Refinement of the CALIOP cloud mask algorithm

A modified cloud mask algorithm was applied to the CALIOP data to have more ability to detect the clouds in the lower atmosphere. In this algorithm, we also adopt the fully attenuation discrimination and the remain noise estimation using the data obtained at an altitude of 40 km to avoid contamination of stratospheric aerosols. The new cloud mask shows an increase in the lower cloud fraction. Comparison of the results to the data observed with a PML ground observation was also made.

Application of multiple-scattering polarization lidar for the evaluation of space-borne lidar algorithms

Multi-field of view multiple-scattering polarization lidar (MFMSPL) has been developed to measure enhanced backscattering and depolarization ratio from optically thick clouds. Collocated cloud radar observations and Monte Carlo simulations supported the system performance such as cloud detectability and values of depolarization ratio. The system is used to evaluate/improve algorithms for space-borne lidar such as CALIOP on CALIPSO and ATLID on EarthCARE.

Development of Algorithm for Discriminating Hydrometeor Particle Types with a Synergistic Use of CloudSat and CALIPSO

We developed a method for classifying hydrometeor particle types, including cloud and precipitation phase and ice crystal habit, by a synergistic use of CloudSat/Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). We investigated how the cloud phase and ice crystal habit characterized by CALIOP globally relate with radar reflectivity and temperature. The global relationship thus identified was employed to develop an algorithm for hydrometeor type classification with CPR alone. The CPR-based type classification was then combined with CALIPSO-based type characterization to give CPR-CALIOP synergy classification. A unique aspect of this algorithm is to exploit and combine the lidar's sensitivity to thin ice clouds and the radar's ability to penetrate light precipitation to offer more complete picture of vertically resolved hydrometeor type classification than has been provided by previous studies. Given the complementary nature of radar and lidar detections of hydrometeors, our algorithm delivers thirteen hydrometeor types: warm water, supercooled water, randomly-oriented ice crystal (3D-ice), horizontally-oriented plate (2D-plate), 3D-ice+2D-plate, liquid drizzle, mixed-phase drizzle, rain, snow, mixed-phase cloud, water+liquid drizzle, water+rain and unknown. The global statistics of three-dimensional occurrence frequency of each hydrometeor type revealed that 3D-ice contributes the most to the total cloud occurrence frequency (53.8%), followed by supercooled water (14.3%), 2D-plate (9.2%), rain (5.9%), warm water (5.7%), snow (4.8%), mixed-phase drizzle (2.3%), and the remaining types (4.0%). This hydrometeor type classification provides useful observation-based information for climate model diagnostics in representation of cloud phase and their microphysical characteristics.

Development of a multiple-field-of-view multiple-scattering polarization lidar: comparison with cloud radar

We developed a multiple-field-of-view multiple-scattering polarization lidar (MFMSPL) to study the microphysics of optically thick clouds. Designed to measure enhanced backscattering and depolarization ratio comparable to space-borne lidar, the system consists of four sets of parallel and perpendicular channels mounted with different zenith angles. Depolarization ratios from water clouds were large as observed by MFMSPL compared to those observed by conventional lidar. Cloud top heights and depolarization ratios tended to be larger for outer MFMSPL channels than for vertically pointing channels. Co-located 95 GHz cloud radar and MFMSPL observations showed reasonable agreement at the observed cloud top height.