Polar stratospheric cloud climatology based on CALIPSO spaceborne lidar measurements from 2006 to 2017

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite has been observing polar stratospheric clouds (PSCs) from mid-June 2006 until the present. The spaceborne lidar profiles PSCs with unprecedented...

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Veröffentlicht in:Atmospheric chemistry and physics 2018-08, Vol.18 (15), p.10881-10913
Hauptverfasser: Pitts, Michael C, Poole, Lamont R, Gonzalez, Ryan
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Sprache:eng
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Zusammenfassung:The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite has been observing polar stratospheric clouds (PSCs) from mid-June 2006 until the present. The spaceborne lidar profiles PSCs with unprecedented spatial (5 km horizontal × 180 m vertical) resolution and its dual-polarization capability enables classification of PSCs according to composition. Nearly coincident Aura Microwave Limb Sounder (MLS) measurements of the primary PSC condensables (HNO.sub.3 and H.sub.2 O) provide additional constraints on particle composition. A new CALIOP version 2 (v2) PSC detection and composition classification algorithm has been implemented that corrects known deficiencies in previous algorithms and includes additional refinements to improve composition discrimination. Major v2 enhancements include dynamic adjustment of composition boundaries to account for effects of denitrification and dehydration, explicit use of measurement uncertainties, addition of composition confidence indices, and retrieval of particulate backscatter, which enables simplified estimates of particulate surface area density (SAD) and volume density (VD). The over 11 years of CALIOP PSC observations in each v2 composition class conform to their expected thermodynamic existence regimes, which is consistent with previous analyses of data from 2006 to 2011 and underscores the robustness of the v2 composition discrimination approach.
ISSN:1680-7324
1680-7316
1680-7324
DOI:10.5194/acp-18-10881-2018