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

降雪是地球水文循环中一个重要但缺乏量化的组成部分。同时，高海拔云层的降雪率被认为是高度不确定的，但在模型实验中，已显示出在气候模拟中控制地球辐射平衡的关键参数。我们可以观察这些过程并测试它们的数值模拟的最重要的方法之一是使用远程传感器，如雷达。这些技术正在迅速发展，利用多个波长来探测粒子尺寸分布的不同部分，利用多重偏振来诊断雪花的类型和方向，以及利用多普勒信息来诊断粒子下落速度的分布，从而诊断云中粒子尺寸的分布。这些技术的开发依赖于对下落雪花的动力学的详细了解--它们落得有多快，它们所采用的方向，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。本博士学位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“模拟”来发展我们对冰粒如何下落以及什么特性控制这一过程的基本理解，使用多视角摄像机和粒子跟踪代码来观察冰粒的下落运动，以重建粒子的轨迹和方向。然后，学生将把新的知识和理解应用于解释来自大气的数据，利用奇尔博顿天文台最近的一个实地项目(“毕加索”)的数据，该项目包括最先进的遥感测量，其中关于粒子的形状、大小、下落速度和取向的数据被编码，以及来自FAAM研究飞机的补充的现场粒子测量。通过将实验室的结果与数值方法相结合，对相应的雷达多普勒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，并有机会改进对冰特性的反演(如为EarthCARE卫星飞行任务计划的那些)。最后，与更大的毕加索项目团队合作，学生将能够将来自这些结果的新理解应用于改进微物理过程应该如何在模型中表示。

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.