Contrail Recognition with Convolutional Neural Network and Contrail Parameterizations Evaluation
Author:
Affiliation:
1. Nanjing University of Information Science and Technology
Publisher
Meteorological Society of Japan
Subject
Atmospheric Science
Link
https://www.jstage.jst.go.jp/article/sola/14/0/14_2018-023/_pdf
Reference25 articles.
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2. Bakan, S., M. Betancor, V. Gayler, and H. Grassl, 1994: Contrail frequency over Europe from NOAA-satellite images. Ann. Geophys., 12, 962-968.
3. Burkhardt, U., and B. Karcher, 2011: Global radiative forcing from contrail cirrus. Nat. Climate Change, 1, 54-58.
4. Burkhardt, U., B. Kärcher, and M. Ponater, 2008: Contrail cirrus supporting areas in model and observations. Geophys. Res. Lett., 35, 16808-16812.
5. Courbariaux, M., I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, 2016: Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or −1. arXiv.org, 1602.02830.
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