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A Novel Framework to Harmonise Satellite Data Series for Climate Applications
Article

Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

1
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, 8058 Zurich-Airport, Switzerland
2
Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland
3
EUMETSAT, Eumetsat-Allee 1, 64295 Darmstadt, Germany
4
Deutscher Wetterdienst, Frankfurterstr. 135, 63067 Offenbach, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2019, 11(9), 1052; https://doi.org/10.3390/rs11091052
Received: 29 March 2019 / Revised: 26 April 2019 / Accepted: 29 April 2019 / Published: 3 May 2019
(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)
Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data. View Full-Text
Keywords: geostationary satellite; cloud fractional cover; climate data record; decadal stability; diurnal cycle; Bayesian classifier; historical satellites geostationary satellite; cloud fractional cover; climate data record; decadal stability; diurnal cycle; Bayesian classifier; historical satellites
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MDPI and ACS Style

Stöckli, R.; Bojanowski, J.S.; John, V.O.; Duguay-Tetzlaff, A.; Bourgeois, Q.; Schulz, J.; Hollmann, R. Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications. Remote Sens. 2019, 11, 1052. https://doi.org/10.3390/rs11091052

AMA Style

Stöckli R, Bojanowski JS, John VO, Duguay-Tetzlaff A, Bourgeois Q, Schulz J, Hollmann R. Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications. Remote Sensing. 2019; 11(9):1052. https://doi.org/10.3390/rs11091052

Chicago/Turabian Style

Stöckli, Reto, Jędrzej S. Bojanowski, Viju O. John, Anke Duguay-Tetzlaff, Quentin Bourgeois, Jörg Schulz, and Rainer Hollmann. 2019. "Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications" Remote Sensing 11, no. 9: 1052. https://doi.org/10.3390/rs11091052

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