ABSTRACT
Investigating Ice in Clouds: A Multifaceted Modeling Approach
The growth of ice in clouds can affect the hydrometeor loading, the distribution of mass, surface precipitation, and at times, cloud lifetime. Cloud ice growth initiates through the deposition of water vapor, many times at the expense of liquid drops, and most times evolves into complex shapes. Beyond deposition, ice growth continues through collection, either of other ice crystals (aggregation) or liquid droplets (riming). Like deposition, the way in which collection of ice occurs is complicated by the complexities of ice crystal geometries and growth mechanisms. The complexity of ice growth has traditionally served as a computational limitation in numerical weather prediction, resulting in oversimplification of the ice growth process. However, computational innovation over the past decade have allowed for improvements in the representation of ice crystal growth. As a result, the Adaptive Habit Model (AHM) was developed to capture non-spherical ice crystal growth via vapor deposition, accurately evolving crystal aspect ratio. In addition, the AHM has been extended to allow for subsequent ice growth through aggregation, uniquely characterized using an offline Ice Particle and Aggregate Simulator (IPAS), or a `theoretical laboratory’ that generates 1000s of aggregates. Analysis of these aggregates serves to generate statistically robust characteristic values, and initial tests within the Weather Research and Forecasting Model has generated positive results, particularly through the generation of dual-polarization radar variables, a unique capability of the AHM. Finally, work is underway to assess the geometrical accuracy of the theoretical aggregates using airborne-captured cloud particle images through a machine learning approach.
