Probabilistic physically based cloud screening of satellite infrared imagery for operational sea surface temperature retrieval
Open accessAbstract
We propose and demonstrate a fully probabilistic (Bayesian) approach to the detection of cloudy pixels in thermal infrared (TIR) imagery observed from satellite over oceans. Using this approach, we show how to exploit the prior information and the fast forward modelling capability that are typically available in the operational context to obtain improved cloud detection. The probability of clear sky for each pixel is estimated by applying Bayes' theorem, and we describe how to apply Bayes' theorem to this problem in general terms. Joint probability density functions (PDFs) of the observations in the TIR channels are needed; the PDFs for clear conditions are calculable from forward modelling and those for cloudy conditions have been obtained empirically. Using analysis fields from numerical weather prediction as prior information, we apply the approach to imagery representative of imagers on polar‐orbiting platforms. In comparison with the established cloud‐screening scheme, the new technique decreases both the rate of failure to detect cloud contamination and the false‐alarm rate by one quarter. The rate of occurrence of cloud‐screening‐related errors of >1 K in area‐averaged SSTs is reduced by 83%. Copyright © 2005 Royal Meteorological Society.
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Bayesian methods of cloud screening were used in the dataset production.
“Bayesian methods of cloud screening of imagery11,12”
From Satellite-based time-series of sea-surface temperature since 1981 for climate applications (2019) · cites this paper as [11]
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Research works printed but not linked: 8
- Di Vittorio, A. V. and Emery, W. J. 2002 An automated, dynamic threshold cloud-masking algorithm for daytime AVHRR images over land. IEEE Trans. Geosci. Remote Sensing, 40, 1682-1694 ECMWF 2000- 2005 'ERA-40 project report series'. ECMWF, Reading, UK, available at http:// www.ecmwf.int/publications/library/do/references/list/192
- Marsham, J. 2003 'Lake temperatures-thermal remote sensing and assimilation into a lake model'. PhD thesis, University of Edinburgh, UK
- Murtagh, F., Barreto, D. and Marcello, J. 2003 Decision boundaries using Bayes factors: the case of cloud masks. IEEE Trans. Geosci. Remote Sensing, 41, 2952-2958
- Saunders, R.W. 1986 An automated scheme for the removal of cloud contamination from AVHRR radiances over Western Europe. Int. J. Remote Sensing, 7, 867-886
- Saunders, R. W. and Kriebel, K. T. 1988 An improved method for detecting clear-sky and cloudy radiances from AHRR data. Int. J. Remote Sensing, 9, 123-150
- Saunders, R., Matricardi, M. and Brunel, P. 1999 An improved fast radiative transfer model for assimilation of satellite radiance observations. Q. J. R. Meteorol. Soc., 125, 1407-1425
- Simpson, J. J., McIntire, T. J. and Sienko, M. 2000 An improved hybrid clustering algorithm for natural scenes. IEEE Trans. Geosci. Remote Sensing, 38, 1016-1032
- Simpson, J. J., Tsou, Y. L., Schmidt, A. and Harris, A. 2005 Analysis of Along-Track Scanning Radiometer-2 (ATSR-2) data for clouds, glint and sea surface temperature using neural networks. Remote Sensing Environ., in press
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Satellite-based time-series of sea-surface temperature since 1981 for climate applications
Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., Good, S. A., Mittaz, J., Rayner, N. A., Berry, D., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0236-x · Cited by 520 · see citing works
Self-citationInter-Comparisons of Daily Sea Surface Temperatures and In-Situ Temperatures in the Coastal Regions
Woo, H.-J., & Park, K.-A. (2020) · Remote Sensing · doi:10.3390/rs12101592 · Cited by 51 · see citing works
Evaluation of AATSR and TMI Satellite SST Data
Reynolds, R. W., Gentemann, C. L., & Corlett, G. K. (2010) · Journal of Climate · doi:10.1175/2009jcli3252.1 · Cited by 49 · see citing works
- Authors
- Merchant, C. J., Harris, A. R., Maturi, E., & Maccallum, S.
- Affiliations
- University of Edinburgh (GB)
- National Oceanic and Atmospheric Administration (US)
- ORCID
- Merchant, C. J.
- Venue
- Quarterly Journal of the Royal Meteorological Society
- Publisher
- Wiley
- Published
- 2005-10
- Volume
- 131
- Issue
- 611
- Pages
- 2735-2755
- ISSN
- 0035-9009 (ISSN-L)
- 1477-870X
- OpenAlex
- W2112608611
- Cited by
- 146
- References
- 0
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 12 · 2023: 3 · 2022: 12 · 2021: 6 · 2020: 11 · 2019: 12 · 2018: 8 · 2017: 5 · 2016: 8 · 2015: 7 · 2014: 15 · 2013: 5 · 2012: 8
- FWCI
- 2.34
- Type
- journal article
- Language
- en
- Open access
- Locations
- Quarterly Journal of the Royal Meteorological Society (publishedVersion)
- CentAUR (University of Reading) (acceptedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Keywords
- cloud screening, sea surface temperature retrieval, thermal infrared imagery, Bayesian detection, cloud detection, joint probability density function, polar-orbiting satellites, false alarm rate, cloud contamination
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