Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 9
Articles | Volume 15, issue 9
https://doi.org/10.5194/amt-15-3031-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-3031-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 9
Research article
 | 
17 May 2022
Research article |  | 17 May 2022

Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data

Daniel Robbins, Caroline Poulsen, Steven Siems, and Simon Proud

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Latest update: 13 Dec 2024
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Short summary
A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.
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