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CLAAS: the CM SAF cloud property data set using SEVIRI

机译:claas:CM SAF云属性数据设置使用Seviri

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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.
机译:提出了名为CLAA的卫星的云属性的8年记录(使用SEVIRI的云属性数据集),这是在气候监测的eumetsat卫星应用设施中得出的。数据集基于Meteosat第二代卫星的Seviro测量,其中可见和近红外通道与MODIS互相互相。应用两个最先进的检索方案确保在云检测,云垂直放置和微手术云属性中的高精度。进一步加工这些性质以每天提供每月平均量,平均昼夜周期和月度直方图。特别地,每月直方图信息增强了云及其属性的时空变异性的洞察力。由于底层的互际测量记录,确保了导出的云属性的稳定性,这是针对整个七六角盘的三个选定的云变量和欧洲次区域的三个选定的云变量示例性。介绍了所有数据产品和处理水平,并指出了验证结果。由于SEVIRI的高度时间分辨率,CLA中平均产品的采样不确定性最小化。通过研究在极地轨道仪器的典型立交桥时间下采取的时间采样率降低的时间采样率的影响,强调了这一点。特别是,云光学厚度和云水路对采样率非常敏感,我们的研究中,如果仅在每天采样一次,我们的研究中的系统偏差量超过10%。 CLAA数据集促进了几公里的小空间尺度的许多云相关应用,并且短时间略小时的时间。除此之外,可以研究季节上云的时空特征,也可以研究多年尺度。

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