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Abstract
Abstract. An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for log 10IWC and log 10Dm. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data.
Highlights
Quantifying snowfall rates is essential for understanding the water cycle in middle and high altitudes
Radar measurements offer better spatial and temporal coverage, but their interpretation is subject to errors/uncertainties that follow from the assumptions made about the scattering properties of the targets in the radar volume; those depend on the snow particle size, density, shape and structure (e.g. Kuo et al, 2016; Eriksson et al, 2018)
The retrieval utilises triple-frequency radar measurements and is based on Bayes’ rule. It does not assume any functional form of the particle size distribution, but it is based on several datasets collected during historical airborne campaigns
Summary
Quantifying snowfall rates is essential for understanding the water cycle in middle and high altitudes. Despite the undeniable importance of precipitation in the solid phase, there is large discrepancy between different snowfall accumulation estimates (Mroz et al, 2021b), which reflects a high degree of uncertainty in these products. Because different frequency radars respond differently to the microphysical properties of snow (once their wavelengths become comparable with the size of snow aggregates), multifrequency algorithms were recognised as a potential tool for solid-phase precipitation studies (Hogan et al, 2000; Kneifel et al, 2011). The retrieval utilises triple-frequency radar measurements and is based on Bayes’ rule. It does not assume any functional form of the particle size distribution, but it is based on several datasets collected during historical airborne campaigns.
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