Enhancing Our Understanding of Deep Versus Shallow Snowfall Microphysics with Ground-Based Observations

Snowfall can be broadly categorized into deep and shallow events based on the vertical distribution of the precipitating ice. Remotely sensed data refine these precipitation categories and aid in discerning the underlying macro- and microphysical mechanisms. The unique patterns in the remotely sensed instruments observations can potentially connect distinct modes of snowfall to specific processes. Though satellites can observe and recognize these patterns in snowfall, these measurements are limited – particularly in cases of shallow and light precipitation, as the snow may be too close to the surface or below the detection limits of the instrumentation. By enhancing satellite measurements with ground-based instrumentation, whether with limited-term field campaigns or long-term strategic sites, we can further our understanding and test assumptions about different snowfall modes and how they are measured from spaceborne instruments. Presented are over three years of data from a ground-based instrument suite consisting of a MicroRain Radar (MRR; optimized for snow events) and a Precipitation Imaging Package (PIP). These instruments are located at the Marquette, Michigan National Weather Service Weather Forecast Office to: a) use coincident meteorological measurements and observations to enhance our understanding of the thermodynamic drivers and b) showcase these instruments in an operational setting to enhance forecasts of shallow snow events. Three winters of MRR and PIP measurements are partitioned, based on meteorological surface observations, into two-dimensional histograms of reflectivity and particle size distribution data. Additionally, statistics of modeled thermodynamic profiles are coupled with the identified modes of snowfall. These statistics improve our interpretation of deep versus shallow precipitation.