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The Role of Advanced Microwave Scanning Radiometer 2 Channels within an Optimal Estimation Scheme for Sea Surface Temperature
Open AccessFeature PaperArticle

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

Department of Meteorology, University of Reading, Reading RG6 6AL, UK
National Centre for Earth Observation, Leicester LE1 7RH, UK
National Physical Laboratory, Teddington TW11 0LW, UK
Norwegian Meteorological Institute, Department of Research and Development, N-0313 Oslo, Norway
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 97;
Received: 24 November 2017 / Revised: 5 January 2018 / Accepted: 9 January 2018 / Published: 12 January 2018
(This article belongs to the Collection Sea Surface Temperature Retrievals from Remote Sensing)
PDF [1996 KB, uploaded 12 January 2018]


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. View Full-Text
Keywords: sea surface temperature; cloud detection; AVHRR; climate data record sea surface temperature; cloud detection; AVHRR; climate data record

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bulgin, C.E.; Mittaz, J.P.D.; Embury, O.; Eastwood, S.; Merchant, C.J. Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data. Remote Sens. 2018, 10, 97.

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