Measuring snowfall properties with the open-source Video In Situ Snowfall Sensor

We do not know the exact pathways through which ice, liquid, cloud dynamics, and aerosols are interacting in clouds while forming snowfall but the involved processes can be identified by their fingerprints on snow particles. The general shape of individual crystals (dendritic, columns, plates) depends on the temperature and moisture conditions during growth from water vapor deposition. Aggregation can be identified when multiple individual particles are combined into a snowflake. Riming describes the freezing of cloud droplets onto the snow particle and can eventually form graupel. In order to exploit these unique fingerprints of cloud microphysical processes, optical in situ observations are required.

The Video In Situ Snowfall Sensor (VISSS) was specifically developed for a campaign in the high Arctic (MOSAiC) to determine particle shape and particle size distributions. Different to other sensors, the VISSS minimizes uncertainties by using two-dimensional high-resolution images, a large measurement volume, and a design limiting the impact of wind. Tracking of particles over multiple frames allows determining fall speed and particle tumbling. The instrument design and software will be released as open-source. Here, we present the design of the instrument, show how particles are detected and tracked and introduce first results from campaigns in the high Arctic (MOSAiC), in the Colorado Rocky Mountains (SAIL), and in and Hyytiälä (Finland).