Investigating the Evolution of Ice Particle Distributions in Mixed-Phase Clouds

Principal Investigator(s):
Kara Sulia, The Research Foundation of SUNY, University at Albany

The microphysical processes that promote precipitation contribute to the evolution of convective and non-convective clouds. More broadly, snowfall prediction is one of the largest uncertainties in short-term winter weather forecasting models and is dependent on rates of vapor growth and collection. Aggregation and riming can control much of the surface precipitation because the sizes and fall speeds of rimed and aggregate crystals largely outweigh those of monomer crystals. Much of the uncertainty surrounding snowfall prediction lies in the inability to effectively tie ice microphysics with regional-scale numerical models. The recently developed Adaptive Habit Model (AHM) evolves ice particle shape and its influence on microphysical and precipitation processes and has been shown to improve model results. The influence of monomer habit on aggregation and hence the microphysical structure of mixed-phase clouds will be investigated. Moreover, the following questions will guide the investigations: (1) What are the effects of ice particle habit on aggregation? (2) How do the growth of ice crystals and aggregates contribute to particle size distribution (PSD) evolution? (3) How do adaptive habit PSDs contribute to the distribution of mass in a system?

AHM single-crystal growth will be used with the Ice Particle and Aggregate Simulator (IPAS) to determine the physical characteristics of aggregates. These characteristics will be incorporated into an AHM aggregation scheme and tested by comparing aggregate properties from aircraft observations with a Cloud Particle Imager (CPI) for a variety of cloud types and research campaigns. The AHM-IPAS aggregation scheme will be used to modify the AHM within the Weather Research and Forecasting (WRF) model to simulate ice processes in mixed-phase systems. The microphysical details and accompanying collection processes will be analyzed via 3D simulations for both simple and complex cases. Model results will be validated using DOE field campaign aircraft microphysical and radar data.

This research will improve ice cloud prediction across scales and the understanding of microphysical processes, which are instrumental in the Earth’s energy and mass balance. This work is influential in the development of microphysical representations that do not rely on categorical thresholding. A summary from the workshop on the NOAA Parameterization of Moist Processes for Next-Generation Weather Prediction held in January 2015 states that “Pre-defined ice categories, though currently the best approach to treating ice habit, have likely outlived their usefulness. Methods for predicting ice habit are in their infancy but seem far more promising in the long run.” The methodology herein will also lead to the development of collection techniques in which realistic ice particle shape and fall speeds are considered. This work is novel in its utilization of IPAS, a state-of-the-art computational program that exploits current technological capabilities by using CPI data to virtually simulate processes that were previously limited to intensive and expensive laboratory experiments.

This work proposes to close the gap between microphysics and larger-scale processes. Processes designed for and analyses using the AHM will shed light on numerous microphysical uncertainties and provide an improved method for snowfall and precipitation forecasts. The AHM has been implemented and will be updated within WRF to include the proposed methodology. Upon completion of this work, the AHM will be made available to all WRF users as a new microphysics option.