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

Abstract

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 13 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 observation‐based insight for climate model diagnostics in representation of cloud phase and their microphysical characteristics.

Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation

“…The diagnostic tool has been designed to reveal potential uncertainties in modeled warm rain processes in GCMs more effectively and simply. The multiplatform products can also be extended to include other diagnostics for mixed-phase and ice clouds (e.g., Mülmenstädt et al, 2015;Kikuchi et al, 2017) in future work. Requests for specific diagnostics, particularly those requiring COSP subcolumn output for fast-process evaluations, are welcome.…”

“…The current version of the simulator package comprises the ISCCP (Klein and Jakob, 1999;Webb et al, 2001), MODIS (Pincus et al, 2012), MISR (Marchand and Ackerman, 2010), PARASOL (Konsta et al, 2016), CloudSat (Haynes et al, 2007), and CALIPSO (Chepfer et al, 2008;Cesana and Chepfer, 2012) simulators. To effectively utilize these capabilities, there is a growing need for "processoriented" model diagnostics (Maloney et al, 2019), which have been recognized as essential to the community effort to advance climate modeling (Tsushima et al, 2017;Webb et al, 2017).…”

Characterizing Vertical Particle Structure of Precipitating Cloud System From Multiplatform Measurements of A‐Train Constellation

“…In this study, we analyzed satellite data set of over 3 years (2007–2009) covered by various instruments loaded on the satellites that fly in a constellation orbit “the A‐Train.” We used the following products: (1) collocated CPR and CALIOP measurements, so‐called “CloudSat‐CALIPSO Merged Data Set”, with a uniform resolution of 240 m vertical × 1.1 km horizontal basically along CloudSat profiles (Hagihara et al, ), which consist of radar reflectivity and lidar backscattering coefficient as well as temperature from the European Centre for Medium‐Range Weather Forecasts; (2) the CloudSat‐CALIPSO hydrometeor particle phase and shape (hereafter, hydrometeor particle type) retrieved from the Merged Data Set using the algorithm developed by Kikuchi et al (); (3) MOD06‐5KM‐AUX product, which provides MODerate resolution Imaging Spectroradiometer (MODIS) cloud top temperature (CTT) collocated with CloudSat along‐track sampling; and (4) Aqua Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) precipitation rate (Liu & Curry, , ) provided in Japan Aerospace Exploration Agency (JAXA) L2 Standard Product selected for the nearest pixels per along‐track CloudSat pixels. Of the total 13 hydrometeor particle types given in (2), we selected the following types that account for 96.4% of the total (see Kikuchi et al, for the percentage breakdown): water (combining warm water and supercooled water), 3D‐ice, 2D‐plate, drizzle (including liquid drizzle and mixed‐phase drizzle), rain, and snow. Note that 3D‐ice refers to ice crystals that are randomly oriented in three‐dimensional space including bullet rosettes, columns, randomly oriented plates, and aggregates, whereas 2D‐plate refers to ice plates that are oriented horizontally in two‐dimensional space (Iwasaki & Okamoto, ; Okamoto et al, ; Sato & Okamoto, ), the former and latter being distinguishable with the lidar measurement capability.…”

“…The vertical structure information of microphysical habit over the globe is offered in recent decade by new satellite‐borne active instruments. In particular, polarization measurement capability by lidar observation of Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) provides vertical particle phase and shape information more directly than passive instruments, leading to development of particle habit identification from lidar (Cesana & Chepfer, ; Hu et al, ; Yoshida et al, ) alone or in combination with the spaceborne Cloud Profiling Radar (CPR; Ceccaldi et al, ; Kikuchi et al, ).…”

The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

“…However, as the lidar penetrates within cloudy layers, the signal eventually attenuates completely for optical thickness greater than 3 to 5. Therefore, it is not always possible to observe the full troposphere with a space-borne lidar, which may cause differences in satellite-based cloud climatologies obtained from different instruments (Kikuchi et al, 2017;Thorsen et al, 2013). In these instances -i.e., in deep convective clouds or in the storm tracks-, the CPR capability complements cloud profiles beneath the height at which the lidar attenuates, although the CPR clutter prevents using CloudSat data below ~ 1000 m. Unfortunately, the RL-GeoProf product is only available for a short period of time (~ 4.5 years) due to the severe anomaly of April 2011, which is why CloudSat-CALIPSO observations satisfy i) but only partially ii).…”