Articles | Volume 14, issue 5
Atmos. Meas. Tech., 14, 3371–3394, 2021
https://doi.org/10.5194/amt-14-3371-2021
Atmos. Meas. Tech., 14, 3371–3394, 2021
https://doi.org/10.5194/amt-14-3371-2021

Research article 07 May 2021

Research article | 07 May 2021

Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks

Charles H. White et al.

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Status: closed
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Charles White on behalf of the Authors (24 Jan 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (11 Mar 2021) by Joanna Joiner
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Short summary
Automated detection of clouds in satellite imagery is an important practice that is useful for predicting and understanding both weather and climate. Cloud detection is often difficult at night and over cold surfaces. In this paper, we discuss how a complex statistical model (a neural network) can more accurately detect clouds compared to currently used approaches. Overall, our results suggest that our approach could result in more reliable assessments of global cloud cover.