A Composite Analysis of Snowfall Modes from Four Winter Seasons in Marquette, Michigan

This presentation highlights precipitation data gathered during four winter seasons from a combined remote sensing and in-situ instrumentation suite at the National Weather Service (NWS) office in Marquette (MQT), Michigan. In a typical winter, the MQT NWS office records seasonal snow accumulation in the range of 350 – 650 cm. Much of this accumulation is the product of surface influenced snow processes, such as Lake Effect Snow (LES) and orographically enhanced snow from shallow mesoscale systems. Since the snow processes observed at NWS MQT are strongly influenced by boundary layer conditions, the site was augmented in 2014 to include a MicroRain Radar (MRR) and a Particle Imaging Package (PIP). The MRR is a vertically pointing 24 GHz radar that provides a detailed view of the precipitation structure and variance from 300 to 3000 meters above ground level. The PIP records in-situ videos of snow falling at the surface, which are used to infer particle size distributions and fall speeds. The MRR and PIP measurements are combined with MQT NWS meteorological observations and surface accumulation measurements to partition and quantify the snowfall modes. We compare occurrence and accumulation statistics from the synoptically forced (deep) versus surface affected or enhanced snowfall (shallow). Both the MRR and PIP illustrate different characteristics with respect to either snowfall mode. Initial results indicate that the deep and shallow modes roughly equally contribute to the accumulation for any given year. The deep and shallow snowfall modes have distinct characteristics as observed by the MRR, as well as differences in particle size distributions as measured by the PIP, and varying snow to liquid ratios as indicated by NWS observers. The results from the enhanced precipitation instrumentation suite indicate that these snowfall modes have distinct dynamical forcing as well as different microphysical processes that produce snowfall.