Articles | Volume 11, issue 10
https://doi.org/10.5194/amt-11-5471-2018
https://doi.org/10.5194/amt-11-5471-2018
Research article
 | 
05 Oct 2018
Research article |  | 05 Oct 2018

Retrieval of snowflake microphysical properties from multifrequency radar observations

Jussi Leinonen, Matthew D. Lebsock, Simone Tanelli, Ousmane O. Sy, Brenda Dolan, Randy J. Chase, Joseph A. Finlon, Annakaisa von Lerber, and Dmitri Moisseev

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Cited articles

Bailey, M. P. and Hallett, J.: A Comprehensive Habit Diagram for Atmospheric Ice Crystals: Confirmation from the Laboratory, AIRS II, and Other Field Studies, J. Atmos. Sci., 66, 2888–2899, https://doi.org/10.1175/2009JAS2883.1, 2009. a
Beyer, W. H.: CRC Handbook of Mathematical Sciences, CRC Press, Boca Raton, Florida, USA, 1987. a
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small Particles, John Wiley & Sons, Inc., New York, USA, 1983. a
Botta, G., Aydin, K., Verlinde, J., Avramov, A. E., Ackerman, A. S., Fridlind, A. M., McFarquhar, G. M., and Wolde, M.: Millimeter wave scattering from ice crystals and their aggregates: Comparing cloud model simulations with X-and Ka-band radar measurements, J. Geophys. Res., 116, D00T04, https://doi.org/10.1029/2011JD015909, 2011. a
Delanoë, J. M. E., Heymsfield, A. J., Protat, A., Bansemer, A., and Hogan, R. J.: Normalized particle size distribution for remote sensing application, J. Geophys. Res.-Atmos., 119, 4204–4227, https://doi.org/10.1002/2013JD020700, 2014. a
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Short summary
We developed a technique for inferring the physical properties (amount, size and density) of falling snow from radar observations made using multiple different frequencies. We tested this method using measurements from airborne radar and compared the results to direct measurements from another aircraft, as well as ground-based radar. The results demonstrate that multifrequency radars have significant advantages over those with a single frequency in determining the snow size and density.
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