Â鶹´å

Electric utilities face growing vulnerability to extreme weather as storm intensity and complexity increase in a changing climate. This talk provides an overview of how different weather hazards stress electric grid infrastructure and translate into outages and prolonged restoration. It explores the role of predictive modeling and data science, including machine-learning approaches, in anticipating storm impacts and supporting operational planning for utilities. Through case studies of the January 2026 southern ice storm and the Pittsburgh-area thunderstorm high-wind “derecho” event in April–May 2025, the presentation examines the relationship between hazard intensity, infrastructure stress, outage extent, and restoration outcomes. Emphasis is placed on lessons learned for future storm preparedness, decision support, and resilience planning.

Jay Shafer (PhD) is an atmospheric scientist and entrepreneur with expertise in extreme weather, applied forecasting, and grid infrastructure resilience. His work emphasizes the translation of meteorological knowledge into operational decision support products for electric utilities and public agencies. A former professor of meteorology, he taught the art and science of weather prediction before founding and leading a company that developed storm-impact prediction software to support utility preparedness and response. Jay currently serves as Chief Innovation Officer at PowerOutage.com, which aggregates real-time power outage information for emergency and crisis management.