Observational Evaluation of the Effects of Atmospheric Temperature and Turbulence on Hydrometeor Fallspeed

恶劣天气事件和极端气候的预报在很大程度上依赖于对雪花下降速度的精确计算机模型表示。提供这些信息被证明是一个极具挑战性的测量问题，因为雪花很脆弱，它们蒸发得很快，它们的形状和大小根据它们形成的条件而异常变化。目前还没有天气或气候模型可以解释雪花在坠落过程中受到湍流的冲击时是如何旋转的。这在很大程度上是因为粒子在运动流体中的沉降问题仍然没有得到解决，尽管这个问题对物理和生物科学的许多领域都具有普遍的重要性。这项研究的目的是通过在犹他州瓦萨奇山脉的高海拔地区使用一套独特的仪器来提高我们对这些问题的认识，这些仪器包括在激光光片中自动跟踪单个雪花运动的能力，量化它们直接环境中的空气湍流的大小，随后测量它们的质量、大小、形状和密度。这项为期三年的研究预计的结果包括修订了雪花降落速度、质量和密度与气温关系的公式，以及风暴中湍流增强或阻碍降水率的程度。气象仪器的预期改进将通过犹他大学的一家衍生公司协助商业化，以扩大天气测量、基础设施恢复能力和交通安全部门的可用性。公众外展包括通过公民科学分类数据和向雪上运动爱好者传播数据。雪花在温暖的空气中密度更大，它们对湍流表现出高度非线性的反应，湍流对它们的平均沉降速度的影响仍有待确定。这项研究的目的是对决定降水粒子下降速度的两个主要大气过程——温度和湍流——有更深入的了解。犹他大学在美国国家科学基金会（National Science Foundation）的支持下，开发了两种新仪器，首次实现了对单个水流星质量和密度的直接、自动化测量，从而独特地解决了这一问题。这些设备将与温度、风和湍流传感器以及激光光片和雾机一起部署在多雪的高海拔地区。粒子成像测速仪将用于跟踪雪花和周围湍流空气的运动。预期的项目成果将是订正的参数化，描述大气温度与水流星大小、质量和密度之间的关系，以及大气湍流如何影响单个水流星质量通量和集体降水率的量化指标。就其更广泛的影响而言，该研究将利用机器学习和公民科学的结合来促进水流星分类。此外，仪器的预期改进将通过犹他大学成立的附属公司的商业化提供给更广泛的科学和社会复原力社区。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。

Forecasts of severe weather events and climate extremes depend heavily upon accurate computer model representations of how fast snowflakes fall. Providing this information has proved an immensely challenging measurement problem because snowflakes are delicate, they evaporate quickly, and their shapes and sizes are exceptionally varied depending on the conditions in which they formed. No weather or climate model currently accounts for how snowflakes swirl as they are buffeted by turbulence during their fall. This is largely because the settling of particles in a moving fluid remains unsolved, despite that fact that the problem has very general importance to a wide range of fields in the physical and biological sciences. The study aims to advance our knowledge of these problems by using a unique suite of instruments deployed to a field site at a high-elevation location in the Wasatch mountain range of Utah, that includes the capacity to automatically track individual snowflake motions in a laser light sheet, quantify the magnitude of air turbulence in their direct environment, and subsequently measure their mass, size, shape, and density. The projected outcomes of this three-year study include revised formulations for the relationship of snowflake fall speed, mass, and density to air temperature, and of the extent to which precipitation rates are enhanced or retarded by turbulence in storms. Anticipated improvements to weather instrumentation will assist commercialization through a University of Utah spin-off company for wider availability to the weather measurement, infrastructure resilience, and transportation safety sectors. Public outreach includes data classification through citizen science and data dissemination to snow-sports enthusiasts.Snowflakes are denser in warmer air, and they display a highly non-linear response to turbulence whose impact on their average settling speed remains to be determined. This study aims to develop a more sophisticated understanding of two of the principle atmospheric processes determining how fast precipitation particles fall, temperature and turbulence. The University of Utah is uniquely poised to address this problem with its development of two new instruments with prior National Science Foundation’s support that permit the first direct, automated, measurements of individual hydrometeor mass and density. These devices will be deployed to a snowy high elevation field site alongside temperature, wind, and turbulence sensors, as well as a laser light sheet and fog machine. Particle Imaging Velocimetry will be used to track the motions of snowflakes and surrounding turbulent air. The expected project outcome will be revised parameterizations describing the relationship between atmospheric temperature and hydrometeor size, mass and density, as well as quantified metrics for how atmospheric turbulence affects individual hydrometeor mass flux and collective precipitation rate. In terms of its broader impacts, the study will exploit a combination of machine-learning and citizen science to facilitate hydrometeor classification. Additionally, anticipated improvements to instruments will be made available to the wider scientific and societal resilience communities through commercialization by an established University of Utah spin-off company.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.