Abstract
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, the main features of the precipitation formation processes, as observed in Finland, are presented.
Highlights
The majority of precipitation during both winter and summer originates from ice clouds (Field and Heymsfield, 2015)
To identify and document such features, a classification method that uses vertical profiles of dual-polarization radar observations can be used. We have developed such an unsupervised classification method based on k-means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase
The centroid profiles of dualpolarization radar variables are inverse transformed from corresponding centroids in principal component analysis (PCA) space
Summary
The majority of precipitation during both winter and summer originates from ice clouds (Field and Heymsfield, 2015). Dual-polarization radar observations are used in fuzzy logic classification to identify the dominant hydrometeor type present in a radar volume (e.g., Chandrasekar et al, 2013; Thompson et al, 2014). Such methods work very well for classification of hydrometeors of summer precipitations and some features of winter precipitation types. A modification for the hydrometeor classifiers was proposed to make the algorithms aware of the surroundings by incorporating measurements from neighboring radar volumes (Bechini and Chandrasekar, 2015; Grazioli et al, 2015b) This step has greatly improved classification robustness, but it aims to identify particle types instead of fingerprints of microphysical processes
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