Prediction & Predictability

Industries (insurance, energy, infrastructure) rely on climate model projections for risk assessment. Different climate models give different predictions for extreme weather (heat waves, floods, etc.). Users often aggregate all models—both accurate and inaccurate—leading to uncertainty in risk assessment.

This project will develop a statistically rigorous method to identify which climate model simulations reliably capture extreme weather trends.

Team: Timothy DelSole (GMU)

Cities can affect thunderstorms. Insurance estimates of storm damage are based on past statistics that often lack urban signals. While urban exposure is a well understood  component of risk, less is known about urban modification of convective storms, a secondary peril that has led to large losses in recent years.

This project will create statistics of storm development in the vicinity of cities as a function of urban, meteorological, and climate drivers. We will also develop new estimates of urban storm modification to extend existing natural catastrophe model capability.

Team: vant-Hull (CUNY), Ortiz (GMU)

In this study, we will develop an extreme value framework to reliably determine distributions of “unprecedented events” from high-resolution observations. From these, large ensemble regional models will provide estimated forced change projections. We will then apply these "record-breaking" probabilities to determine a local "risk awareness index" based on historical experience and identify regions where increased risk awareness and risk reduction measures should be a priority.

Team: de Vries (MIT),  O'Gorman (MIT)

Due to climate change, in particular sea level change and hurricane patter alteration, the resilience of residential areas and critical infrastructure to ocean waters has gotten more attention recently.  Existing climate resilience research has substantial gaps from actual need of resilience development, i.e., we need a more accurate prediction of local extreme events instead of the theoretical and macro study of large-scale hazards.  This project will develop a modeling framework to improve the resilience of critical infrastructure to extreme ocean waves and surges, so that the outcome will provide capabilities to directly resolve local extreme events such as wave slamming at the structure of our interest. 

Team: Kawaguchi (CUNY), Tang (CUNY), Devineni (CUNY)