Analysis of the Run-to-Run Variability Within the NAMm Forecasts for the Northeast U.S. Blizzard of 8-9 February 2013

On 8-9 February 2013, a strong extratropical cyclone brought historic winter storm conditions to the Northeast United States with a swath of one to three feet of snow falling across much of New England, with isolated pockets exceeding three feet across south-central Connecticut. Given the high socioeconomic impact that resulted from this blizzard, this study focuses on the run-to-run variability in operational model (North American Mesoscale model; NAM) forecasts leading up to the event. These forecasts, initialized forty-eight hours or less from the start of the event, showed two instances indicating a substantial shift in the expected impact. The first occurred between the runs of 0600 UTC and 1200 UTC 7 February 2013 and the second between 0000 UTC and 0600 UTC 8 February 2013. These shifts were discovered to have primarily resulted from large differences in the forecast strength of the warm air advection and the accumulated effects of those differences over time. Analysis of the mesoscale patterns show that the typical mesoscale snowband ingredients of mid-level frontogenesis and conditional symmetric instability (CSI) were not significant contributors to this high impact case. Instead, strong and focused warm air advection, with a secondary contribution from cyclonic vorticity advection through the Sutcliffe self-development process, appears to be the main source of cyclone track variations and the attendant intense, localized vertical motion that lead to the development of the snowbands

“Sounding” Like Lake-Effect Snow: Evaluating the Thermodynamic and Synoptic Setup of Northwest Indiana Lake-Effect Events Using Local Profiles and Numerical Modeling

Lake-effect snow presents significant hazards for Northwest Indiana, which necessitates the need for accurate forecasts for these events. Recent radiosonde observations were taken routinely with radiosonde launches by Valparaiso University’s Aviation Team surrounding lake-effect events. From these soundings, thermodynamic environments are analyzed in conjunction with numerical simulations and official Green Bay soundings of 12 lake-effect precipitation events. This study will use the data from the sounding profiles and from numerical simulations to determine similarities and differences within the thermodynamic and synoptic environments surrounding these events. Through analysis of these pre-storm local environments, unique thresholds will be identified in environmental parameters associated with the setup and formation of lake-effect events over the southern Lake Michigan region. Creating a mean sounding for Northwest Indiana of these lake-effect setups would be optimal for aiding forecasts of similar future events. This analysis should allow observation of model biases and enhance the ability to predict lake-effect snow

Synoptic and Lake-Effect Classification of Snowfall in the Lake Michigan Region

This research is part of a broader project that explores the role of Lake Michigan in its regional climatology. Since 1950, the wintertime temperatures have risen, but snowfall amounts have stayed constant. This study explores snowfall events in October and November to see the role that early lake-effect snow has on the constant yearly snowfall average, as well as to see which areas around the lake are most impacted by lake-effect snow. In order to understand the role of lake-effect snow in the regional climatology, this study used the daily weather map archive to focus on the overall synoptic-scale pattern for each event. Through analysis of reported snow since 1950, the type of snowfall, either synoptic or lake-effect, has been categorized for the whole region. The six sub-regions surrounding Lake Michigan were also individually categorized. This research utilized statistical correlations to learn more about the distribution of synoptic versus lake-effect snow in each sub-region. By understanding the role of lake-effect snow in the regional climatology, improved forecasting tools for the Lake Michigan region can be created