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On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

机译:关于使用CALIPSO-CALIOP数据评估被动卫星影像中云产品的最佳方法:调查CM SAF CLARA-A1数据集的示例

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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.
机译:提出了一种方法,用于详细评估来自极轨NOAA和Metop卫星的新卫星衍生的全球28年云和辐射气候学(气候监测SAF云,反照率和来自AVHRR数据的辐射,称为CLARA-A1)。该方法结合了CALIPSO-CALIOP(云气溶胶激光雷达和红外探路者卫星观测-具有正交偏振的云气溶胶激光雷达)云激光雷达的1 km和5 km分辨率的云数据集,以估计云探测局限性和云顶高度估计的准确性。已显示云检测对于光学厚度大于0.30的云有效运行,除非在暮光条件下当该值增加到0.45时。在亚热带和热带地区的半干旱地区,发现白天无云表面有些误分类,导致高估了20%的云量。此外,在极地冬季,由于缺乏地表温度或云层之间的温度差或温度反差,在极地地区仍未发现大部分云(至少20-30%)。随后的云顶高度评估考虑了有关云检测极限的导出信息。结果表明,这对于取得的成果具有根本的重要性。与未采取任何措施来补偿云探测限制的情况下的-2762 m的偏差相比,实现了-274 m的总体偏差。尽管取得了这种改进,但可以得出的结论是,高层云仍然会遭受高度上的低估,而对于底层(边界层)云则相反。建议使用验证方法和在时间和空间上具有最佳匹配的专门收集的卫星数据集,以在将来更广泛地用于评估其他基于被动卫星图像的云检索方法。

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