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