Forecasting snowfall associated with a winter extratropical cyclone (ETC) is really a two-step process. The first step is to assess the current dynamic and thermodynamic characteristics of the storm and then study numerical model forecasts, especially the model quantitative precipitation forecast (QPF). Next, the forecaster must determine a snow to liquid equivalent ratio to forecast the actual snow amount. Thus, it is beneficial to approach the problem of forecasting snowfall in a holistic manner. This research examines interactions between the synoptic and mesoscale within heavy banded snowfall, as well as interactions between the microscale and mesoscale in the form of snow to liquid equivalent ratios.

On 26-27 November 2001 heavy snow fell over the upper Midwestern states, exceeding 20 inches in various locations. This area of heavy snowfall occurred in a relatively narrow band with an average half-width of 98 km over Minnesota, Wisconsin, South Dakota, Nebraska, and the Upper Peninsula of Michigan. The narrow characteristics of the snowband suggest that unique kinematic and dynamic processes played an integral role in the organization of the precipitation within a trough of warm air aloft (trowal). These processes will be examined along with a diagnosis of frontogenesis and conditional symmetric instability.

Snow to liquid equivalent ratios can vary considerably from the established guideline of 10 to 1, causing inaccurate snowfall depth forecasts even when the amount of liquid water created by the ETC is correctly predicted. A 30-year climatology of snow to liquid ratio has been computed for the United States. For single cases, some skill is apparent in determining an absolute snow to liquid equivalent ratio in comparison to the 30-year average. In most cases it is possible to determine relative areas of higher and lower snow to liquid equivalent values if clear patterns in snow to liquid equivalent are found, as in the 26-27 November 2001 case. The value of the snow to liquid equivalent ratio is determined by both in-cloud and sub-cloud microphysical interactions, some of which can be extracted through an examination of vertical profiles of temperature and moisture.