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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-307
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-307
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 20 Aug 2019

Submitted as: research article | 20 Aug 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Unsupervised classification of vertical profiles of dual polarization radar variables

Jussi Tiira1 and Dmitri N. Moisseev1,2 Jussi Tiira and Dmitri N. Moisseev
  • 1Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
  • 2Finnish Meteorological Institute, Helsinki, Finland

Abstract. Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an unsupervised classification method. The method is based on k-means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase. For rain events, the classification is applied to radar profiles truncated at the melting layer top. For the snowfall cases, the surface air temperature is used as an additional input parameter. The proposed unsupervised classification was applied to 3.5 years of data collected by the Finnish Meteorological Institute Ikaalinen radar. The vertical profiles of radar variables were computed above the University of Helsinki Hyytiälä station, located 64 km east of the radar. Using these data, we show that the profiles of radar variables can be grouped into 10 and 16 classes for rainfall and snowfall events respectively. These classes seem to capture most important snow growth and ice cloud processes. Using this classification, main features of the precipitation formation processes, as observed in Finland, are presented.

Jussi Tiira and Dmitri N. Moisseev
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Jussi Tiira and Dmitri N. Moisseev
Jussi Tiira and Dmitri N. Moisseev
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Latest update: 22 Sep 2019
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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 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.
Modern weather radars are sensitive for properties of precipitating snow particles, such as...
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