Articles | Volume 14, issue 5
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, Andrew K. Heidinger, and Steven A. Ackerman

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Cited articles

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Ackerman, S. A., Holz, R. E., Frey, R., Eloranta, E. W., Maddux, B. C., and McGill, M.: Cloud Detection with MODIS, Part II: Validation, J. Atmos. Ocean. Technol., 25, 1073–1086,, 2008. a
Ackerman, S., Richard, F., Kathleen, S., Yinghui, L., Liam, G., Bryan, B., and Paul, M.: Discriminating Clear-Sky from Cloud with MODIS, Algorithm Theoretical Basis Document (MOD35) – Version 6.1, Tech. Rep., NASA, (last access: 21 July 2020), 2010. a
Braun, B. M., Sweetser, T. H., Graham, C., and Bartsch, J.: CloudSat's A-Train Exit and the Formation of the C-Train: An Orbital Dynamics Perspective, in: 2019 IEEE Aerospace Conference, 1–10,, 2019. a
Bulgin, C. E., Mittaz, J. P., Embury, O., Eastwood, S., and Merchant, C. J.: Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data, Remote Sens., 10, 97,, 2018. a
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.