From CloudSat-CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations

• Published in: Journal of geophysical research. Atmospheres 2013-07, Vol.118 (14), p.7962-7981
• Main Authors: Ceccaldi, M., Delanoë, J., Hogan, R. J., Pounder, N. L., Protat, A., Pelon, J.
• Format: Article
• Language: English
• Subjects: Airborne instruments; Airborne radar; Airborne remote sensing; Algorithms; Atmospheric and Oceanic Physics; Classification; Cloud classification; Clouds; Configurations; DARDAR; Data; Detection; Doppler effect; Doppler sonar; Earth Sciences; Earth, ocean, space; Exact sciences and technology; External geophysics; Field tests; Geophysics; Hydrometeors; Lidar; Meteorology; Physics; Pixels; Planetary evolution; Radar; Resolution; Sciences of the Universe; Spatial distribution; Spectral resolution; Troposphere; Wavelength
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1002/jgrd.50579

This paper presents the implementation of a new version of the DARDAR (radar lidar) classification derived from CloudSat and CALIPSO data. The resulting target classification called DARDAR v2 is compared to the first version called DARDAR v1. Overall DARDAR v1 reports more cloud or rain pixels than DARDAR v2. In the low troposphere this is because v1 detects too many liquid cloud pixels, and in the higher troposphere this is because v2 is more restrictive in lidar detection than v1. Nevertheless, the spatial distribution of different types of hydrometeors show similar patterns in both classifications. The French airborne Radar‐Lidar (RALI) platform carries a CloudSat/CALIPSO instrument configuration (lidar at a wavelength of 532nm and a 95GHz cloud radar) as well as an EarthCare instrument configuration (high spectral resolution lidar at 355nm and a 95GHz Doppler cloud radar). It therefore represents an ideal go‐between for A‐Train and EarthCare. The DARDAR v2 classification algorithm is adapted to RALI data for A‐Train overpasses during dedicated airborne field experiments using the lidar at 532nm and the radar Doppler measurements. The results from the RALI classification are compared with the DARDAR v2 classification to identify where the classification should still be interpreted with caution. Finally, the RALI classification algorithm with lidar at 532nm is adapted to RALI with high spectral resolution lidar data at 355nm in preparation for EarthCare. Key Points A new DARDAR classification is developed from CloudSat and CALIOP profiles The new version is compared to the first one and it shows improvements The method is adapted to airborne data for validation and preparation to E-Care