Automatically Finding Ship-Tracks to Enable Large-Scale Analysis of Aerosol-Cloud Interactions

Ship tracks appear as long winding linear features in satellite images and are produced by aerosols from ship exhausts changing low cloud properties. They are one of the best examples of aerosol‐cloud interaction experiments. However, manually finding ship tracks from satellite data on a large scale...

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Bibliographic Details
Published in:Geophysical research letters 2019-07, Vol.46 (13), p.7726-7733
Main Authors: Yuan, Tianle, Wang, Chenxi, Song, Hua, Platnick, Steven, Meyer, Kerry, Oreopoulos, Lazaros
Format: Article
Language:English
Subjects:
Aerosol effects
aerosol indirect effects
Aerosol-cloud interactions
Aerosols
Artificial neural networks
Cloud interaction
Cloud properties
Clouds
Cybernetics, Artificial Intelligence And Robotics
deep learning
Earth Resources And Remote Sensing
Exhausts
Low clouds
Neural networks
Railway tracks
Satellite data
Satellite imagery
ship tracks
Shipping
Spaceborne remote sensing
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Summary:Ship tracks appear as long winding linear features in satellite images and are produced by aerosols from ship exhausts changing low cloud properties. They are one of the best examples of aerosol‐cloud interaction experiments. However, manually finding ship tracks from satellite data on a large scale is prohibitively costly while a large number of samples are required to improve our understanding. Here we train a deep neural network to automate finding ship tracks. The neural network model generalizes well as it not only finds ship tracks labeled by human experts but also detects those that are occasionally missed by humans. It finds more ship tracks than all previous studies combined and produces a map of ship track distributions off the California coast that matches well with known shipping traffic. Our technique will enable studying aerosol effects on low clouds using ship tracks on a large scale, which will potentially narrow the uncertainty of the aerosol‐cloud interactions.
ISSN:0094-8276
1944-8007
DOI:10.1029/2019GL083441