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Using LiDAR Output to Identify Atmospheric Rotors: A Convolutional Neural Network Approach

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Published in:Meteorological Applications
Format: Online Article RSS Article
Published: 2026
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FRELIP Feed Integration
container_title Meteorological Applications
description
discipline_display Environmental Sciences
discipline_facet Environmental Sciences
format Online Article
RSS Article
genre Journal Article
id rss_article:24872
institution FRELIP
journal_source_facet Meteorological Applications
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Using LiDAR Output to Identify Atmospheric Rotors: A Convolutional Neural Network Approach
— — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
sub_discipline_display Climate & Atmospheric Sciences
sub_discipline_facet Climate & Atmospheric Sciences
subject_display — — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
— — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
subject_facet — — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
title Using LiDAR Output to Identify Atmospheric Rotors: A Convolutional Neural Network Approach
title_auth Using LiDAR Output to Identify Atmospheric Rotors: A Convolutional Neural Network Approach
title_full Using LiDAR Output to Identify Atmospheric Rotors: A Convolutional Neural Network Approach
title_fullStr Using LiDAR Output to Identify Atmospheric Rotors: A Convolutional Neural Network Approach
title_full_unstemmed Using LiDAR Output to Identify Atmospheric Rotors: A Convolutional Neural Network Approach
title_short Using LiDAR Output to Identify Atmospheric Rotors: A Convolutional Neural Network Approach
title_sort using lidar output to identify atmospheric rotors: a convolutional neural network approach
topic — — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
url https://rmets.onlinelibrary.wiley.com/doi/10.1002/met.70187?af=R