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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
SMHI, Research Department, Atmospheric remote sensing.ORCID iD: 0000-0001-8256-0228
SMHI, Research Department, Atmospheric remote sensing.
2013 (English)In: ATMOSPHERIC MEASUREMENT TECHNIQUES, ISSN 1867-1381, Vol. 6, no 5, 1271-1286 p.Article in journal (Refereed) Published
Abstract [en]

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 -274m was achieved compared to a bias of -2762m 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.

Place, publisher, year, edition, pages
2013. Vol. 6, no 5, 1271-1286 p.
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
URN: urn:nbn:se:smhi:diva-406DOI: 10.5194/amt-6-1271-2013ISI: 000321679200012OAI: oai:DiVA.org:smhi-406DiVA: diva2:800007
Available from: 2015-04-01 Created: 2015-03-31 Last updated: 2016-04-07Bibliographically approved

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CiteExportLink to record
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