Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 2
Articles | Volume 14, issue 2
https://doi.org/10.5194/amt-14-1075-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-1075-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 2
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

Data sets

FAAM C013 Instrument Test flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft FAAM (Facility for Airborne Atmospheric Measurements), NERC (Natural Environment Research Council), and Met Office http://catalogue.ceda.ac.uk/uuid/b1c81845ad294f8399f9b564da59c68c

FAAM C076 PICASSO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft FAAM, NERC, and Met Office http://catalogue.ceda.ac.uk/uuid/dbbf8c12f3ad48309829735f7f2e6359

FAAM C081 PICASSO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft FAAM, NERC, and Met Office http://catalogue.ceda.ac.uk/uuid/64c9279112bb4e0cadb6adaebf1141eb

FAAM C082 PICASSO flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft FAAM, NERC, and Met Office http://catalogue.ceda.ac.uk/uuid/25b2a346c6f24032ba746b9dc852ff75

NWP-UKV: Met Office UK Atmospheric High Resolution Model data Met Office https://catalogue.ceda.ac.uk/uuid/f47bc62786394626b665e23b658d385f

NCAS mobile X-band radar scan data from 1st November 2016 to 4th June 2018 deployed on long-term observations at the Chilbolton Facility for Atmospheric and Radio Research (CFARR), Hampshire, UK L. Bennett https://doi.org/10.5285/ffc9ed384aea471dab35901cf62f70be

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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.
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