Snowflake dynamics: in the lab, in the atmosphere, in models

雪花动力学：在实验室、在大气中、在模型中

• 批准号: 2600412
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2600412
• 关键词: Snowflake; dynamics; lab; atmosphere; models

Snowfall is an important but poorly quantified component of the earth's hydrological cycle. Meanwhile, the rate at which snow falls out of high-altitude clouds is considered highly uncertain, yet in model experiments has shown to be a critical parameter controlling the earth's radiation budget in climate simulations.One of the most important ways in which we can observe these processes, and test numerical simulations of them, is using remote sensors, such as radar. These techniques are rapidly advancing, utilitising multiple wavelengths to probe different parts of the size distribution, multiple polarisations to diagnose the types and orientations of the snowflakes, and Doppler information to diagnose the distribution of particle fall speeds and hence the distribution of particle sizes in the cloud. Exploitation of these techniques relies on a detailed understanding of the dynamics of falling snowflakes - how fast they fall, the orientation they adopt, and their trajectories (which may be unsteady, e.g. fluttering, tumbling). Unfortunately, our understanding of this is currently very limited. This PhD will involve using new experiments, observations, data analysis and numerical calculations to resolve these issues. In the laboratory, the student will use 3D-printed and micro-machined snowflake "analogues" to develop our fundamental understanding of how ice particles fall, and what properties control this, observing the fall motion using multi-view cameras and particle tracking codes to reconstruct the trajectories and orientations of the particles. The student will then apply this new knowledge and understanding to interpreting data from the atmosphere, exploiting data from a recent field project ("PICASSO") at the Chilbolton Observatory comprising state of the art remote-sensing measurements, in which data on the shapes, sizes, fall speeds and orientations of the particles are encoded, along with complementary in-situ measurements of particles from the FAAM research aircraft. By connecting the results from the laboratory and numerical methods to model the corresponding radar Doppler spectra and polarimetric observations, we will gain a deeper understanding of how to interpret such data, and the opportunity to improve retrievals of ice properties (such as those planned for the EarthCARE satellite mission). Finally, working in collaboration with the larger PICASSO project team, the student will be able to apply the new understanding from these results to the improvement of how microphysical processes should be represented in models.

降雪是地球水文循环中一个重要但数量很少的组成部分。与此同时，高空云层中降雪福尔斯的速率被认为是高度不确定的，但在模型实验中，它被证明是控制气候模拟中地球辐射收支的关键参数。我们可以观察这些过程并对其进行数值模拟测试的最重要方法之一是使用雷达等遥感器。这些技术正在迅速发展，利用多个波长来探测不同部分的大小分布，利用多个偏振来诊断雪花的类型和方向，利用多普勒信息来诊断粒子下落速度的分布，从而诊断云中粒子大小的分布。这些技术的开发依赖于对雪花下落动力学的详细理解-它们下落的速度，它们采用的方向以及它们的轨迹（可能是不稳定的，例如飘动，翻滚）。不幸的是，我们目前对此的了解非常有限。这个博士将涉及使用新的实验，观察，数据分析和数值计算来解决这些问题。在实验室中，学生将使用3D打印和微加工的雪花“类似物”来发展我们对冰颗粒如何下落的基本理解，以及什么属性控制这一点，使用多视图相机和粒子跟踪代码观察下落运动，以重建粒子的轨迹和方向。然后，学生将运用这一新的知识和理解来解释来自大气层的数据，利用最近在奇尔博尔顿天文台进行的一个实地项目（“PICASSO”）的数据，该项目包括最先进的遥感测量，其中对粒子的形状、大小、下落速度和方向的数据进行编码，沿着来自FAAM研究飞机的粒子的补充现场测量。通过将实验室和数值方法的结果连接起来，对相应的雷达多普勒光谱和偏振观测进行建模，我们将更深入地了解如何解释这些数据，并有机会改善冰特性的检索（例如计划用于EarthCARE卫星使命的检索）。最后，与更大的PICASSO项目团队合作，学生将能够将这些结果的新理解应用于如何在模型中表示微物理过程的改进。