Monte Carlo Lagrangian modeling of ice microphysical process in wintertime clouds
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Abstract
This study explores the intricate processes governing ice microphysics, particularly the evolution of ice habits and their consequential impact on cloud microphysics and precipitation patterns.
Leveraging the Lagrangian Monte-Carlo ice microphysics model McSnow, several methodological advancements are introduced to refine the representation of ice habits.
McSnow is extended by an explicit habit prediction scheme, combined with the hydrodynamic theory of Böhm.
Böhm's original cylindrical shape assumption for prolates is compared against recent lab results, showing that interpolation between cylinder and prolate yields the best agreement.
For constant temperature and supersaturation, the predicted mass, size, and density agree well with the laboratory results, and a comparison with real clouds using the polarizability ratio shows regimes capable of improvement.
An updated form of the inherent growth function to describe the primary habit growth tendencies is proposed and combined with a habit-dependent ventilation coefficient.
The modifications contrast the results from general mass size relations and significantly impact the main ice microphysical processes. Depending on the thermodynamic regime, ice habits significantly alter depositional growth and affect aggregation and riming.
The findings underscore the critical importance of explicit ice habit prediction in accurately capturing the variability inherent in ice microphysics.
Through comprehensive simulations, the research demonstrates that these detailed representations are essential for modeling the complex feedback mechanisms that occur between microphysical processes and large-scale cloud dynamics.
This is especially pertinent in mixed-phase clouds, where the interaction of liquid and ice phases exerts a substantial influence on cloud longevity, precipitation efficiency, and radiative properties.
A key aspect of the research involves synthesizing model simulations with observational data to enhance the realism of the simulations.
By incorporating polarimetric radar observations, the study evaluates microphysical processes within more realistic atmospheric scenarios.
Although constrained to one-dimensional simulations, these efforts yield significant insights into the feedback loops between cloud microphysics and cloud dynamics, with a particular focus on mixed-phase cloud conditions.
The study thereby highlights the necessity of observational data in both validating and refining model outputs.
In addition to the advancements in microphysical modeling, the research addresses the phenomenon of SIP, with particular emphasis on ice-ice collisions and fragmentation processes.
A revised fragmentation model is proposed, which incorporates an approach for the fragmentation potential based on fragility of particles.
This development is essential for elucidating the rapid increases in ice particle concentrations observed in certain cloud regimes, which hold profound implications for cloud evolution and precipitation development.
The implications of these findings are far-reaching, particularly in the context of atmospheric modeling, where they contribute to enhancing the accuracy of weather forecasting and climate models. The study concludes by identifying several avenues for future research, including the need for further laboratory experiments aimed at refining our understanding of ice habits and their role in cloud microphysical processes.
Higher-dimensional simulations, coupled with the integration of advanced observational techniques, can serve as a crucial step forward in advancing the field of cloud microphysics.
Ultimately, this research contributes to the ongoing efforts to improve the fidelity of atmospheric models, thereby supporting the broader objectives of advancing weather prediction, cloud process understanding, and climate modeling.