Cloud and precipitation particle identification using cloud radar and Lidar measurements: Retrieval technique and validation

This paper describes a technique for identifying hydrometeor particle types using airborne HIAPER Cloud Radar (HCR) and High Spectral Resolution Lidar (HSRL) observations. HCR operates at a frequency of 94 GHz (3 mm wavelength), while HSRL is an eye-safe lidar system operating at a wavelength of 532...

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Bibliographic Details
Published in:Earth and Space Science
Other Authors: Romatschke, Ulrike (author), Vivekanandan, Jothiram (author)
Format: Article in Journal/Newspaper
Language:English
Published: 2022
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Online Access:https://doi.org/10.1029/2022EA002299
Description
Summary:This paper describes a technique for identifying hydrometeor particle types using airborne HIAPER Cloud Radar (HCR) and High Spectral Resolution Lidar (HSRL) observations. HCR operates at a frequency of 94 GHz (3 mm wavelength), while HSRL is an eye-safe lidar system operating at a wavelength of 532 nm. Both instruments are deployed on the NSF-NCAR HIAPER aircraft. HCR is designed to fly in an underwing pod and HSRL is situated in the cabin. The HCR and HSRL data used in this study were collected during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES). Comprehensive observations of the vertical distributions of liquid and mixed-phase clouds were obtained using in situ probes on the aircraft and remote sensing instruments. Hydrometeor particle types were retrieved from HCR, HSRL, and temperature fields with a newly developed fuzzy logic particle identification (PID) algorithm. The PID results were validated with in situ measurements collected onboard the HIAPER aircraft by a 2D-Stereo (2D-S) cloud probe. Particle phases derived from the PID results compare well with those obtained from the 2D-S observations and agree in over 70% of cases. Size distributions are also consistent between the two methods of observation. Knowledge of the particle type distribution gained from the PID results can be used to constrain microphysical parameterizations and improve the representation of cloud radiative effects in weather and climate models. 1852977