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,...
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 III1,2Maryna Lukach et al.Maryna Lukach1,2,David Dufton1,2,Jonathan Crosier3,4,Joshua M. Hampton1,2,Lindsay Bennett1,2,and Ryan R. Neely III1,2
Received: 10 Apr 2020 – Accepted for review: 05 May 2020 – Discussion started: 20 May 2020
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.
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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.
This paper presents a novel technique of data-driven hydrometeor classification (HC) from QVPs,...