5B.5 WRF-errific Adventures with November Snowfall: Modeling of Early Season Snow Events in the Lake Michigan Region

Tuesday, 30 June 2015: 9:00 AM
Salon A-5 (Hilton Chicago)
Kevin H. Goebbert, Valparaiso Univ, Valparaiso, IN; and R. Connelly and C. Clark

A climatological dataset of snowfall characteristics since 1950 was previously developed for the Lake

Michigan region from an examination of the climatological record of 47 stations surrounding the lake. A

clear feature of the evaluation was a marked decrease in the frequency of November snow for much of the

basin during recent decades, which has not been matched by similar decreases in December, January, and

February. This implies a reduction of early season lake-effect snowfall; however, precipitation data can

be sensitive to observing practices and lake-effect snowfall is not easily distinguished from concurrent or

antecedent synoptic-scale precipitation. Therefore, the topic warrants case-level exploration, with a focus of

individual snow events.

One key aspect of the work is the ongoing generation of a WRF-based climatology of all November Lake

Michigan snow cases since 1950. These simulations utilize NCEP/NCAR Reanalysis data for initial and

boundary conditions, using the WRF Single-Moment 3-class microphysical scheme, the Eta surface layer

similarity scheme, the Mellor-Yamada-Janjic planetary boundary layer scheme, and the Kain-Fritsch

cumulus parameterization scheme. These simulations well-represent precipitation and other key features

of synoptic systems, while capturing the bulk features of lake-effect snow bands and some aspects of event

morphology. Additionally, the spatial distribution of simulated snowfall is largely consistent with the

observed snowfall distribution. Therefore, the results from this WRF-based climatology are compared with

a previous synoptic-based classification of the snow days in order to better understand the relative roles of

synoptic versus lake-effect snowfall.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner