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| Titel |
CLAAS: the CM SAF cloud property data set using SEVIRI |
| VerfasserIn |
M. Stengel, A. Kniffka, J. F. Meirink, M. Lockhoff, J. Tan, R. Hollmann |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 14, no. 8 ; Nr. 14, no. 8 (2014-04-30), S.4297-4311 |
| Datensatznummer |
250118647
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| Publikation (Nr.) |
 copernicus.org/acp-14-4297-2014.pdf |
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| Zusammenfassung |
| An 8-year record of satellite-based cloud properties named CLAAS (CLoud
property dAtAset using SEVIRI) is presented, which was derived within the
EUMETSAT Satellite Application Facility on Climate Monitoring. The data set is
based on SEVIRI measurements of the Meteosat Second Generation satellites, of
which the visible and near-infrared channels were intercalibrated with MODIS.
Applying two state-of-the-art retrieval schemes ensures high accuracy in
cloud detection, cloud vertical placement and microphysical cloud properties.
These properties were further processed to provide daily to monthly averaged
quantities, mean diurnal cycles and monthly histograms. In particular, the
per-month histogram information enhances the insight in spatio-temporal
variability of clouds and their properties. Due to the underlying
intercalibrated measurement record, the stability of the derived cloud
properties is ensured, which is exemplarily demonstrated for three selected
cloud variables for the entire SEVIRI disc and a European subregion. All data
products and processing levels are introduced and validation results
indicated. The sampling uncertainty of the averaged products in CLAAS is
minimized due to the high temporal resolution of SEVIRI. This is emphasized
by studying the impact of reduced temporal sampling rates taken at typical
overpass times of polar-orbiting instruments. In particular, cloud optical
thickness and cloud water path are very sensitive to the sampling rate, which
in our study amounted to systematic deviations of over 10% if only sampled
once a day. The CLAAS data set facilitates many cloud related applications at
small spatial scales of a few kilometres and short temporal scales of a~few
hours. Beyond this, the spatiotemporal characteristics of clouds on diurnal
to seasonal, but also on multi-annual scales, can be studied. |
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