1 A dual-polarization radar hydrometeor classification algorithm for ...

4204215. Increase maximum Z dr for plates and maximum K dp for dendrites based on radar observationsto account for larger diameter crystals than could be parameterized in our model.422423424425426427428429430431432433434435436437438439440441442The wet snow MBF is substantially wide based on Knight (1979), Fujiyoshi (1986), Barthazyet al. (1988), Zrnic et al. (1993), Vivekanandan et al. (1993), Ryzhkov and Zrnic (1998a), Strakaet al. (2000), and Brandes and Ikeda (2004) as well as to account for noise, non-Rayleighscattering, and differential attenuation beyond the ML. We implemented overlapping ! hv MBFsbetween wet snow (0.6-0.95, Illingsworth and Caylor 1989, Ryzhkov and Zrnic 1998a, Straka etal. 2000) and “other” hydrometeors (0.90-1.0) based on trial and error efforts to reducemisclassification of noise or dendrites as wet snow, especially within/around the ML wherebright band signatures are not exactly collocated (Brandes and Ikeda 2004).c) Variable weighting systemAlgorithm performance was very sensitive to small (5%) changes in the weighting systemand those listed here gave the most physically-realistic results. We ensured that the algorithmcould identify the radar bright band, dendritic growth zone, aggregates, etc. on a case-by-casebasis according to examples in previous studies. It is significant that the weighting system inTable 5 works well on four different radar platforms at X-, C-, and S-band, as demonstrated inSection 5.The weights allow the algorithm to capitalize on strengths of specific radar variables indifferentiating between certain hydrometeors. For example, ! hv is heavily weighted for wet snowidentification, but not 100% because the ! hv ML signature is quite narrow and increasedweighting > 56% resulted in misclassifications. Z dr was weighted second highest for MLdetection because it was more helpful than Z H . Figure 4 shows an example of how the Z h bright20