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Cited by 29Atmospheric aerosols and cloudsCalibration and Measurement TechniquesMeteorological Phenomena and Simulations

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

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Bulgin, Claire, Mittaz, Jonathan, Embury, Owen, Eastwood, Steinar, Merchant, Christopher·2018·Remote Sensing
DOI 10.3390/rs10010097
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Abstract

Cloud detection is a source of significant errors in retrieval of sea surface temperature (SST). We apply a Bayesian cloud detection scheme to 37 years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data, which is an important source of multi-decadal global SST information. The Bayesian scheme calculates a probability of clear-sky for each image pixel, conditional on the satellite observations and prior probability. We compare the cloud detection performance to the operational Clouds from AVHRR Extended algorithm (CLAVR-x), as a measure of improvement from reduced cloud-related errors. To do this we use sea surface temperature differences between satellite retrievals and in situ observations from drifting buoys and the Global Tropical Moored Buoy Array (GTMBA). The Bayesian scheme reduces the absolute difference between the mean and median SST biases and reduces the standard deviation of the SST differences by ~10% for both daytime and nighttime retrievals. These reductions are indicative of removing cloud contaminated outliers in the distribution, as these fall only on one side of the distribution forming a cold tail. At a probability threshold of 0.9 typically used to determine a binary cloud mask for SST retrieval, the Bayesian mask also reduces the robust standard deviation by ~5–10% during the day, in comparison with the operational cloud mask. This shows an improvement in the central distribution of SST differences for daytime retrievals.

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Cited by

Sorted by citations

Authors
Bulgin, C., Mittaz, J., Embury, O., Eastwood, S., & Merchant, C.
Affiliations
  • National Centre for Earth Observation (GB)
  • National Physical Laboratory (GB)
  • Norwegian Meteorological Institute (NO)
Corresponding author
Bulgin, Claire
ORCID
Venue
Remote Sensing
Publisher
MDPI AG
Published
2018-01-12
Volume
10
Issue
1
Pages
97
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs10010097
OpenAlex
W2783345137
Cited by
29
References
0
Citations by year
2026: 3 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 5 · 2021: 3 · 2020: 1 · 2019: 4 · 2018: 2
FWCI
1.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • CentAUR (University of Reading) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • European Space Agency
  • Natural Environment Research Council (nceo020006, NE/R016518/1)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
  • Physical Sciences › Engineering › Aerospace Engineering › Calibration and Measurement Techniques
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
Keywords
cloud detection, sea surface temperature retrieval, drifting buoys, satellite SST

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