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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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This paper presents a novel technique of data-driven hydrometeor classification (HC) from QVPs, 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 the dual-polarimetric weather radar observations and the embedded flexibility in the extent of granularity is the main advantage of this technique.
Preprints
https://doi.org/10.5194/amt-2020-143
https://doi.org/10.5194/amt-2020-143

  20 May 2020

20 May 2020

Review status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

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

Maryna Lukach1,2, David Dufton1,2, Jonathan Crosier3,4, Joshua M. Hampton1,2, Lindsay Bennett1,2, and Ryan R. Neely III1,2 Maryna Lukach et al.
  • 1National Centre for Atmospheric Sciences, Leeds, United Kingdom
  • 2School of Earth and Environment, University of Leeds, Leeds, United Kingdom
  • 3National Centre for Atmospheric Sciences, University of Manchester, United Kingdom
  • 4Department of Earth and Environmental Sciences, University of Manchester, Manchester, United Kingdom

Abstract. Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from Quasi-Vertical Profiles (QVP). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the NCAS X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.

Maryna Lukach et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Maryna Lukach et al.

Maryna Lukach et al.

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Short summary
This paper presents a novel technique of data-driven hydrometeor classification (HC) from QVPs, 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 the dual-polarimetric weather radar observations and the embedded flexibility in the extent of granularity is the main advantage of this technique.
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