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

A method for detailed evaluation of a new satellite-derived global 28-yr cloud and radiation climatology (Climate Monitoring SAF Cloud, Albedo and Radiation dataset 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 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 generating erroneous clouds over land surfaces in semi-arid regions in the sub-tropical and tropical regions are revealed. In addition, a substantial fraction 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 -274m was achieved compared to a bias of -2762m if 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. [ABSTRACT FROM AUTHOR]