Articles | Volume 14, issue 2
https://doi.org/10.5194/amt-14-1075-2021
https://doi.org/10.5194/amt-14-1075-2021
Research article
 | 
10 Feb 2021
Research article |  | 10 Feb 2021

Hydrometeor classification of quasi-vertical profiles of polarimetric radar measurements using a top-down iterative hierarchical clustering method

Maryna Lukach, David Dufton, Jonathan Crosier, Joshua M. Hampton, Lindsay Bennett, and Ryan R. Neely III

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Latest update: 01 Nov 2024
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Short summary
This paper presents a novel technique of data-driven hydrometeor classification (HC) from quasi-vertical profiles, where the hydrometeor types are identified from an optimal number of hierarchical clusters, obtained recursively. This data-driven HC approach is capable of providing an optimal number of classes from dual-polarimetric weather radar observations. The embedded flexibility in the extent of granularity is the main advantage of this technique.