Polarimetric radar signatures branched planar crystal aggregates

Much uncertainty still exists in how aggregates develop from pristine crystals. In particular, the presence of rapidly growing branched planar crystals has been thought to increase the efficiency of aggregate formation owing to mechanical interlocking of pristine ice crystal branches. To better understand aggregation of growing branched planar crystals, we perform Monte-Carlo simulations of aggregation by attaching individual branched planar crystal monomers. We then use these particles to simulate the polarimetric radar variables of aggregates in the dendritic growth zone. Given the uncertainty in the aggregation process itself, we generate several sets of 50 random aggregates, with each set assuming differing fall and attachment behavior during collection. We show that these assumptions result in different physical properties of the particles (e.g., maximum dimension, effective density, and aspect ratio). These randomly generated aggregates span the range of typical empirical mass-size relations for documented aggregates of planar crystals, providing some evidence that the simulated aggregates are consistent with those found in nature.

Using these simulated aggregates, we perform discrete dipole approximation (DDA) scattering computations and explore how assumed the aggregation efficiency impacts the radar signatures. As suggested in previous studies, we find that higher aggregation efficiencies are associated with more rapid decreases in differential reflectivity (ZDR) towards the ground, and find that specific differential phase (KDP) maxima that are dominated by pristine crystals. As the degree of horizontal alignment for the aggregates increases, their contribution to KDP becomes substantial, with higher-effective-density aggregates producing the largest values relative to their mass. Comparisons of the aggregate scattering calculations with dual-frequency, polarimetric radar observations within the dendritic growth zone are also assessed to better understand the validity of the varying assumptions in the aggregation model.