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| Titel |
Hydrometeor classification from polarimetric radar measurements: a clustering approach |
| VerfasserIn |
J. Grazioli, D. Tuia, A. Berne |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 1 ; Nr. 8, no. 1 (2015-01-09), S.149-170 |
| Datensatznummer |
250116044
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| Publikation (Nr.) |
 copernicus.org/amt-8-149-2015.pdf |
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| Zusammenfassung |
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 (nopt = 7) 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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