Exploring the Predictability of Synoptically Induced Cold-Air Damming in the Eastern United States

Wintertime cold-air damming (CAD) induces weather conditions along the eastern slopes of the US Appalachian Mountains that can adversely affect those living in the region. For example, cold air, freezing rain, and hazardous air quality due to temperature inversions can disrupt transportation in the air and on the ground. In addition, CAD poses particular forecasting challenges due to persistent cold-air pools and often inadequate boundary-layer resolution and physical realism in NWP models.

Because of the challenges in forecasting CAD along the eastern seaboard, we investigate the sources and scales of the predictability of CAD induced by synoptic forcing—in particular, the occurrence of a cold surface high north of our region of interest. In this research we utilize an objective CAD identification algorithm (Bailey et al. 2003), high-resolution mesoscale NWP forecasts, and a novel Bayesian multi-scale dynamical-statistical forecasting method building upon analogue post-processing techniques in the literature (Delle Monache et al. 2013, Frediani et al. 2017). In addition, our hybrid forecasting method is sensitive to the relationship of ensemble skill and spread, allowing us to explore how differing periods of predictability are reflected in the forecast ensemble’s variable dispersion.