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

398399400401402403404405406407408409410411412413414415416417418419domain but the ML is “painted” into the classification for reference, as in Fig. 5b and 5c. Ifcomplete melting occurs, the above and below ML HCAs are stitched together above and belowthe ML median height and the melting layer is also included. The latter iteration is chosen forFig. 5d. When K dp is noisy above and within the melting layer, it is useful to restrict plates anddendrites above 0.5-0.7 km of the ML top and keep wet snow pixels below this level. Thistechnique was used in Fig. 5.b) Membership beta functionsThe categories in each HCA step are assigned a score depending on the radar variables’ fitinto that category’s MBF, which is defined by its center value (m), half width (a), and slope (b).Each radar variable in an MBF is assigned a weight (0-100%) in calculating the score. Thehydrometeor category with the highest score is determined to be the dominant bulk hydrometeortype in a particular radar gate. The expected value ranges in Fig. 1 and Table 2 were modified,usually by trial and error, to produce the MBFs in Figures 6-8 and Table 5 as follows:1. Implement slope (b) parameters to gradually widen the MBFs but preserve the preciseboundaries between categories predicted by scattering simulations.2. Increase maximum aggregate K dp values incrementally for decreasing & and slightly extendthe minimum K dp allowed for dendrites to reduce dendrite over-classification.3. Increase maximum Z dr for aggregates to 1 dB (regarded as acceptable for classificationpurposes considering noise, uncertainty, and the varying degree of aggregation by Bader etal. 1987, Illingsworth et al. 1987, and Straka et al. 2000).4. Decrease minimum Z h for dendrites and aggregates to -1 dBZ to account for not modelingspherical or extremely small particles for either category.19