Polar Liquid Cloud Base Detection Algorithms for High Spectral Resolution or Micropulse Lidar Data

Liquid layers in clouds affect their microphysical processes, as well as the surface energy budget. Studies focusing on these and other areas of research are often in need of skillful estimation of liquid-bearing cloud layer boundaries. The bases of these layers are predominantly determined by ground-based lidar instruments. Most studies requiring liquid cloud base height (LCBH) information use either fixed lidar parameter (depolarization and/or backscatter cross section) thresholds or cloud base height data products that do not distinguish between ice and liquid, all of which might introduce inconsistencies and errors in the resolved LCBH. In this paper, two explicit LCBH detection algorithms are presented. The first algorithm uses the high spectral resolution lidar (HSRL) data. Examination of this algorithm in multiple cases and scenarios during numerous days and first-order comparison with microwave-radiometer data show satisfactory results. The second algorithm incorporates widely available micropulse lidar (MPL) data for the LCBH detection. A 1-year long comparison of data gathered at Barrow, Alaska, and McMurdo Station, Antarctica, which includes other cloud base detection methodologies (ceilometer, MPL value-added product cloud base height, and LCBHs detected using a fixed MPL depolarization threshold), emphasizes the merits of the presented MPL algorithm. Examination of several unusual LCBH configurations suggests that the current practice of operating lidar at a tilting angle of 4° off zenith may not be sufficient to avoid specular reflection from oriented ice crystals. Data collected at Madison Wisconsin are used to show that specular reflection may impact measurements even at 4°.