Coincident In-situ and Triple-Frequency Radar Airborne Observations in the Arctic
The dataset collected during the Radar Snow Experiment (RadSnowExp) presents the first-ever triple-frequency radar reflectivities combined with almost perfectly co-located and coincident airborne in situ microphysics probes on board the National Research Council Canada (NRC) Convair-580 aircraft. Ov...
Main Authors: | , , , , , , , |
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Format: | Text |
Language: | English |
Published: |
2021
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Subjects: | |
Online Access: | https://doi.org/10.5194/amt-2021-148 https://amt.copernicus.org/preprints/amt-2021-148/ |
Summary: | The dataset collected during the Radar Snow Experiment (RadSnowExp) presents the first-ever triple-frequency radar reflectivities combined with almost perfectly co-located and coincident airborne in situ microphysics probes on board the National Research Council Canada (NRC) Convair-580 aircraft. Over 12 hours of flight data in mixed phased and glaciated clouds with more than 3.4 hours in non-Rayleigh regions for at least one of the radar frequencies provide a unique opportunity for studying the relationship between cloud microphysical properties and radar triple-frequency signals. The in situ particle imagery data for this study include imagery from the CPI probe, which provides high resolution particle imagery and allow accurate identification of particle types including level of riming within the DFR plane. The airborne triple-frequency radar data are analysed and grouped based on the dominant particle compositions and microphysical processes (level of aggregation and riming). The results from this study are consistent with the main findings of previous modelling studies with specific regions of the dual-frequency ratio (DFR) plane associated with unique scattering properties of different ice habits, especially in clouds where radar signal is dominated by large aggregates. Moreover, the analysis shows that the close relationships between the triple-frequency signatures and particles’ bulk density, level of riming and aggregations and characteristic size of the particle size distribution (PSD). |
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