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Automatically Finding Ship Tracks to Enable Large‐Scale Analysis of Aerosol‐Cloud Interactions
Author(s) -
Yuan Tianle,
Wang Chenxi,
Song Hua,
Platnick Steven,
Meyer Kerry,
Oreopoulos Lazaros
Publication year - 2019
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2019gl083441
Subject(s) - cloud computing , aerosol , satellite , scale (ratio) , meteorology , remote sensing , artificial neural network , computer science , track (disk drive) , environmental science , geology , artificial intelligence , aerospace engineering , geography , engineering , cartography , operating system
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.