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Polarimetric radar measurements made by the recently upgraded CSU-CHILL radar system in a severe hailstorm are analyzed permitting for the first time the combined use of Z h , Z DR , linear depolarization ratio (LDR), K DP , and h to infer hydrometeor types. A chase van equipped for manual collection of hail, and instrumented with a rain gauge, intercepted the storm core for 50 min. The period of golfball-sized hail is easily distinguished by high LDR (greater than or equal to Ϫ18 dB), negative Z DR (less than or equal to Ϫ0.5 dB), and low h (less than or equal to 0.93) values near the surface. Rainfall accumulation over the entire event (about 40 mm) estimated using K DP is in excellent agreement with the rain gauge measurement. Limited dual-Doppler synthesis using the CSU-CHILL and Denver WSR-88D radars permit estimates of the horizontal convergence at altitudes less than 3 km above ground level (AGL) at 1747 and 1812 mountain daylight time (MDT). Locations of peak horizontal convergence at these times are centered on well-defined positive Z DR columns. Vertical sections of multiparameter radar data at 1812 MDT are interpreted in terms of hydrometeor type. In particular, an enhanced LDR ''cap'' area on top of the the positive Z DR column is interpreted as a region of mixed phase with large drops mixed with partially frozen and frozen hydrometeors. A positive K DP column on the the western fringe of the main updraft is inferred to be the result of drops (1-2 mm) shed by wet hailstones. Swaths of large hail at the surface (inferred from LDR signatures) and positive Z DR at 3.5 km AGL suggest that potential frozen drop embryos are favorably located for growth into large hailstones. Thin section analysis of a sample of the large hailstones shows that 30%-40% have frozen drop embryos.

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The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG); and NCAR's S-band dual-polarization Doppler radar (S-Pol).