Abstract
The occurrence of supercooled liquid water in mixed-phase cloud (MPC) affects their cloud microphysical and radiative properties. The prevalence of MPCs in the mid- and high latitudes translates these effects to significant contributions to Earth’s radiative balance and hydrological cycle. The current study develops and assesses a radar-only, moment-based phase partition technique for the demarcation of supercooled liquid water volumes in arctic, MPC conditions. The study utilizes observations from the Ka band profiling radar, the collocated high spectral resolution lidar, and ambient temperature profiles from radio sounding deployments following a statistical analysis of 5.5 years of data (January 2014–May 2019) from the Atmospheric Radiation Measurement observatory at the North Slope of Alaska. The ice/liquid phase partition occurs via a per-pixel, neighborhood-dependent algorithm based on the premise that the partitioning can be deduced by examining the mean values of locally sampled probability distributions of radar-based observables and then compare those against the means of climatologically derived, per-phase probability distributions. Analyzed radar observables include linear depolarization ratio (LDR), spectral width, and vertical gradients of reflectivity factor and radial velocity corrected for vertical air motion. Results highlight that the optimal supercooled liquid water detection skill levels are realized for the radar variable combination of spectral width and reflectivity vertical gradient, suggesting that radar-based polarimetry, in the absence of full LDR spectra, is not as critical as Doppler capabilities. The cloud phase masking technique is proven particularly reliable when applied to cloud tops with an Equitable Threat Score (ETS) of 65%; the detection of embedded supercooled layers remains much more uncertain (ETS = 27%).
Highlights
The phase of hydrometeors in cloud and precipitation systems is broadly classified as liquid, ice, and mixed-phase
This study presented the formulation and assessment of a radar-only, moment-based methodology for the ice/liquid phase partitioning in arctic, mixed-phase conditions
The analysis covered a 5.5-year period of Atmospheric Radiation Measurement (ARM) NSA climate research facility data (January 2014–May 2019) collected by the polarimetric, Ka band Doppler profiler, the collocated High Spectral Resolution Lidar (HSRL), and radio sounding deployments
Summary
The phase of hydrometeors in cloud and precipitation systems is broadly classified as liquid, ice, and mixed-phase. Examples of the more commonly deployed sensors include millimeter cloud radar systems (MMCR), ceilometers, microwave radiometers, and radiosondes, the depolarization lidar usually resides at the heart of phase discrimination methods. This is due to the lidar capability in separating liquid from ice thanks to its high backscatter and low depolarization [23,24,25]. This study constitutes an effort to formulate a radar-only, moment-based technique for the detection of SLW volumes in arctic, mixed-phase conditions by exploring point-wise radar observables and radar-based variables that account for volumetric growth and sedimentation variation with altitude within localized time-height neighborhoods. Supplementary information is provided in appendices A and B regarding lidar-based cloud phase mask criteria and a brief description of higher order numerical derivation schemes applied in the gradient’s computation
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