Compositing Parameterization Ensemble Simulations of Static Stability in East Coast Winter Storms Using Kernel Principal Component Analysis

Quasigeostrophic (QG) theory remains a key diagnostic tool in forecasting the propagation and intensification of extratropical cyclones, including high-impact East Coast winter storms. Most synoptic-scale diagnostic studies focus on the traditional QG omega terms to diagnose rising motion but often neglect the importance of the static stability parameter in enhancing these terms. In particular, weaker static stability fields enhance rising air, which can amplify the deepening of extratropical cyclones.

This study focuses on identifying the dominant patterns of low and mid-level static stability in different numerical weather prediction parameterization ensembles in an effort to identify how model parameterizations govern the configuration of static stability fields within East Coast winter storm simulations. A set of twenty 30-member parameterization ensemble WRF simulations of East Coast winter storms from 1999 to 2009 were selected for this study. Static stability fields were formulated using the traditional definition of the static stability parameter for each case at each time in the simulation. Each case was scaled to standard anomalies, based on the mean and standard deviation of all 30 ensemble members of that case. Afterwards, a kernel principal component analysis was used to cluster ensemble members and events to identify any patterns or biases within low and mid-level static stability fields associated with model initialization. The resulting patterns showed key diagnostic considerations that should be considered when forecasting the deepening of East Coast winter storms.