Accurate measurements of snowfall in mid-latitudes 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 (SRs) 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 (NRMSEs) 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, NRMSEs are below 75 % for IWC>0.1 gm-3 and below 80 % for SR>0.2 mmh-1.

Snowfall is an important climate change indicator affecting surface albedo, glaciers, sea ice, freshwater storage, cloud lifetime, and ecosystems. Precise snowfall measurements at high latitudes are particularly important for the estimation of the mass balance of ice sheets; however, the snowfall is difficult to quantify with in situ measurements in those locations. In this context, spaceborne radar and radiometer atmospheric missions can help in the assessment of snowfall at high latitudes. The decommissioned NASA CloudSat mission provided invaluable information about global snowfall climatology from 2006 to 2023. The CloudSat-based estimates of global snowfall are considered the reference for global snowfall estimates, but these data suffer from poor sampling and the inability to see shallow or retrieve heavy precipitation, which limits their use, for example, as input to surface mass balance models of the major ice sheets. WIVERN (WInd VElocity Radar Nephoscope), one of the ESA Earth Explorer 11 selected missions, is equipped with a conical scanning 94 GHz Doppler radar and a passive 94 GHz radiometer, with the main objective of measuring global in-cloud horizontal winds, but also quantifying cloud ice water content and precipitation rate. Its conically scanning system, with a 42° incidence angle, is expected to reduce the radar blind zone near the surface (especially over the ocean) and allows the mission to have a swath width of 800 km and 70 times more sampled points than a fixed-looking instrument. The proposed radar measurements tackle the current uncertainties in snowfall estimates, highly improving the sampling frequency and accuracy of snowfall measurements. The uncertainty in snowfall measurements arises from various factors, including the diurnal cycle, uncertainty in the <Z-<S relationship, and the sampling error. This study quantifies each of these contributors individually and demonstrates the improved sampling capabilities of the WIVERN conically scanning geometry for some specific regions (Antarctica, Greenland) by computing the sampling error at different spatial and temporal scales via simulations of WIVERN vs. CloudSat orbits and scanning geometry, based on the snowfall rates produced by ERA5 reanalysis. Results show that a WIVERN-like conically scanning system significantly reduces the uncertainty in polar snowfall estimates if compared to a CloudSat-like near-nadir fixed viewing geometry. While CloudSat generates acceptable errors at the annual zonal scales, WIVERN can produce estimates within the climatological variability for latitude-longitude domain larger than 0.5° × 0.5° already at the monthly timescale, making it a valuable product for regional climate model evaluation and as an input to surface mass balance models of the major ice sheets and glaciers.

The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.

Riming is a key precipitation formation process in mixed-phase clouds which efficiently converts cloud liquid to ice water. Here, we present two methods to quantify riming of ice particles from airborne observations with the normalized rime mass, which is the ratio of rime mass to the mass of a size-equivalent spherical graupel particle. We use data obtained during the HALO-(AC)3 aircraft campaign, where two aircraft collected radar and in situ measurements that were closely spatially and temporally collocated over the Fram Strait west of Svalbard in spring 2022. The first method is based on an inverse optimal estimation algorithm for the retrieval of the normalized rime mass from a closure between cloud radar and in situ measurements during these collocated flight segments (combined method). The second method relies on in situ observations only, relating the normalized rime mass to optical particle shape measurements (in situ method). We find good agreement between both methods during collocated flight segments with median normalized rime masses of 0.024 and 0.021 (mean values of 0.035 and 0.033) for the combined and in situ method, respectively. Assuming that particles with a normalized rime mass smaller than 0.01 are unrimed, we obtain average rimed fractions of 88ĝ€¯% and 87ĝ€¯% over all collocated flight segments. Although in situ measurement volumes are in the range of a few cubic centimeters and are therefore much smaller than the radar volume (about 45ĝ€¯m footprint diameter at an altitude of 500ĝ€¯m above ground, with a vertical resolution of 5ĝ€¯m), we assume they are representative of the radar volume. When this assumption is not met due to less homogeneous conditions, discrepancies between the two methods result. We show the performance of the methods in a case study of a collocated segment of cold-air outbreak conditions and compare normalized rime mass results with meteorological and cloud parameters. We find that higher normalized rime masses correlate with streaks of higher radar reflectivity. The methods presented improve our ability to quantify riming from aircraft observations.

Snowfall rate (SR) estimates over Antarctica are sparse and characterised by large uncertainties. Yet, observations by precipitation radar offer the potential to get better insight in Antarctic SR. Relations between radar reflectivity (Ze) and snowfall rate (Ze-SR relations) are however not available over Antarctica. Here, we analyse observations from the first Micro Rain Radar (MRR) in Antarctica together with an optical disdrometer (Precipitation Imaging Package; PIP), deployed at the Princess Elisabeth station. The relation Ze = A*SR^B was derived using PIP observations and its uncertainty was quantified using a bootstrapping approach, randomly sampling within the range of uncertainty. This uncertainty was used to assess the uncertainty in snowfall rates derived by the MRR. We find a value of A = 18 [11–43] and B = 1.10 [0.97–1.17]. The uncertainty on snowfall rates of the MRR based on the Ze-SR relation are limited to 40%, due to the propagation of uncertainty in both Ze as well as SR, resulting in some compensation. The prefactor (A) of the Ze-SR relation is sensitive to the median diameter of the snow particles. Larger particles, typically found closer to the coast, lead to an increase of the value of the prefactor (A = 44). Smaller particles, typical of more inland locations, obtain lower values for the prefactor (A = 7). The exponent (B) of the Ze-SR relation is insensitive to the median diameter of the snow particles. In contrast with previous studies for various locations, shape uncertainty is not the main source of uncertainty of the Ze-SR relation. Parameter uncertainty is found to be the most dominant term, mainly driven by the uncertainty in mass-size relation of different snow particles. Uncertainties on the snow particle size distribution are negligible in this study as they are directly measured. Future research aiming at reducing the uncertainty of Ze-SR relations should therefore focus on obtaining reliable estimates of the mass-size relations of snow particles.