Distinguishing cirrus cloud presence in autonomous lidar measurements

Abstract

Abstract. 2012 Level-2 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite-based cloud data sets are investigated for thresholds that distinguish the presence of cirrus clouds in autonomous lidar measurements, based on temperatures, heights, optical depth and phase. A thermal threshold, proposed by Sassen and Campbell (2001) C, respectively, for tops and bases) and optical depths (1.18 vs. 1.23) reflect the sensitivity to this constraint. Over 99 % of all T top ≤ −37 • C clouds are classified as ice by CALIOP Level-2 algorithms. Over 81 % of all ice clouds correspond with T top ≤ −37 • C. For instruments lacking polarized measurements, and thus practical estimates of phase, T top ≤ −37 • C provides sufficient justification for distinguishing cirrus, as opposed to the risks of glaciated liquid-water cloud contamination occurring in a given sample from clouds identified at relatively "warm" (T top > −37 • C) temperatures. Although accounting for uncertainties in temperatures collocated with lidar data (i.e., model reanalyses/sondes) may justifiably relax the threshold to include warmer cases, the ambiguity of "warm" ice clouds cannot be fully reconciled with available measurements, conspicuously including phase. Cloud top heights and optical depths are investigated, and global distributions and frequencies derived, as functions of CALIOP-retrieved phase. These data provide little additional information, compared with temperature alone, and may exacerbate classification uncertainties overall. MotivationCirrus clouds are recognized by sky gazers for their translucent and fibrous appearance, cast frequently as delicate white filaments across otherwise clear blue skies at relatively high tropospheric altitudes. To climate scientists however, cirrus clouds, which are composed almost exclusively of ice crystals, are distinct for their physical and radiative properties (e.g., Liou, 1986). As cold and optically thin counterparts to most liquid-water and mixed-phase clouds (e.g., Sassen and Cho, 1992), the net column-integrated radiative impact of cirrus cloud presence during sunlit hours varies between positive and negative, depending on the relative magnitudes of their simultaneous and offsetting contributions diurnally to tropospheric warming (infrared absorption and reemission) and cooling (solar albedo effects; Stephens et al., 1990). This attribute makes cirrus relatively unique among cloud genera. Combined with their relatively high occurrence frequencies globally (e.g., Holz et al., 2008), cirrus are significant and distinct contributors to climate overall .Lidars are primary remote-sensing tools used for monitoring cirrus clouds (e.g., Sassen, 1991). Two complementary NASA lidar projects are presently tasked with compiling Published by Copernicus Publications on behalf of the European Geosciences Union.

Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds

“…The basis for applying this thermal threshold is motivated by Campbell et al (2015). Though conservative, significant ambiguity arises from interpreting autonomous lidar signals and from distinguishing ''warm cirrus'' (typically, sheared fallstreaks decoupled from their parent cloud, which give the appearance of a cirrus cloud with an apparent top height temperature warmer than 2378C) from glaciated liquid water clouds that, though ice, are not cirrus in the phenomenological sense.…”

MPLNET V3 Cloud and Planetary Boundary Layer Detection

“…Cloud phase is determined using a cloud top temperature threshold suggested by Sassen and Campbell (2001) and Campbell et al (2015) in order to identify cirrus based on the ice nucleation threshold. An estimated cloud optical depth (COD) is provided for clouds identified as cirrus using the procedure described by Chew et al (2011).…”

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“…The presence of horizontally oriented crystals is also detected when observing specular spots from the spotlight on the clouds (Borovoi et al, 2008); flutter width is also estimated to be 0.4 • . Lidar observations of the ice clouds provide the basic information about particle orientation Chen et al, 2002;Noel and Sassen, 2005).…”