Columnar Vertical Profile (CVP) Methodology for Validating Polarimetric Radar Retrievals in Ice Using In Situ Aircraft Measurements

Abstract

A novel way to process polarimetric radar data collected via plan-position indicator (PPI) scans and display those data in a time-height format is introduced. The columnar vertical profile (CVP) methodology uses radar data collected via multiple elevation scans, limited to data within a set region in range and azimuth relative to the radar, to create vertical profiles of polarimetric radar data representative of that limited region in space. This technique is compared to others existing in the literature, and various applications are discussed. Polarimetric ice microphysical retrievals are performed on CVPs created within the stratiform rain region of two mesoscale convective systems sampled during two field campaigns, where CVPs follow the track of research aircraft. Aircraft in situ data are collocated to microphysical retrieval data, and the accuracy of these retrievals is tested against other retrieval techniques in the literature.

Hydrometeor classification of quasi-vertical profiles of polarimetric radar measurements using a top-down iterative hierarchical clustering method

“…The radar is a magnetronbased system and operates at a nominal frequency of 9.375 GHz (λ ∼ 3.2 cm). The detailed characteristics of the NXPol radar can be found in Neely et al (2018). From the observations made in 2017-2018 we selected eight dates with the longest precipitation events occurring within a 30 km range of the radar presented on Fig.…”

“…Application of in situ observations for the assessment of QVP-based clusters has its limits, as not all optimal clusters were captured by the FAAM BAe 146 flights, and this process requires a comparison of data from essentially one-point measurement to the cluster based on the mean QVP domain values. An appropriate validation process would utilize columnar vertical profiles (CVPs) as described in Murphy et al (2020) with the thorough co-location of the aircraft observations. Utilizing CVPs within the presented technique is a part of the planned work for the future.…”

Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

“…where 7<b is the mean volume diameter in mm with the subscript emp denoting empirical estimates; "$ is in units of mm 6 m -3 ; and "$ is in ° km -1 . Murphy et al (2020) also estimated the number concentration and IWC using:…”

“…Polarimetric radar measurements contain information on ice properties and have been proven useful for studying ice microphysical processes (e.g., Kennedy and Rutledge, 2011;Grazioli et al 2015;Moisseev et al, 2015). Many empirical relationships were developed to provide important bulk properties such as ice water content (e.g., Ryzhkov et al, 1998;, median volume diameter and number concentration (e.g., Murphy et al, 2020), but they cannot inform the partitioning between ice species. The partitioning is of particular importance for pristine ice crystals because it not only influences snow aggregation rates (Hobbs et al, 1974;Barrett et al, 2019), which impact precipitation production and cloud lifetime (Schmitt and Heymsfield, 2014), but also acts as a major control on cloud phase partitioning (Fusina et al, 2007;Matus and L'Ecuyer, 2017).…”

Identification of snowfall microphysical processes from vertical gradients of polarimetric radar variables

“…QVPs are computed from Plan Position Indicator (PPI) radar scans at different elevation angles that are azimuthally averaged to give vertical profiles of polarimetric variables above the radar. A similar method named Columnar Vertical Profiles (CVPs) provides vertical profiles at any point of space and was presented in Murphy et al (2020). The authors emphasize that the noise and standard deviation of the signal are strongly reduced owing to the azimuthal averaging.…”