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

大气温度和湍流对水凝物下降速度影响的观测评估

• 批准号: 2210179
• 负责人: Timothy Garrett
• 金额: $ 74.7万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210179&HistoricalAwards=false
• 关键词: Observational; Evaluation; Effects; Atmospheric; Temperature

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.

对恶劣天气事件和极端气候的预测在很大程度上依赖于精确的计算机模型，以反映雪花飘落的速度。提供这些信息已被证明是一个非常具有挑战性的测量问题，因为雪花是脆弱的，它们蒸发得很快，并且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能够解释雪花在下落过程中受到湍流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仍然没有解决，尽管这个问题对物理和生物科学的广泛领域具有非常普遍的重要性。该研究旨在通过使用部署在犹他州瓦萨奇山脉高海拔位置的现场的一套独特的仪器来提高我们对这些问题的认识，该仪器包括在激光片中自动跟踪单个雪花运动的能力，量化其直接环境中空气湍流的大小，并随后测量其质量，大小，形状和密度。这项为期三年的研究的预计成果包括修改公式的雪花下降速度，质量和密度与空气温度的关系，并在多大程度上提高或推迟风暴中的湍流降水率。预计天气仪器的改进将有助于通过犹他州大学的一家分拆公司实现商业化，以便更广泛地应用于天气测量、基础设施恢复力和运输安全部门。公众宣传包括通过公民科学进行数据分类，并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，它们对湍流的响应呈现高度非线性，湍流对其平均沉降速度的影响仍有待确定。这项研究的目的是发展一个更复杂的理解两个原则的大气过程，确定如何快速降水粒子下降，温度和湍流。犹他州大学在国家科学基金会的支持下开发了两种新仪器，首次直接自动测量单个水凝物的质量和密度，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片和雾机一起部署在多雪的高海拔现场。粒子成像测速仪将用于跟踪雪花和周围湍流空气的运动。项目的预期成果将是修订描述大气温度与水凝物大小、质量和密度之间关系的参数，以及大气湍流如何影响单个水凝物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凝物分类。此外，通过犹他州大学的一家附属公司的商业化，将为更广泛的科学和社会复原力社区提供对仪器的预期改进。该奖项反映了NSF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。