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