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

White, Charles H.; Heidinger, Andrew K.; Ackerman, Steven A.

doi:10.5194/amt-14-3371-2021

i

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

Supporting Files

2021
By White, Charles H. ; Heidinger, Andrew K. ; Ackerman, Steven A.

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Journal Title:

Atmospheric Measurement Techniques
Personal Author:

White, Charles H. ; Heidinger, Andrew K. ; Ackerman, Steven A.
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research)
Description:

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.
Keywords:

Clouds Infrared Imaging Meteorological Satellites Remote Sensing Infrared Imaging Meteorological Satellites Remote Sensing
Source:

Atmos. Meas. Tech., 14, 3371–3394
DOI:

https://doi.org/10.5194/amt-14-3371-2021
Document Type:

Journal Article
Funding:

Grant no. NA15NES4320001
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

urn:sha256:0d1f605f3a2acfdfcd69d722cdfaf097266449a9c8f2aeb178a33bbcf348e386
Download URL:

https://repository.library.noaa.gov/view/noaa/41825/noaa_41825_DS1.pdf
File Type:

[PDF - 15.46 MB ]

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