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dc.contributor.authorArnaud Mounier
dc.contributor.authorLaure Raynaud
dc.contributor.authorLucie Rottner
dc.contributor.authorMatthieu Plu
dc.contributor.authorPhilippe Arbogast
dc.contributor.authorMichaël Kreitz
dc.contributor.authorLéo Mignan
dc.contributor.authorBenoît Touzé
dc.date.accessioned2023-04-12T18:52:16Z
dc.date.available2023-04-12T18:52:16Z
dc.date.copyright2022/02/01
dc.date.issued2022
dc.identifier.otherAIES-D-21-0010.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290387
dc.description.abstractBow echoes (BEs) are bow-shaped lines of convective cells that are often associated with swaths of damaging straight-line winds and small tornadoes. This paper describes a convolutional neural network (CNN) able to detect BEs directly from French kilometer-scale model outputs in order to facilitate and accelerate the operational forecasting of BEs. The detections are only based on the maximum pseudoreflectivity field predictor (“pseudo” because it is expressed in mm h
dc.publisherAmerican Meteorological Society
dc.titleDetection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network
dc.typeJournal Paper
dc.journal.volume1
dc.journal.issue2
dc.journal.titleArtificial Intelligence for the Earth Systems
dc.identifier.doi10.1175/AIES-D-21-0010.1
dc.treeArtificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 002
dc.contenttypeFulltext


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