This paper describes the Spectral Ice Habit Prediction System (SHIPS), which represents a continuous-property approach to microphysics simulation in an Eulerian cloud-resolving model (CRM). A two-moment hybrid-bin method is adopted to predict the solid hydrometeor distribution, where the distribution is divided into the mass bins with a simple mass distribution inside each bin. Each bin is characterized by a single representative ice crystal habit and the type of solid hydrometeor. These characteristics are diagnosed based on a series of particle property variables (PPVs) of solid hydrometeors that reflect the history of microphysical processes and the mixing between bins and air parcels in space. Thus, SHIPS allows solid hydrometeors to evolve characteristics and size distribution based on their movement through a cloud. SHIPS was installed into the University of Wisconsin-Nonhydrostatic Modeling System (UW-NMS) and tested for ice nucleation and vapor deposition processes. Two-dimensional idealized simulations were employed to simulate a winter orographic storm observed during the second Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) campaign. The simulated vertical distributions of ice crystal habits showed that the dynamic advection of dendrites produces wider dendritic growth region than local atmospheric conditions suggest. SHIPS showed the sensitivities of the habit distribution in the low- and midlevel to the upper-level growth mode (T < -20°C) of ice crystals through the sedimentation. Comparison of the results to aircraft observations casts doubt on the role of the columnar growth mode (T < -20°C) traditionally thought to be dominant in the literature. The results demonstrated how the complexity of the vapor deposition growth of ice crystals, including dendrites and capped columns, in varying temperature and moisture lead to particular observed habits.
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