Investigating aggregate properties using a multi-faceted modeling approach

Description

A novel ice particle and aggregate simulator (IPAS) is employed as a statistical tool to simulate theoretical aggregates. Both monomer and aggregate physical characteristics such as aspect ratio and axis lengths can be determined and evolved offline using the simulator to reduce computational expense in bulk ice microphysical models. To emulate laboratory data, IPAS acts as a “theoretical laboratory” and provides likely aggregate properties given an input monomer shape and any number of monomers. The expected falling orientations, overlap of each monomer and any contact angle that may form through so-called constrained randomization is recorded. Initial results and detailed discussion of ice-ice aggregation in IPAS appear in Przybylo et al (2019, in review). Further simulations of multi-monomer aggregates reveal relatively asymptotic behavior of most aggregate properties. Using IPAS-generated aggregate parameters, a new aggregation scheme (ice-ice only) has been implemented into the Adaptive Habit Model (AHM) in WRF; initial simulations yield the development of relatively low snow mass quantities, as to be expected from ice-ice only aggregation. Multi-monomer aggregate properties implemented in analogous fashion and initial idealized simulations to investigate impact on snow mass are forthcoming. Lastly, there is a fundamental need to validate the simulated theoretical aggregates in IPAS with observations in a quantitative sense for a multitude of environments and parameters (i.e., aspect ratio, major axis dimensions, complexity, etc). The development of a machine learning algorithm to accomplish this is underway, where first round attempts to accurately categorize particle types using CPI imagery yield promising results.