Cloud and Precipitation Particle Identification Using Cloud Radar and Lidar Measurements: Retrieval Technique and Validation

Abstract

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.

Characterization of Southern Ocean Boundary Layer Clouds Using Airborne Radar, Lidar, and In Situ Cloud Data: Results From SOCRATES

“…A comparison of the results of the PID scheme to phase identified from the in situ observations within SO BL clouds indicated an agreement rate exceeding 70%, comparable to previous evaluation of the PID scheme (Romatschke & Vivekanandan, 2022). Discrepancies between the PID scheme and the in situ observations include an underestimation of SLD (SLD was only about half as frequent in the PID scheme as in the in situ data), and an overabundance of ice (about half of "small ice" classifications were liquid phase in situ; though "large ice" compared well).…”

“…The in situ observations are shown in Figure 5d in the form of a color-coded line following the aircraft's altitude, which cuts through the PID retrievals at around 03:38 UTC. A gap in the availability of the PID around the location of the aircraft is caused by the ∼100 m zone of invalid data around the radar due to near-aircraft effects (Romatschke & Vivekanandan, 2022).…”

“…The sample rate of the HSRL is 2 Hz, with vertical range resolution of 7.5 m (Romatschke & Vivekanandan, 2022). Romatschke and Vivekanandan (2022) describe the development of a remote-sensing based particle-identification (PID) scheme that uses data from the HCR and HSRL. As boundaries between particle types within clouds generally are not sharply and clearly defined, the PID scheme employs a fuzzy-logic algorithm to determine particle type.…”

“…Zaremba et al (2020) describes its use during SOCRATES. The sample rate of the HSRL is 2 Hz, with vertical range resolution of 7.5 m (Romatschke & Vivekanandan, 2022). Romatschke and Vivekanandan (2022) describe the development of a remote-sensing based particle-identification (PID) scheme that uses data from the HCR and HSRL.…”

“…Before using the PID scheme to investigate the properties of SO BL clouds statistically, it is necessary to evaluate its performance against the collected in situ data. In their initial verification of the PID scheme, Romatschke and Vivekanandan (2022) found hit rates (averaged among all particle classifications) of approximately 71% during SOCRATES when compared to the in situ, collocated University of Washington Liquid-Ice Discriminator data set (Atlas et al, 2021). The following comparison procedure serves to double-check this result against the modified D'Alessandro (2021) phase product.…”