Collaborative Research: Effects of Air Turbulence and Snowflake Morphology on Snow Fall Speed

GSTDTAP

项目编号	1822192
	Collaborative Research: Effects of Air Turbulence and Snowflake Morphology on Snow Fall Speed
	Michele Guala
主持机构	University of Minnesota-Twin Cities
项目开始年	2018
	2018-08-01
项目结束日期	2021-07-31
资助机构	US-NSF
项目类别	Continuing grant
项目经费	190801(USD)
国家	美国
语种	英语
英文摘要	Numerical weather models contain built-in assumptions for the fall speed of raindrops and snowflakes. For raindrops, the fall speed is relatively well-constrained near terminal velocity. However, snowflakes can have very complex patterns allowing them to tumble, spin, and collide with other flakes. The result is that forecast models have a difficult time predicting snowflake fall speed, affecting the projection of accumulations on the ground. This award will make use of advanced techniques used in the fluid dynamics community to measure snowflakes in real-world settings and in the laboratory, and use this data to improve numerical weather models. The main societal benefit of the work will be the potential for better weather forecasts, especially for winter weather events that have significant safety and economic impacts. The project also focuses on education and training, and a special public outreach event is planned in coordination with the Minnesota Winter Carnival. The overarching goal of this project is to develop a predictive understanding of the effects of snowflake morphology and atmospheric turbulence on the fall speed of snow. The research team will study the physical mechanisms controlling snowflake fall speed in atmospheric flows with a combination of field campaigns and laboratory experiments, and will evaluate the impact of such mechanisms on snowfall predictions. Field observations will take place at a research station in southern Minnesota, where natural snowflake motion will be obtained by Particle Image Velocimetry (PIV), trajectory of snowflakes will be reconstructed by Particle Tracking Velocimetry (PTV), and the morphology of snowflakes will be quantified by Digital In-line Holography (DIH). Laboratory experiments will be conducted in a custom instrument with 256 air jets that are able to generate turbulent flow. Synthetic snowflakes, manufactured by 3D printing, will enter the instrument and similar PIV and PTV techniques would be used to capture their motion. Finally, the data will be used to develop parameterizations which will be integrated into a bulk microphysics scheme in WRF and assessed through simulations and comparisons to observations. The work plan is derived to answer the following three main research questions: 1) Which aspects of the snowflake morphology are most influential for the snow fall speed? 2) What is the effect of ambient turbulence on the fall speed of a snowflake of given morphology? 3) What is the effect of snowflake fall speed on cloud system characteristics and predicted snowfall? This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/72959
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Michele Guala.Collaborative Research: Effects of Air Turbulence and Snowflake Morphology on Snow Fall Speed.2018.