Upcoming PCAR Seminar: Enhancing Climate Resilience Through Weather Hazard Prediction by Dr. Malaquias Peña, University of Connecticut

Abstract: In the face of accelerating climate change, forecasting weather hazards is critical for community, infrastructure, and ecosystem resilience. The practical task for scientists and engineers is to make forecast products timely and with sufficient confidence in accuracy and information to influence users’ decisions. This presentation explores the roles of weather prediction and socio-economic impact models to inform decision-making. We begin with a brief historical review of numerical prediction models from atmospheric to Earth system models. Then, we highlight challenges and opportunities in transitioning from first principles to data-driven approaches. We present the complexity of the dynamics behind El Niño events and their impact on North American weather patterns to showcase model limitations for effective hazard forecasting and the need to quantify and communicate uncertainties. We then delve into numerical experiments assessing Sandy-like hurricane risks on New England's energy security amidst increasing renewable energy penetration. In this case, ensemble modeling and high-resolution simulations aid in predicting weather conditions, whereas optimal power flow, outage power forecasting, and UC/ED algorithms aid in determining power grid responses and socioeconomic impacts depending on supply and demand scenarios. Lastly, we discuss socioeconomic factors influencing flood insurance adoption, and implications for tailoring resilience strategies and enhancing adaptive capacity. In conclusion, we stress the pivotal role of weather hazard forecasting and system impact-response for climate change resilience. By harnessing scientific advances, stakeholder engagement, and adaptive governance, we can build more resilient societies that thrive in a changing climate landscape.

Bio: Dr. Malaquías Peña received his BSc. in Physics from the National Autonomous University of Mexico, MSc. in Meteorology from the University of Oklahoma, Norman, OK, and Ph.D. in Meteorology from the University of Maryland, College Park, MD. He was a scientist and task leader in ensemble modeling at the Environmental Modeling Center of the National Weather Service, NOAA. He was a model developer contributing to various numerical prediction systems including the Global Ensemble Forecast System (GEFS), the Climate Forecast System version 2 (CFSv2), the Unified Forecast System (UFS), and managed various national and international projects for high-impact weather and sub-seasonal to seasonal prediction. He is an Associate Professor in Civil and Environmental Engineering and founding director of the Power Grid Modernization Certificate Program at the University of Connecticut. His areas of interest include process modeling in the natural and built environment, observing networks, data assimilation, seasonal to weather forecasting, and its applications to water security and renewable energy integration.

UMass Host: Brenda Philips