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• W4213087844 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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 <span class=inline-formula>log ₁₀IWC</span> and <span class=inline-formula>log ₁₀<i>D</i>_m</span>. 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." @default.
• W4213087844 created "2022-02-24" @default.
• W4213087844 date "2021-09-09" @default.
• W4213087844 modified "2023-09-28" @default.
• W4213087844 title "Comment on amt-2021-227" @default.
• W4213087844 doi "https://doi.org/10.5194/amt-2021-227-rc1" @default.
• W4213087844 hasPublicationYear "2021" @default.
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