Atmos. Chem. Phys. Discuss., 13, 26451-26487, 2013
www.atmos-chem-phys-discuss.net/13/26451/2013/
doi:10.5194/acpd-13-26451-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
Review Status
This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
CLAAS: the CM SAF cloud property dataset using SEVIRI
M. Stengel1, A. Kniffka1, J. F. Meirink2, M. Lockhoff1, J. Tan1, and R. Hollmann1
1Deutscher Wetterdienst (DWD), Offenbach, Germany
2Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands

Abstract. An 8 yr 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 dataset is based on SEVIRI measurements of the Meteosat Second Generation satellites, of which the visible and near-infrared channels were intercalibrated with MODIS. Including latest development components of the two applied state-of-the-art retrieval schemes ensure 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 collected histogram information enhance 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 disk 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 dataset 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.

Citation: Stengel, M., Kniffka, A., Meirink, J. F., Lockhoff, M., Tan, J., and Hollmann, R.: CLAAS: the CM SAF cloud property dataset using SEVIRI, Atmos. Chem. Phys. Discuss., 13, 26451-26487, doi:10.5194/acpd-13-26451-2013, 2013.
 
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