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

合作研究：空气湍流和雪花形态对降雪速度的影响

• 批准号: 1822192
• 负责人: Michele Guala
• 金额: $ 56.26万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822192&HistoricalAwards=false
• 关键词: Collaborative; Research; Effects; Air; Turbulence

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.

数值天气模型包含了对雨滴和雪花下落速度的内在假设。 对于雨滴，下落速度是相对较好的约束附近的终端速度。 然而，雪花可以有非常复杂的模式，使他们翻滚，旋转，并与其他雪花碰撞。 其结果是，预测模型很难预测雪花降落的速度，影响了地面积雪的预测。 该奖项将利用流体动力学社区使用的先进技术来测量真实世界和实验室中的雪花，并使用这些数据来改进数值天气模型。 这项工作的主要社会效益将是更好的天气预报的潜力，特别是对具有重大安全和经济影响的冬季天气事件。 该项目还侧重于教育和培训，计划与明尼苏达冬季狂欢节协调举办一次特别的公众宣传活动，其总体目标是预测了解雪花形态和大气湍流对降雪速度的影响。 该研究小组将通过实地活动和实验室实验相结合的方式，研究控制大气流中雪花下落速度的物理机制，并将评估这些机制对降雪预测的影响。 现场观测将在明尼苏达州南部的一个研究站进行，其中自然雪花运动将通过粒子图像测速（PIV）获得，雪花的轨迹将通过粒子跟踪测速（PTV）重建，雪花的形态将通过数字在线全息术（DIH）量化。 实验室实验将在具有256个能够产生湍流的空气射流的定制仪器中进行。 由3D打印制造的合成雪花将进入仪器，类似的PIV和PTV技术将用于捕捉它们的运动。 最后，这些数据将用于开发参数化，这些参数化将被整合到WRF的批量微物理方案中，并通过模拟和与观测结果的比较进行评估。 工作计划是为了回答以下三个主要的研究问题：1）雪花形态的哪些方面对降雪速度影响最大？ 2)环境湍流对给定形态的雪花下落速度的影响是什么？3)雪花下落速度对云系特征和预测降雪有什么影响？该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

Snow Particle Analyzer for Simultaneous Measurements of Snow Density and Morphology

用于同时测量雪密度和形态的雪颗粒分析仪

• DOI: 10.1029/2023jd038987
• 发表时间: 2023
• 期刊: Journal of Geophysical Research: Atmospheres
• 作者: Li, Jiaqi;Guala, Michele;Hong, Jiarong
• 通讯作者: Hong, Jiarong

Thin disks falling in air

• DOI: 10.1017/jfm.2023.209
• 发表时间: 2023-04
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Amy Tinklenberg;M. Guala;F. Coletti

Evidence of preferential sweeping during snow settling in atmospheric turbulence

大气湍流中积雪沉降期间优先清扫的证据

• DOI: 10.1017/jfm.2021.816
• 发表时间: 2021
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Li, Jiaqi;Abraham, Aliza;Guala, Michele;Hong, Jiarong
• 通讯作者: Hong, Jiarong

Settling and clustering of snow particles in atmospheric turbulence

大气湍流中雪颗粒的沉降和聚集

• DOI: 10.1017/jfm.2020.1153
• 发表时间: 2021
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Li, Cheng;Lim, Kaeul;Berk, Tim;Abraham, Aliza;Heisel, Michael;Guala, Michele;Coletti, Filippo;Hong, Jiarong
• 通讯作者: Hong, Jiarong