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

数值天气模式包含雨滴和雪花下落速度的内置假设。对于雨滴来说，下落速度在接近终端速度时受到相对较好的约束。然而，雪花可以有非常复杂的图案，使它们能够翻滚、旋转和与其他雪花碰撞。结果是，预测模型很难预测雪花的下降速度，从而影响了地面上积雪的预测。该奖项将利用流体动力学领域的先进技术，在现实世界和实验室环境中测量雪花，并利用这些数据改进数值天气模型。这项工作的主要社会效益将是更好的天气预报，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，并计划与明尼苏达州冬季狂欢节协调举办特别的公共宣传活动。该项目的总体目标是对雪花形态和大气湍流对雪下降速度的影响进行预测。课题组将通过野外活动和实验室实验相结合的方式，研究大气流动中控制雪花降落速度的物理机制，并评估这些机制对降雪预测的影响。野外观测将在美国明尼苏达州南部的一个研究站进行，利用粒子图像测速仪（PIV）获得雪花的自然运动，利用粒子跟踪测速仪（PTV）重建雪花的运动轨迹，利用数字直线全息（DIH）对雪花的形态进行量化。实验室实验将在一个定制的仪器中进行，该仪器有256个能够产生湍流的空气射流。由3D打印制造的合成雪花将进入仪器，类似的PIV和PTV技术将被用来捕捉它们的运动。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的整体微物理方案中，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研究问题：1)雪花形态的哪些方面对降雪速度的影响最大？2)环境湍流对给定形态雪花下落速度的影响是什么？3)雪花降落速度对云系特征和预测降雪量的影响是什么？该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.