Next Article in Journal
Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm
Next Article in Special Issue
Role of El Niño Southern Oscillation (ENSO) Events on Temperature and Salinity Variability in the Agulhas Leakage Region
Previous Article in Journal
Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves
Previous Article in Special Issue
The Role of Advanced Microwave Scanning Radiometer 2 Channels within an Optimal Estimation Scheme for Sea Surface Temperature
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessFeature PaperArticle
Remote Sens. 2018, 10(1), 97; doi:10.3390/rs10010097

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

1
Department of Meteorology, University of Reading, Reading RG6 6AL, UK
2
National Centre for Earth Observation, Leicester LE1 7RH, UK
3
National Physical Laboratory, Teddington TW11 0LW, UK
4
Norwegian Meteorological Institute, Department of Research and Development, N-0313 Oslo, Norway
*
Author to whom correspondence should be addressed.
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)
View Full-Text   |   Download PDF [1996 KB, uploaded 12 January 2018]   |  

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

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).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top