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

Related authors

A guide to optimised spatiotemporal data co-location by mutual information maximisation
Andrew Steven Martin, Heather Guy, Michael Ray Gallagher, and Ryan Reynolds Neely III
EGUsphere, https://doi.org/10.5194/egusphere-2025-6079,https://doi.org/10.5194/egusphere-2025-6079, 2025
Short summary
Measurement report: In situ vertical profiles of below-cloud aerosol over the central Greenland Ice Sheet
Heather Guy, Andrew S. Martin, Erik Olson, Ian M. Brooks, and Ryan R. Neely III
Atmos. Chem. Phys., 24, 11103–11114, https://doi.org/10.5194/acp-24-11103-2024,https://doi.org/10.5194/acp-24-11103-2024, 2024
Short summary
Deep Convective Microphysics Experiment (DCMEX) coordinated aircraft and ground observations: microphysics, aerosol, and dynamics during cumulonimbus development
Declan L. Finney, Alan M. Blyth, Martin Gallagher, Huihui Wu, Graeme J. Nott, Michael I. Biggerstaff, Richard G. Sonnenfeld, Martin Daily, Dan Walker, David Dufton, Keith Bower, Steven Böing, Thomas Choularton, Jonathan Crosier, James Groves, Paul R. Field, Hugh Coe, Benjamin J. Murray, Gary Lloyd, Nicholas A. Marsden, Michael Flynn, Kezhen Hu, Navaneeth M. Thamban, Paul I. Williams, Paul J. Connolly, James B. McQuaid, Joseph Robinson, Zhiqiang Cui, Ralph R. Burton, Gordon Carrie, Robert Moore, Steven J. Abel, Dave Tiddeman, and Graydon Aulich
Earth Syst. Sci. Data, 16, 2141–2163, https://doi.org/10.5194/essd-16-2141-2024,https://doi.org/10.5194/essd-16-2141-2024, 2024
Short summary
Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System
Gillian Young McCusker, Jutta Vüllers, Peggy Achtert, Paul Field, Jonathan J. Day, Richard Forbes, Ruth Price, Ewan O'Connor, Michael Tjernström, John Prytherch, Ryan Neely III, and Ian M. Brooks
Atmos. Chem. Phys., 23, 4819–4847, https://doi.org/10.5194/acp-23-4819-2023,https://doi.org/10.5194/acp-23-4819-2023, 2023
Short summary
Passive ground-based remote sensing of radiation fog
Heather Guy, David D. Turner, Von P. Walden, Ian M. Brooks, and Ryan R. Neely
Atmos. Meas. Tech., 15, 5095–5115, https://doi.org/10.5194/amt-15-5095-2022,https://doi.org/10.5194/amt-15-5095-2022, 2022
Short summary
More articles (2)

Cited articles

Abdi, H. and Williams, L. J.: Principal component analysis, WIREs Comp. Stat., 2, 433–459, 2010. 
Al-Sakka, H., Boumahmoud, A. A., Fradon, B., Frasier, S. J., and Tabary, P.: A new fuzzy logic hydrometeor classification scheme applied to the French X-, C-, and S-band polarimetric radars, J. Appl. Meteorol. Clim., 52, 2328–2344, 2013. 
Aydin, K., Seliga, T. A., and Balaji, V.: Remote sensing of hail with a dual linear polarization radar, J. Appl. Meteorol., 25, 1475–1484, 1986. 
Baldini, L. and Gorgucci, E.: Identification of the Melting Layer through Dual-Polarization Radar Measurements at Vertical Incidence, J. Atmos. Ocean. Tech., 23, 829–839, https://doi.org/10.1175/JTECH1884.1, 2006. 
Baumgardner, D., Jonsson, H., Dawson, W., O'Connor, D., and Newton, R.: The cloud, aerosol and precipitation spectrometer: a new instrument for cloud investigations, Atmos. Res., 59–60, 251–264, https://doi.org/10.1016/S0169-8095(01)00119-3, 2001. 
More articles (55)
Download
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.
Read more
Share