Triple-Frequency Radar Reflectivity Signatures of Snow: Observations and Comparisons with Theoretical Ice Particle Scattering Models

An observation-based study is presented that utilizes aircraft data from the 2003 Wakasa Bay Advanced Microwave Scanning Radiometer Precipitation Validation Campaign to assess recent advances in the modeling of microwave scattering properties of nonspherical ice particles in the atmosphere. Previous...

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Veröffentlicht in:Journal of applied meteorology and climatology 2014-04, Vol.53 (4), p.1080-1098
Hauptverfasser: Kulie, Mark S., Hiley, Michael J., Bennartz, Ralf, Kneifel, Stefan, Tanelli, Simone
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Sprache:eng
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Zusammenfassung:An observation-based study is presented that utilizes aircraft data from the 2003 Wakasa Bay Advanced Microwave Scanning Radiometer Precipitation Validation Campaign to assess recent advances in the modeling of microwave scattering properties of nonspherical ice particles in the atmosphere. Previous work has suggested that a triple-frequency (Ku–Ka–W band) reflectivity framework appears capable of identifying key microphysical properties of snow, potentially providing much-needed constraints on significant sources of uncertainty in current snowfall retrieval algorithms used for microwave remote sensing instruments. However, these results were based solely on a modeling framework. In contrast, this study considers the triple-frequency approach from an observational perspective using airborne radar observations from the Wakasa Bay field campaign. After accounting for several challenges with the observational dataset, such as beam mismatching and attenuation, observed dual-wavelength ratio results are presented that confirm both the utility of a multifrequency approach to snowfall retrieval and the validity of the unique signatures predicted by complex aggregate ice particle scattering models. This analysis provides valuable insight into the microphysics of frozen precipitation that can in turn be applied to more readily available single- and dual-frequency systems, providing guidance for future precipitation retrieval algorithms.
ISSN:1558-8424
1558-8432
DOI:10.1175/JAMC-D-13-066.1