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Hydrometeor classification from polarimetric radar measurements: a clustering approach


Grazioli, Jacopo; Tuia, Devis; Berne, Alexis (2015). Hydrometeor classification from polarimetric radar measurements: a clustering approach. Atmospheric Measurement Techniques, 8(1):149-170.

Abstract

A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number of hydrometeor classes (nopt) that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second step, the nopt clusters are assigned to the appropriate hydrometeor class by means of human interpretation and comparisons with the output of other classification techniques. The main innovation in the proposed method is the unsupervised part: the hydrometeor classes are not defined a priori, but they are learned from data. The approach is applied to data collected by an X-band polarimetric weather radar during two field campaigns (from which about 50 precipitation events are used in the present study). Seven hydrometeor classes (noptD7) have been found in the data set, and they have been identified as light rain (LR), rain (RN), heavy rain (HR), melting snow (MS), ice crystals/small aggregates (CR), aggregates (AG), and rimed-ice particles (RI).

Abstract

A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number of hydrometeor classes (nopt) that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second step, the nopt clusters are assigned to the appropriate hydrometeor class by means of human interpretation and comparisons with the output of other classification techniques. The main innovation in the proposed method is the unsupervised part: the hydrometeor classes are not defined a priori, but they are learned from data. The approach is applied to data collected by an X-band polarimetric weather radar during two field campaigns (from which about 50 precipitation events are used in the present study). Seven hydrometeor classes (noptD7) have been found in the data set, and they have been identified as light rain (LR), rain (RN), heavy rain (HR), melting snow (MS), ice crystals/small aggregates (CR), aggregates (AG), and rimed-ice particles (RI).

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5 citations in Web of Science®
6 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2015
Deposited On:23 Dec 2015 10:00
Last Modified:05 Apr 2016 19:45
Publisher:Copernicus Publications
ISSN:1867-1381
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.5194/amt-8-149-2015
Related URLs:http://www.atmos-meas-tech.net/8/149/2015/ (Publisher)

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