Session Parameterization of cloud microphysics across scales


Description:

Cloud microphysics is critical for weather and climate prediction, being at the center of precipitation formation and cloud-radiative interactions. Representing cloud microphysics in  models requires statistical parameterizations of hydrometeor populations and the processes by which they evolve and interact. We invite presentations on all aspects of cloud microphysics parameterizations across different scales of modeling, with a focus on key issues for climate as clouds change in response to climate change. Topics may include the development of new parameterization methods and evaluation using observations in key regimes that are important for weather and climate. We invite topics on new data driven methods such as  machine learning and Bayesian methods for parameterization development, emulation and evaluation.


Papers:
Comparisons of Rainfall and Snowfall Microphysical Characteristics Between the southeastern Tibetan Plateau and Low-altitude Areas
Xuelong Chen, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 11, China; Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, Beijing, 11, China

Prognostic treatment of snow and graupel number concentrations in the WDM6 scheme and its impact on simulated precipitation
Kyo-Sun Sunny Lim, Kyungpook National Univ., Daegu, South korea; and J. Kwon and S. Y. Park

Calibrating Liquid-Vapor and Ice-Vapor Supersaturation Relaxation Timescales in Mixed-Phase Clouds using SOCRATES Field Campaign Data
Jordan T. Benjamin, California Institute of Technology, Pasadena, CA; and T. Schneider and A. Jaruga

The Breakdown of Paradigmatic CMIP6 Assumptions for Parameterizing Warm Rain Initiation
Kaitlyn Loftus, Columbia University, New York, NY; and R. Wordsworth

The impacts of liquid fraction and multiple free ice-phase categories on the simulation of hail
Jason A. Milbrandt, Environment and Climate Change Canada, Dorval, QC, Canada; and H. Morrison and M. Cholette

Bayesian Workflow for the Evaluation of Constraints on Depositional Ice Growth Models with Cloud Chamber Observations
Jonas Magdy Mikhaeil, Columbia University, New York, NY; and K. D. Lamb, M. van Lier-Walqui, and J. Y. Harrington

Learning Constraints on Depositional Ice Growth Models from Cloud Chamber Experiments with Physics Informed Neural Networks
Kara D. Lamb, Columbia University, New York, NY; Earth and Environmental Engineering Department, Columbia University, New York City, NY; and J. M. Mikhaeil and J. Y. Harrington

Submission Deadline: Thursday, 31 August 2023