The MSG-SEVIRI-based cloud property data record CLAAS-2
Clouds play a central role in the Earth's atmosphere, and satellite observations are crucial for monitoring clouds and understanding their impact on the energy budget and water cycle. Within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite App...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-07-01
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| Series: | Earth System Science Data |
| Online Access: | https://www.earth-syst-sci-data.net/9/415/2017/essd-9-415-2017.pdf |
| Summary: | Clouds play a central role in the Earth's atmosphere, and
satellite observations are crucial for monitoring clouds and understanding their
impact on the energy budget and water cycle. Within the European
Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)
Satellite Application Facility on Climate Monitoring (CM SAF), a new cloud
property data record was derived from geostationary Meteosat Spinning
Enhanced Visible and Infrared Imager (SEVIRI) measurements for the time
frame 2004–2015. The resulting CLAAS-2 (CLoud property dAtAset using SEVIRI,
Edition 2) data record is publicly available via the CM SAF website (<a href="https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002" target="_blank">https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002</a>). In this paper
we present an extensive evaluation of the CLAAS-2 cloud products, which
include cloud fractional coverage, thermodynamic phase, cloud top
properties, liquid/ice cloud water path and corresponding optical thickness
and particle effective radius. Data validation and comparisons were performed on
both level 2 (native SEVIRI grid and repeat cycle) and level 3 (daily and
monthly averages and histograms) with reference datasets derived from
lidar, microwave and passive imager measurements. The evaluation results
show very good overall agreement with matching spatial distributions and
temporal variability and small biases attributed mainly to differences in
sensor characteristics, retrieval approaches, spatial and temporal samplings and
viewing geometries. No major discrepancies were found. Underpinned by the
good evaluation results, CLAAS-2 demonstrates that it is fit for the envisaged
applications, such as process studies of the diurnal cycle of
clouds and the evaluation of regional climate models. The data record is
planned to be extended and updated in the future. |
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| ISSN: | 1866-3508 1866-3516 |