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Article

Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations

1
Remote Sensing Centre, Institute of Geodesy and Cartography, Kaczmarskiego 27, 02-679 Warsaw, Poland
2
Federal Office of Meteorology and Climatology MeteoSwiss, Climate Services, Operation Center 1, P.O. Box 257, CH-8058 Zürich-Airport, Switzerland
3
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 804; https://doi.org/10.3390/rs10050804
Received: 1 May 2018 / Revised: 18 May 2018 / Accepted: 20 May 2018 / Published: 22 May 2018
(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)
The CM SAF Cloud Fractional Cover dataset from Meteosat First and Second Generation (COMET, https://doi.org/10.5676/EUM_SAF_CM/CFC_METEOSAT/V001) covering 1991–2015 has been recently released by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF). COMET is derived from the MVIRI and SEVIRI imagers aboard geostationary Meteosat satellites and features a Cloud Fractional Cover (CFC) climatology in high temporal (1 h) and spatial (0.05° × 0.05°) resolution. The CM SAF long-term cloud fraction climatology is a unique long-term dataset that resolves the diurnal cycle of cloudiness. The cloud detection algorithm optimally exploits the limited information from only two channels (broad band visible and thermal infrared) acquired by older geostationary sensors. The underlying algorithm employs a cyclic generation of clear sky background fields, uses continuous cloud scores and runs a naïve Bayesian cloud fraction estimation using concurrent information on cloud state and variability. The algorithm depends on well-characterized infrared radiances (IR) and visible reflectances (VIS) from the Meteosat Fundamental Climate Data Record (FCDR) provided by EUMETSAT. The evaluation of both Level-2 (instantaneous) and Level-3 (daily and monthly means) cloud fractional cover (CFC) has been performed using two reference datasets: ground-based cloud observations (SYNOP) and retrievals from an active satellite instrument (CALIPSO/CALIOP). Intercomparisons have employed concurrent state-of-the-art satellite-based datasets derived from geostationary and polar orbiting passive visible and infrared imaging sensors (MODIS, CLARA-A2, CLAAS-2, PATMOS-x and CC4CL-AVHRR). Averaged over all reference SYNOP sites on the monthly time scale, COMET CFC reveals (for 0–100% CFC) a mean bias of −0.14%, a root mean square error of 7.04% and a trend in bias of −0.94% per decade. The COMET shortcomings include larger negative bias during the Northern Hemispheric winter, lower precision for high sun zenith angles and high viewing angles, as well as an inhomogeneity around 1995/1996. Yet, we conclude that the COMET CFC corresponds well to the corresponding SYNOP measurements, and it is thus useful to extend in both space and time century-long ground-based climate observations. View Full-Text
Keywords: cloud fractional cover; climate data record; Meteosat; MVIRI; SEVIRI; CM SAF cloud fractional cover; climate data record; Meteosat; MVIRI; SEVIRI; CM SAF
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MDPI and ACS Style

Bojanowski, J.S.; Stöckli, R.; Duguay-Tetzlaff, A.; Finkensieper, S.; Hollmann, R. Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations. Remote Sens. 2018, 10, 804. https://doi.org/10.3390/rs10050804

AMA Style

Bojanowski JS, Stöckli R, Duguay-Tetzlaff A, Finkensieper S, Hollmann R. Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations. Remote Sensing. 2018; 10(5):804. https://doi.org/10.3390/rs10050804

Chicago/Turabian Style

Bojanowski, Jędrzej S., Reto Stöckli, Anke Duguay-Tetzlaff, Stephan Finkensieper, and Rainer Hollmann. 2018. "Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations" Remote Sensing 10, no. 5: 804. https://doi.org/10.3390/rs10050804

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