| dc.contributor.author | Arnaud Mounier | |
| dc.contributor.author | Laure Raynaud | |
| dc.contributor.author | Lucie Rottner | |
| dc.contributor.author | Matthieu Plu | |
| dc.contributor.author | Philippe Arbogast | |
| dc.contributor.author | Michaël Kreitz | |
| dc.contributor.author | Léo Mignan | |
| dc.contributor.author | Benoît Touzé | |
| dc.date.accessioned | 2023-04-12T18:52:16Z | |
| dc.date.available | 2023-04-12T18:52:16Z | |
| dc.date.copyright | 2022/02/01 | |
| dc.date.issued | 2022 | |
| dc.identifier.other | AIES-D-21-0010.1.pdf | |
| dc.identifier.uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4290387 | |
| dc.description.abstract | Bow 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.publisher | American Meteorological Society | |
| dc.title | Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network | |
| dc.type | Journal Paper | |
| dc.journal.volume | 1 | |
| dc.journal.issue | 2 | |
| dc.journal.title | Artificial Intelligence for the Earth Systems | |
| dc.identifier.doi | 10.1175/AIES-D-21-0010.1 | |
| dc.tree | Artificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 002 | |
| dc.contenttype | Fulltext | |