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  • Unsupervised Clustering and Supervised Machine Learning for Lightning Classification: Application to Identifying EIPs for Ground‐Based TGF Detection
Title: Unsupervised Clustering and Supervised Machine Learning for Lightning Classification: Application to Identifying EIPs for Ground‐Based TGF Detection
Key Points A framework merging unsupervised clustering and supervised convolutional neural network (CNN) for lightning classification is developed Clustering of positive polarity energetic lightning radio pulses (>150 kA) identifies three processes: +EIPs (6%–7%), +NBEs, and +CGs CNNs detect 95.2% of manually identified +EIPs with up to 98.7% accuracy, enabling studying EIP‐TGF link with lower peak current (>50 kA)  more » « less
Award ID(s):
2026304
PAR ID:
10448563
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Journal of Geophysical Research: Atmospheres
Volume:
128
Issue:
9
ISSN:
2169-897X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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