Parameterization of Arctic Hydrometeor Physics using New Precipitation Measurement Technologies

Principal Investigator(s):
Timothy Garrett, University of Utah

Weather and climate predictions of precipitation amount, location and duration are especially sensitive to the equations that describe how frozen precipitation particles grow and fall. Yet very few published measurements are available that can be used to describe relationships between precipitation type, size, density and fall speed. Almost none are from the Arctic, an especially important region for understanding regional and global climate. Current numerical models that are being used to forecast weather and climate draw from a small database of measurements made in the Cascade Mountain Range of Washington State over the course of two winters in 1971 and 1972 when just 376 snow particles were sampled in detail. Without more extensive data that is geographically specific, current numerical model development has been limited to ad hoc tuning, for example by halving precipitation particle density in order to improve the agreement between weather predictions and observations.

The proposed study will provide refined parameterizations of precipitation properties and processes with a particular focus on the Arctic. To accomplish the project goals, the study will use new instrumentation that was recently installed at the DOE ARM Oliktok Point Mobile Facility in Alaska. The Multi-Angle Snowflake Camera (MASC) is the first device that is able to automatically photograph precipitation particles in free-fall from multiple angles while simultaneously measuring their fall speed. Past efforts using the MASC took millions of pictures of frozen precipitation particles at Alta, Utah to shed new light on snowfall properties as a function of local meteorological conditions. Substantial differences were found between the observations and current weather and climate model assumptions. Among the most interesting was that snowflakes that have been heavily rimed by frozen cloud droplets fall more slowly when the temperature is low, probably because the frozen accretions are more porous. Additionally, it was observed that in highly turbulent conditions, snow fell at a speed that was nearly independent of particle size. This results runs counter to existing computer model parameterizations which assume that snow falls in still air and that larger particles fall fastest.

The MASC installed at the DOE ARM Oliktok Point mobile facility will be used to further examine these relationships, taking particular advantage of the unique suite of precipitation, meteorological, and remote sensing instrumentation that is available at the high latitude site. Combined with radiometer data at Oliktok Point the data open the possibility to relate precipitation particle characteristics to the clouds where the snow is created. Ground-based wind measurements will provide detailed data on turbulence. The anticipated outcomes from the study will be refined mathematical expressions for relationships between hydrometeor fallspeed, microphysics that can be used to improve weather and climate predictions.