Articles | Volume 13, issue 3
Research article
13 Mar 2020
Research article |  | 13 Mar 2020

Unsupervised classification of vertical profiles of dual polarization radar variables

Jussi Tiira and Dmitri Moisseev

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Cited articles

Andrić, J., Kumjian, M. R., Zrnić, D. S., Straka, J. M., and Melnikov, V. M.: Polarimetric Signatures above the Melting Layer in Winter Storms: An Observational and Modeling Study, J. Appl. Meteorol. Clim., 52, 682–700,, 2013. a
Arthur, D. and Vassilvitskii, S.: k-means++: The advantages of careful seeding, 1027–1035, Society for Industrial and Applied Mathematics, 2007. a
Bechini, R. and Chandrasekar, V.: A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications, J. Atmos. Ocean. Tech., 32, 22–47,, 2015. a
Bechini, R., Baldini, L., and Chandrasekar, V.: Polarimetric Radar Observations in the Ice Region of Precipitating Clouds at C-Band and X-Band Radar Frequencies, J. Appl. Meteorol. Clim., 52, 1147–1169,, 2013. a, b
Cattell, R. B.: The Scree Test For The Number Of Factors, Multivar. Behav. Res., 1, 245–276, 1966. a
Short summary
Modern weather radars are sensitive for properties of precipitating snow particles, such as their sizes, shapes and number concentration. Vertical profiles of such radar measurements can be used for studying the processes through which snow is formed. We created a profile classification method for this purpose, and we show how it can be used for automatic identification of snow growth processes. Being able to identify the processes is expected to improve radar-based precipitation estimation.