Abstract
Abstract. Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.
Highlights
Radar-based hydrometeor classification, that is the proper identification of different types of hydrometeors from radar observations, is important for an improved understanding of atmospheric dynamics, an improved quantitative precipitation estimation (QPE), an improved verification and assimilation in numerical weather prediction models and operational nowcasting applications like aircraft or road safety (Bringi et al, 2007)
We propose a novel semi-supervised method for hydrometeor classification from polarimetric radar data
The idea is to combine the main advantages of both supervised and unsupervised approaches, while keeping the potential operational implementation reasonably simple. This is achieved through the statistical clustering of representative observations of the considered polarimetric radar
Summary
Radar-based hydrometeor classification, that is the proper identification of different types of hydrometeors from radar observations, is important for an improved understanding of atmospheric dynamics, an improved quantitative precipitation estimation (QPE), an improved verification and assimilation in numerical weather prediction models and operational nowcasting applications like aircraft or road safety (Bringi et al, 2007). The proposed semi-supervised algorithm mainly relies on two statistical tools, elaborated in the following subsections: the unsupervised k-medoids clustering and the KS statistical test. These two methods serve as a sort of link between the polarimetric radar measurements and the hydrometeor scattering hypotheses. As it would be the case with the k-means (Lloyd., 1982), the employed k-medoids algorithm (Kaufman and Rousseeuw, 2009) is used to partition the multivariate observation vectors As it would be the case with the k-means (Lloyd., 1982), the employed k-medoids algorithm (Kaufman and Rousseeuw, 2009) is used to partition the multivariate observation vectors (x1, x2, . . .xn) into k subsets or clusters (S1, S2, . . .Sk) in such a way that the subsets minimise D, the sum of distances between the observations and the centroids of subset μi: k D= di (x − μi ). (1) i=1 x∈Si
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