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

Abstract

Abstract. Ice and mixed phase clouds play a key role in our climate system, because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates, because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 %, and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in-situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in total ice number concentration and 44 % in total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

Climatology of the Vertical Profiles of Polarimetric Radar Variables and Retrieved Microphysical Parameters in Continental/Tropical MCSs and Landfalling Hurricanes

“…(2020) and Kedzuf et al. (2021). These initial verification tests showed good promise, and the polarimetric radar retrievals in ice and rain have already been utilized in a number of recent observational studies (e.g., Homeyer et al., 2021; Hu et al., 2020; Troemel et al., 2019; Wu et al., 2021).…”

“…Alternative statistical polarimetric retrieval methods in ice were recently suggested by Kedzuf et al. (2021) and Munchak et al. (2021) which are also designed for estimation of IWC, D m , and N t for each ice species and their mixtures along the radials of polarimetric radar data.…”

“…The quality of the polarimetric retrievals of IWC has been evaluated using in situ microphysical measurements onboard research aircrafts (Kedzuf et al., 2021; Murphy et al., 2020; Nguyen et al., 2019; Ryzhkov et al., 1998) and at the surface (Bukovcic et al., 2018, 2020), whereas the corresponding retrievals of D m and N t have been validated by Murphy et al. (2020) and Kedzuf et al.…”

“…Kedzuf et al. (2021) proposed the iterative stochastic ensemble Kalman filter approach and Munchak et al. (2021) used optimal estimation to solve for several parameters that describe PSD, relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously.…”

“…The inability of existing microphysical parameterization schemes in the NWP models to adequately capture the complexity of various microphysical processes is commonly blamed as a main source of forecast uncertainties (e.g., Fan et al., 2017; Morrison et al., 2015, 2020). Because the polarimetric radars are capable of distinguishing between hydrometeors with different microphysical properties and identifying “polarimetric fingerprints” of various microphysical processes, they are a powerful resource for optimization of cloud models (e.g., Kennedy & Rutledge, 2011; Kumjian et al., 2013, 2014; Kumjian & Ryzhkov, 2009, 2010, 2012; Ryzhkov et al., 2013).…”