Synergies and complementarities of CloudSat-CALIPSO snow observations

• Published in: Journal of Geophysical Research: Atmospheres
• Main Authors: Battaglia, Alessandro, Delanoe, J.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley for American Geophysical Union (AGU) 2016
• Subjects: Science & Technology; Physical Sciences; Meteorology & Atmospheric Sciences; GEOSCIENCES; MULTIDISCIPLINARY; MIXED-PHASE CLOUDS; REMOTE SENSORS; ARCTIC-OCEAN; LIDAR; RADAR; SCATTERING; WATER; APPROXIMATION; RETRIEVAL; ALGORITHM; Arctic; Arctic Ocean
• Online Access: http://onlinelibrary.wiley.com/doi/10.1029/2012JD018092/abstract; http://hdl.handle.net/2381/37634; https://doi.org/10.1029/2012JD018092

[1] Four years (2007–2010) of colocated 94 GHz CloudSat radar reflectivities and 532 nm CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) backscattering coefficients are used to globally characterize snow-precipitating clouds. CALIOP is particularly useful for the detection of mixed and supercooled liquid water (SLW) layers. Liquid layers are common in snow precipitating clouds: overall/over sea/over land 49%/57%/33% of the snowy profiles present SLW or mixed-phase layers. The spatial and seasonal dependencies of our results—with snowing clouds more likely to be associated with mixed phase during summer periods—are related to snow layer top temperatures. SLW occurs within the majority (>80%) of snow-precipitating clouds with cloud tops warmer than 250 K, and is present 50% of the time when the snow-layer top temperature is about 240 K. There is a marked tendency for such layers to occur close to the top of the snow-precipitating layer (75% of the times within 500 m). Both instruments can be synergetically used for profiling ice-phase-only snow, especially for light snow (Z<0 dBZ, S<0.16 mm/h) when CALIOP is capable of penetrating, on average, more than half of the snow layer depth. These results have profound impact for deepening our understanding of ice nucleation and snow growth processes, for improving active and passive snow remote sensing techniques, and for planning snow-precipitation missions. Peer-reviewed Publisher Version