Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.400 IF 3.400
  • IF 5-year value: 3.841 IF 5-year
    3.841
  • CiteScore value: 3.71 CiteScore
    3.71
  • SNIP value: 1.472 SNIP 1.472
  • IPP value: 3.57 IPP 3.57
  • SJR value: 1.770 SJR 1.770
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 70 Scimago H
    index 70
  • h5-index value: 49 h5-index 49
Preprints
https://doi.org/10.5194/amt-2020-143
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-143
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 20 May 2020

Submitted as: research article | 20 May 2020

Review status
This preprint is currently under review for the journal AMT.

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.

Interactive discussion

Status: open (until 15 Jul 2020)
Status: open (until 15 Jul 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Maryna Lukach et al.

Maryna Lukach et al.

Metrics will be available soon.
Latest update: 03 Jun 2020
Publications Copernicus
Download
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.
This paper presents a novel technique of data-driven hydrometeor classification (HC) from QVPs,...
Citation