Accurate measurements of snowfall in mid- and high-latitudes are particularly important, because snow provides a vital freshwater source, and impacts glacier mass balances as well as surface albedo. However, ice water content (IWC) and snowfall rates (SR) are hard to measure due to their high spatial variability and the remoteness of polar regions. In this study, we present novel ice water content – equivalent radar reflectivity (IWC-Ze) and snowfall rate – equivalent radar reflectivity (SR-Ze) relations for 40° slanted and vertically pointing W-band radar. The relations are derived from joint in situ snowfall and remote sensing (W-band radar and radiometer) data from the SAIL site (Colorado, USA) and validated for sites in Hyytiälä (Finland), Ny-Ålesund (Svalbard), and Eriswil (Switzerland). In addition, gauge measurements from SAIL and Hyytiälä are used as an independent reference for validation. We show the dependence of IWC-Ze and SR-Ze on riming, which we utilize to reduce the spread in the IWC-Ze and SR-Ze spaces. Normalized root mean square errors (NRMSE) are below 25 % for IWC > 0.1 gm⁻³. For SR, the NRMSE is below 70 % over the whole SR range. We also present relations using liquid water path as a proxy for the occurrence of riming, which can be applied to both ground-based and space-borne radar-radiometer instruments. The latter is demonstrated using the example of the proposed ESA Earth Explorer 11 candidate mission WIVERN. With this approach, NRMSE are below 75 % for IWC > 0.1 gm⁻³ and below 80 % for SR > 0.2 mmhr⁻¹.

Observations show that the ice water content (IWC) in mixed-phase clouds (MPCs) tends to occur in clusters. However, it is not sufficiently understood which ice crystal formation and growth processes play a dominant role in IWC clustering in clouds. One important ice growth process is riming, which occurs when liquid water droplets freeze onto ice crystals upon contact. Here we use airborne measurements of MPCs at mid- and high-latitudes to investigate the spatial variability of ice clusters in clouds and how this variability is linked to riming. We use data from the IMPACTS (mid-latitudes) and the HALO-(AC)3 (high-latitudes) aircraft campaigns, where spatially and temporally colocated cloud radar and in situ measurements were collected. We derive riming and IWC by combining cloud radar and in situ measurements. Ice cluster scales in clouds are quantified using pair correlation functions.

During all analyzed flight segments, riming is responsible for 66 % and 63 % of the total IWC during IMPACTS and HALO-(AC)3, respectively. In mid-latitude MPCs, riming does not significantly change IWC cluster scales but increases the probability of cluster occurrence. In cold-air-outbreak MPCs observed during HALO-(AC)3, riming leads to additional in-cloud IWC clustering at spatial scales of 3–5 km due to the presence of mesoscale updraft features. An increased liquid water path might increase the effect, but it is not a necessary criterion. These results can be used to evaluate and constrain models' representations of MPCs.

Riming is a key process of precipitation formation in ice-containing clouds, but quantifying riming from observations is challenging, limiting our ability to evaluate the riming process in numerical weather models. One challenge for radar observations is that riming changes both the physical properties (mass, area cross-section) and scattering properties of ice particles. These changes need to be implemented consistently as a function of riming in radar forward operators, which are required for retrievals and model evaluation in observation space. In this study, mass–size, cross-section area–size, and backscattering cross-section relations are developed as a function of the normalized rime mass for aggregates composed of various monomer types (columns, dendrites, needles, plates, and rosettes). The proposed framework allows us to simulate scattering properties of aggregated ice particles consistently as a function of riming in retrievals and radar forward operators. The parameterizations are developed from a large data set of simulated rimed aggregates of different sizes and monomer crystal types. The backscattering cross-section parameterization (the “riming-dependent parameterization”) is evaluated for radar frequencies of 35.6 and 94.0 GHz and is based on the Self-Similar Rayleigh–Gans approximation (SSRGA), which is increasingly used to calculate microwave scattering of ice crystals and snowflakes. Compared with parameterizations from the literature that do not consider riming, the riming-dependent parameterization leads to significantly smaller biases in terms of backscattering cross-section. When using the particle masses and scattering properties of the individual particles simulated by the aggregation and riming model as a reference, the bias of our parameterization is below 1 dB when integrating over an exponential particle size distribution with sizes from 0.1–10 mm.

Simulated rimed aggregates generated with https://github.com/jleinonen/aggregation in setting "aggregation followed by riming". Aggregates were built from between 10 to 700 monomer crystals of columns, dendrites, needles, plates or rosettes with mean sizes of 100 or 200 micrometer. Then they were exposed to ELWP = 2.0 kg m⁻². Monomer crystals are composed of cubical elements with resolution 20 micrometer. Frozen rime droplets are also represented by 20 micrometer cubes. The data set contains folders with evolution (evol) and shape files for each monomer crystal type. For each particle one evolution and one corresponding shape file exists. The evolution (evol) file contains particle mass, rime mass, area, size, fall speed (Heymsfield&Westbrook, 2010), fall speed (Khvorostyanov&Curry, 2005) for each step during the aggregation and riming process. The corresponding shape file contains the x,y,z positions of the cubical elements that compose the particle for each step. For further documentation see readme.