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| Titel |
Identification of hydrometeor mixtures in polarimetric radar measurements and their linear de-mixing |
| VerfasserIn |
Nikola Besic, Jordi Figueras i Ventura, Jacopo Grazioli, Marco Gabella, Urs Germann, Alexis Berne |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250148435
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| Publikation (Nr.) |
 EGU/EGU2017-12693.pdf |
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| Zusammenfassung |
| The issue of hydrometeor mixtures affects radar sampling volumes without a clear dominant
hydrometeor type. Containing a number of different hydrometeor types which significantly
contribute to the polarimetric variables, these volumes are likely to occur in the vicinity of the
melting layer and mainly, at large distance from a given radar. Motivated by potential benefits
for both quantitative and qualitative applications of dual-pol radar, we propose a method for
the identification of hydrometeor mixtures and their subsequent linear de-mixing. This
method is intrinsically related to our recently proposed semi-supervised approach for
hydrometeor classification.
The mentioned classification approach [1] performs labeling of radar sampling volumes
by using as a criterion the Euclidean distance with respect to five-dimensional centroids,
depicting nine hydrometeor classes. The positions of the centroids in the space formed by
four radar moments and one external parameter (phase indicator), are derived through a
technique of k-medoids clustering, applied on a selected representative set of radar
observations, and coupled with statistical testing which introduces the assumed microphysical
properties of the different hydrometeor types.
Aside from a hydrometeor type label, each radar sampling volume is characterized by an
entropy estimate, indicating the uncertainty of the classification. Here, we revisit the
concept of entropy presented in [1], in order to emphasize its presumed potential for
the identification of hydrometeor mixtures. The calculation of entropy is based
on the estimate of the probability (pi ) that the observation corresponds to the
hydrometeor type i (i = 1,⋅⋅⋅9) . The probability is derived from the Euclidean
distance (di ) of the observation to the centroid characterizing the hydrometeor
type i . The parametrization of the d → p transform is conducted in a controlled
environment, using synthetic polarimetric radar datasets. It ensures balanced entropy
values: low for pure volumes, and high for different possible combinations of mixed
hydrometeors.
The parametrized entropy is further on applied to real polarimetric C and X band radar
datasets, where we demonstrate the potential of linear de-mixing using a simplex formed by a
set of pre-defined centroids in the five-dimensional space. As main outcome, the proposed
approach allows to provide plausible proportions of the different hydrometeors contained in a
given radar sampling volume.
[1] Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne,
A.: Hydrometeor classification through statistical clustering of polarimetric radar
measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425-4445,
doi:10.5194/amt-9-4425-2016, 2016. |
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