USING 3D-PRINTED ANALOGUES TO UNDERSTAND THE AERODYNAMICS OF COMPLEX ICE PARTICLES

Many of the clouds in the atmosphere contain ice particles. These ice particles play an important role in the climate system, because high-altitude cirrus clouds cover around 30% of the globe at any one time, and act to warm the planet. Ice particles are also important for the development of precipitation, and not only in cold polar climates: even in mid-latitudes (like the UK), over three quarters of the precipitation that falls originates as snowflakes aloft - it is just that most of it melts before arriving at the surface.

Ice particles, both in clouds and in snowfall at the surface, precipitate. In other words, they are able to grow large enough to fall through the air. This has several implications. The most obvious of these is that the rate at which the particles fall out controls the transport of water vertically through the atmosphere and to the surface. More subtly, the movement of each ice particle through the air directly influences the rate at which the particle grows, evaporates and melts. For example, if the air is humid enough, water molecules will diffuse to the ice crystal's surface and deposit there, leading to growth. If the particle is stationary, this growth occurs steadily but slowly, because the growing ice crystal depletes the vapour around it, leading to a shallow gradient in the concentration of molecules. If the particle is falling, this growth can occur much faster, because the ice crystal is constantly falling into fresh, humid air, leading to steep concentration gradients.

The quantitative details of exactly how fast an ice particle of a given size and shape falls, and how much the growth rates are enhanced by, is determined by the airflow around the ice particle, or its aerodynamics. Unfortunately, this is an area of cloud physics where our understanding is extremely limited. The aerodynamics of simple shapes like spheres, spheroids and discs is well studied. However it is clear from observation of natural ice particles that they are not simple in their geometry. Instead the particles are often complex and irregular in their shape. We have almost no high-quality data on the aerodynamics of such particles. As a result, even state-of-the-art microphysical models are forced to approximate the aerodynamical effects on ice processes as though these complex irregular particles were spheres or spheroids, hoping that this is an adequate approximation.

To solve this problem, experimental data is needed for the aerodynamics of particles with the complex shapes that we observe in the atmosphere. The stumbling block is that making suitable observations of natural ice particles in free-fall is extremely challenging. In snowfall at the surface the particles are small, fragile, easily blown by the wind, and likely to melt or evaporate if not handled with great care. Direct sampling of falling particles in cirrus clouds is impossible. In neither case is it possible to directly determine the airflow around the particle or the influence of that flow on the microphysical process rates.

In this project we overcome these problems with the use of analogues. Using 3D printing techniques we will create plastic particles with the same complex geometry as natural ice particles. By dropping the particles in tanks of liquids, and through air in the laboratory and a vertical wind tunnel, we can determine how the fall speed of the particles is controlled by their size and geometry. Exploiting recent developments in tomographic particle imaging velocimetry we can measure the airflow around the falling analogues. From this we can directly determine how the airflow enhances the particle growth, evaporation and melting rates.

National weather services such as the Met Office and DWD need accurate parameterisations for their numerical weather prediction and climate models. This project will provide essential new data to underpin these schemes and will provide new parameterisations of ice particle fall speed, ventilation factor and riming efficiency which can be incorporated into these models making their physical basis stronger and their predictions more robust. DWD will benefit through our direct collaboration during this project.

National weather services will also benefit through the improved understanding of ice particle orientations which can be used to construct more reliable hydrometeor classification algorithms for their dual-polarisation radar networks (for example such a scheme is currently being developed for the UK network by researchers at the University of Leeds and the Met Office).

Government, businesses and the public will benefit as the improvements in weather forecasts and reduction in uncertainty in climate predictions are realised through improved treatment of cloud processes.

The European Space Agency will benefit, because the algorithms being developed for the forthcoming EarthCARE satellite carrying the first Doppler cloud radar in space needs accurate equations linking ice particle size and mass with fall speed. NASA will also benefit since our results on riming distributions on complex snowflakes can be used to improve work (eg. by J. Leinonen at UCLA/JPL) to model the radar backscatter properties of rimed snowflakes for precipitation measurement from spaceborne sensors.

3D printer manufacturers will benefit from the opportunity of an additional scientific application for their products.