales (FR)

Peace, Justice and strong institutions

Openalex Percentile: Top 15%

Meteorological Phenomena and Simulations

1.88

DOI Open Access PDF OpenAlex

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