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| Titel |
On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset |
| VerfasserIn |
K.-G. Karlsson, E. Johansson |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 6, no. 5 ; Nr. 6, no. 5 (2013-05-15), S.1271-1286 |
| Datensatznummer |
250017893
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| Publikation (Nr.) |
 copernicus.org/amt-6-1271-2013.pdf |
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| Zusammenfassung |
A method for detailed evaluation of a new satellite-derived global 28 yr
cloud and radiation climatology (Climate Monitoring SAF Clouds, Albedo and
Radiation from AVHRR data, named CLARA-A1) from polar-orbiting NOAA and
Metop satellites is presented. The method combines 1 km and 5 km resolution
cloud datasets from the CALIPSO-CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder
Satellite Observation – Cloud-Aerosol Lidar with Orthogonal Polarization) cloud lidar for estimating cloud
detection limitations and the accuracy of cloud top height estimations.
Cloud detection is shown to work efficiently for clouds with optical
thicknesses above 0.30 except for at twilight conditions when this value
increases to 0.45. Some misclassifications of cloud-free surfaces during daytime
were revealed for semi-arid land areas in the sub-tropical and tropical regions
leading to up to 20% overestimated cloud amounts. In
addition, a substantial fraction (at least 20–30%) of all clouds remains
undetected in the polar regions during the polar winter season due to the
lack of or an inverted temperature contrast between Earth surfaces and
clouds.
Subsequent cloud top height evaluation took into account the derived
information about the cloud detection limits. It was shown that this has
fundamental importance for the achieved results. An overall bias of −274 m
was achieved compared to a bias of −2762 m when no measures were taken to
compensate for cloud detection limitations. Despite this improvement it was
concluded that high-level clouds still suffer from substantial height
underestimations, while the opposite is true for low-level (boundary layer)
clouds.
The validation method and the specifically collected satellite dataset with
optimal matching in time and space are suggested for a wider use in the
future for evaluation of other cloud retrieval methods based on passive
satellite imagery. |
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