Observational Evaluation of the Effects of Atmospheric Temperature and Turbulence on Hydrometeor Fallspeed

Forecasts of severe weather events and climate extremes depend heavily upon accurate computer model representations of how fast snowflakes fall. Providing this information has proved an immensely challenging measurement problem because snowflakes are delicate, they evaporate quickly, and their shapes and sizes are exceptionally varied depending on the conditions in which they formed.

No weather or climate model currently accounts for how snowflakes swirl as they are buffeted by turbulence during their fall. This is largely because the settling of particles in a moving fluid remains unsolved, despite that fact that the problem has very general importance to a wide range of fields in the physical and biological sciences. The study aims to advance our knowledge of these problems by using a unique suite of instruments deployed to a field site at a high-elevation location in the Wasatch mountain range of Utah, that includes the capacity to automatically track individual snowflake motions in a laser light sheet, quantify the magnitude of air turbulence in their direct environment, and subsequently measure their mass, size, shape, and density.

The projected outcomes of this three-year study include revised formulations for the relationship of snowflake fall speed, mass, and density to air temperature, and of the extent to which precipitation rates are enhanced or retarded by turbulence in storms. Anticipated improvements to weather instrumentation will assist commercialization through a University of Utah spin-off company for wider availability to the weather measurement, infrastructure resilience, and transportation safety sectors.

Public outreach includes data classification through citizen science and data dissemination to snow-sports enthusiasts.

Snowflakes are denser in warmer air, and they display a highly non-linear response to turbulence whose impact on their average settling speed remains to be determined. This study aims to develop a more sophisticated understanding of two of the principle atmospheric processes determining how fast precipitation particles fall, temperature and turbulence.

The University of Utah is uniquely poised to address this problem with its development of two new instruments with prior National Science Foundation’s support that permit the first direct, automated, measurements of individual hydrometeor mass and density. These devices will be deployed to a snowy high elevation field site alongside temperature, wind, and turbulence sensors, as well as a laser light sheet and fog machine.

Particle Imaging Velocimetry will be used to track the motions of snowflakes and surrounding turbulent air. The expected project outcome will be revised parameterizations describing the relationship between atmospheric temperature and hydrometeor size, mass and density, as well as quantified metrics for how atmospheric turbulence affects individual hydrometeor mass flux and collective precipitation rate.

In terms of its broader impacts, the study will exploit a combination of machine-learning and citizen science to facilitate hydrometeor classification. Additionally, anticipated improvements to instruments will be made available to the wider scientific and societal resilience communities through commercialization by an established University of Utah spin-off company.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.