New measurements of snowflake scattering and microstructure using a novel Multi-Wavelength, Multi-Angle Scatterometer (MuWMAS)

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At high latitudes the majority of precipitation falling at the earth's surface is snow. Quantitative measurements and forecasting of snow precipitation is important because heavy snow can disrupt transport and electricity infrastructure, snow conditions are important for the health of natural ecosystems, agriculture and tourism, and snow is an essential part of the hydrological cycle. However, not all snow falls at the ground. In the mid-latitudes (like the UK), most of our precipitation falls as rain: the majority of that rain actually originates as snowflakes higher up in the atmosphere that melt as they fall to earth. Snowflakes in clouds are also important as they strongly affect the earth's climate. We need to be able to quantitatively measure the microphysical properties of snowflakes, realistically simulate ice-phase processes in computer models, and "assimilate" remote sensing satellite data affected by snowflakes to betterinitialise numerical weather prediction models and, in turn, improve forecasts. Essential to these applications is the famously complex and highly variable geometry, or "microstructure", of the snowflakes. This microstructure controls the way that snowflakes scatter electromagnetic waves; information that is vital to interpreting and exploiting remote sensing measurements for research and for initialising forecasts. There is an ever-expanding range of models for the shape and microstructure of snowflakes, with numerous databases of scattering properties for snowflakes generated by various numerical algorithms or models, each based on different assumptions about the nature of the particles or the physical processes that generated them. The questions we wish to answer are: (1) which, if any, of these models is right?, and (2) under what conditions is a given model a realistic representation and under what conditions does it fail? Our project is a unique experiment which will allow us to answer these questions. The aim of this proposal is to constrain those scattering properties and microstructure information by developing and exploiting a Multi-Wavelength, Multi-Angle Scatterometer (MuWMAS), a novel ground-based instrument which illuminates natural falling snowflakes in-situ with millimetre and sub-millimetre electromagnetic waves. The snowflakes scatter some of these waves to an array of 5 detectors at different angles. We will use the data to directly test current state-of-the-art models for the scattering properties of snowflakes, and theory shows us that this also provides a direct constraint on the microstructure of those particles. Furthermore, by sampling at both horizontal and vertical polarisations we will be able to test theories of snowflake orientation and deduce the impact of this on the remote sensing of snow. MuWMAS will be a reliable, automated instrument, providing calibrated accurate data. We will deploy it to a high-latitude (62 N) field site where there is frequent snowfall and which is well equipped with additional instrumentation (optical imaging probes, radars, lidars etc.) that can help us interpret our results in greater depth. MuWMAS will sample snowflakes at this site continuously over two winter seasons in a variety of snowfalls, allowing us to investigate the influence of different microphysical processes and growth conditions on the scattering and microstructure properties of the snowflakes. Finally, we will use our results on the accuracy (or otherwise) of scattering models and databases to improve the representation of snowflake scattering to be used by meteorological services, such as the Met Office. This will enhance the assimilation of satellite measurements used to initialise meteorological models and ultimately lead to better weather forecasts. The improved understanding of snowflake scattering will also lead to more accurate operational "retrievals" of ice properties from satellites like the Ice Cloud Imager due to be launched in 2024.