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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2020-61
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-61
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Mar 2020

31 Mar 2020

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A revised version of this preprint is currently under review for the journal AMT.

Applying Deep Learning to NASA MODIS Data to Create a Community Record of Marine Low Cloud Mesoscale Morphology

Tianle Yuan1,2, Hua Song3, Robert Wood4, Johannes Mohrmann4, Kerry Meyer1, Lazaros Oreopoulos1, and Steven Platnick1 Tianle Yuan et al.
  • 1Earth Science Directorate, NASA Goddard Space Flight Center
  • 2Joint Center for Earth Systems Technology, University of Maryland, Baltimore County
  • 3Science Systems and Applications, Inc.
  • 4Department of Atmospheric Sciences, University of Washington

Abstract. Marine low clouds display rich mesoscale morphological types, distinct spatial patterns of cloud fields. Being able to differentiate low cloud morphology offers a tool for the research community to go one step beyond bulk cloud statistics such as cloud fraction and advance the understanding of low clouds. Here we report the progress of a NASA funded project that aims to create an observational record of low cloud mesoscale morphology at a near-global (60S–60N) scale. First, a training set is created by our team members manually labeling thousands of mesoscale (128 x 128) MODIS scenes into six different categories: stratus, closed cellular convection, disorganized convection, open cellular convection, clustered cumulus convection, and suppressed cumulus convection. Then we train a deep convolutional neural network model using this training set to classify individual MODIS scenes at 128 x 128 resolution, and test it on a test set. The trained model achieves a cross-type average precision of about 93 %. We apply the trained model to 16 years of data over the Southeast Pacific. The resulting climatological distribution of low cloud morphology types show both expected and unexpected features and suggest promising potential for low cloud studies as a data product.

Tianle Yuan et al.

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Tianle Yuan et al.

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Latest update: 20 Sep 2020
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Short summary
We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process large amount of satellite data. The algorithm will help low cloud researchers to better understand their mesoscale organiizations.
We use deep transfer learning techniques to classify satellite cloud images into different...
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