Title: From Sensors to Ensembles: Precipitation Science for Extreme Weather and Flood Resilience

Abstract: Extreme rainfall and hailstorms are among the costliest natural hazards, yet measuring and predicting them remains surprisingly difficult. Rain gauges are sparse, radar has blind spots, satellites introduce their own biases, and the weather and climate models we use to forecast these events operate at scales far too coarse to capture the localized storms that cause the most damage. At the same time, decision-makers need information that is not only accurate, but also honest about uncertainty. In this talk, I will present my group's approach to closing these gaps, organized around a simple idea: precipitation science should hold up in practice. I will discuss how we quantify and communicate uncertainty in satellite and radar precipitation products, and why that matters for everything downstream. I will then show how stochastic methods can generate realistic high-resolution storm ensembles from coarse model outputs — turning a single blurry forecast into hundreds of plausible scenarios. Applications include improving seasonal predictions of extreme rainfall events, emulating hail hazards across the U.S. under current and future climate, and validating satellite precipitation ensembles against ground-based radar networks. I will close with my research vision at the University of Iowa: connecting better precipitation observations and uncertainty-aware prediction to real-world decisions about floods, infrastructure, and weather risk.

Short Bio: Yagmur Derin is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Iowa. Her research aims to improve how extreme precipitation is measured, modeled, and translated into actionable information for flood resilience. She develops stochastic downscaling approaches, uncertainty quantification methods for satellite precipitation products, and data-driven hazard emulators that bridge large-scale atmospheric modeling and local-scale impacts. Before joining Iowa, she was a Research Scientist at the University of Wisconsin–Madison, where she worked on convective hazard emulation and stochastic downscaling of NASA seasonal forecasts. She previously served as a postdoctoral researcher at the University of Oklahoma from 2019 to 2023. She earned her Ph.D. in Civil and Environmental Engineering from the University of Connecticut in 2019 and her M.Sc. in Geological Engineering from Middle East Technical University in 2014.