Lukens, Katherine ElizabethThe subseasonal-to-seasonal (S2S) forecast period (2 weeks—2 months) represents a major gap in operational forecasting. Advancing S2S prediction is an international priority, particularly for disaster mitigation and resource management decisions. If storm tracks contain S2S signals, their characterization in long term forecasts could advance S2S prediction by providing important information at longer lead times that may not be acquired from standard wind and precipitation forecasts. Potential damaging effects of Northern Hemisphere winter storm tracks on North American weather are investigated using the NCEP Climate Forecast System (CFS) reanalysis (CFSR). Storm tracks are described by objectively tracking 320-K isentropic potential vorticity anomalies (PV320). Large increases in deep convective heating, near-surface winds, and precipitation are found where strong storms (those with higher PV320) are most intense. The eastern US and North American coasts are most vulnerable to strong-storm related losses, which depend on the dynamics and local population density. Despite representing a small fraction (16%) of all storms, strong-storm tracks have a significant imprint on winter weather potentially leading to structural/economic loss. Storm tracks in weeks 3-4 CFS reforecasts (CFSRR) are examined to assess their potential use in S2S prediction. Removal of statistically significant positive biases in PV320 storm intensity improves general storm track features. CFSRR reproduces observed storm-related weather and the characteristic intensity/frequency of hazardous strong-storm winds. Bias-corrected reforecasts better depict the observed variability in storm-related weather. CFSRR contains useful storm track-related information supporting our hypothesis that storm track statistics contribute to the advancement of S2S prediction of hazardous weather in North America. The weeks 3-4 CFS version 2 (CFSv2) operational forecast performance is evaluated from a storm-focused perspective. CFSv2 retains the ability to predict general storm track behavior. Significant negative biases in storm intensity are apparently driven by mean static stability, with relative vorticity being a secondary driver. CFSv2 partially encapsulates the variability in storm winds and generally reproduces more extreme precipitation observations. Bias corrections improve storm wind forecasts. This work demonstrates that the use of climatological PV storm track statistics coupled with an appropriate storm track bias correction is a powerful instrument for the advancement of S2S prediction.enDissertationAtmospheric sciencesMeteorologyHigh Impact WeatherPrecipitationStorm TracksSubseasonal-to-Seasonal (S2S) PredictionWindsWinter Storms