2026: Improving MCS Wind Representation in Insurance Loss Footprints (Momentum) (KCC)
Mesoscale convective systems (MCS), including derechos and other organized windstorms, create significant challenges for accurately estimating storm-related insurance losses. These large, long-lasting weather events can cause widespread damage that traditional forecasting tools and severity measures may underestimate. Karen Clark & Company (KCC), which provides automated modeling tools to help insurers estimate losses and claims, has determined MCS-related wind events as an important gap in its current models. This research project will explore how machine learning and artificial intelligence can improve the detection and analysis of MCS events. The goal is to develop and test a prototype that identifies MCS-related wind hazards and estimates their location and potential intensity, supporting more accurate insurance loss modeling.
Cornell: Arthur DeGaetano and Toby Ault (both Cornell CALS / Earth and Atmospheric Sciences); Swatah Snigdha Borkotoky (Research Scientist)
Karen Clark & Company (KCC): Dan Ward, Atmospheric Scientist