Effects of Turbulence on Snowfall: Application of Laboratory Measurements in a Cloud Microphysics Model
Abstract
A predictive understanding of snow settling is necessary for reliably forecasting snowfall. Laboratory and field measurements indicate that air turbulence can significantly enhance the settling velocity of inertial particles in general, and snowflakes in particular. Laboratory data obtained in a zero-mean flow turbulence chamber are used to generate a parameterization of this process, which is incorporated into the Predicted Particle Properties (P3) cloud microphysics scheme. Simulations are run with and without the influence of turbulence on the settling of snow crystals for idealized orographic snowfall. Contrary to expectations, the average precipitation remains largely unchanged, although local differences in snowfall are significant owing to shifts in the snowfall spatial distribution. These differences in snow accumulation are related to the effect of turbulence-enhanced fall speeds on the microphysical characteristics of snow and ice in the atmosphere. We extend our understanding of these processes from idealized simulations to a real case study of a wintertime mid-latitude cyclone.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A44D..05L
- Keywords:
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- 3310 Clouds and cloud feedbacks;
- ATMOSPHERIC PROCESSES;
- 3311 Clouds and aerosols;
- ATMOSPHERIC PROCESSES;
- 3333 Model calibration;
- ATMOSPHERIC PROCESSES;
- 3354 Precipitation;
- ATMOSPHERIC PROCESSES