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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-74
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-74
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 14 Apr 2020

Submitted as: research article | 14 Apr 2020

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This preprint is currently under review for the journal AMT.

Leveraging spatial textures, through machine learning, to identify aerosol and distinct cloud types from multispectral observations

Willem J. Marais1, Robert E. Holz1, Jeffrey S. Reid2, and Rebecca M. Willett3 Willem J. Marais et al.
  • 1Space Science Engineering Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
  • 2Marine Meteorology Division, Naval Research Laboratory, Monterey, California, USA
  • 3Department of Statistics & Computer Science, University of Chicago, Illinois, USA

Abstract. Current cloud and aerosol identification methods for multi-spectral radiometers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), employ multi-channel spectral tests on individual pixels (i.e. field of views). The use of the spatial information in cloud and aerosol algorithms has been primarily statistical parameters such as non-uniformity tests of surrounding pixels with cloud classification provided by the multi-spectral microphysical retrievals such as phase and cloud top height. With these methodologies there is uncertainty in identifying optically thick aerosols, since aerosols and clouds have similar spectral properties in coarse spectral-resolution measurements. Furthermore, identifying clouds regimes (e.g. stratiform, cumuliform) from just spectral measurements is difficult, since low-altitude cloud regimes have similar spectral properties. Recent advances in computer vision using deep neural networks provide a new opportunity to better leverage the coherent spatial information in multi-spectral imagery. Using a combination of machine learning techniques combined with a new methodology to create the necessary training data we demonstrate improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types. The training labeled dataset was created from an adapted NASA Worldview platform that provides an efficient user interface to assemble a human labeled database of cloud and aerosol types. The Convolutional Neural Network (CNN) labeling accuracy of aerosols and cloud types was quantified using independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and MODIS cloud and aerosol products. By harnessing CNNs with a unique labeled dataset, we demonstrate the improvement of the identification of aerosol and distinct cloud types from MODIS and VIIRS images compared to a per-pixel spectral and standard deviation thresholding method. The paper concludes with case studies that compare the CNN methodology results with the MODIS cloud and aerosol products.

Willem J. Marais et al.

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Willem J. Marais et al.

Willem J. Marais et al.

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Latest update: 10 Jul 2020
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Short summary
Space agencies use moderate resolution satellite imagery to study how smoke, dust, pollution (aerosols) and cloud types impacts the Earth's climate; these space agencies include NASA, ESA, China Meteorological Administration, etc. We demonstrate in this paper that an algorithm with convolutional neural networks can greatly enhance the automated detection of aerosols and cloud types from satellite imagery. Our algorithm is an improvement compared current aerosol and cloud detection algorithms.
Space agencies use moderate resolution satellite imagery to study how smoke, dust, pollution...
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