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USING ADVANCED STATISTICAL TECHNIQUES TO IDENTIFY THE DRIVERS AND OCCURRENCE OF HISTORICAL AND FUTURE EXTREME AIR QUALITY EVENTS IN THE UNITED STATES FROM OBSERVATIONS AND MODELS
Impact/Purpose:
Extreme weather events can be accompanied by extreme air quality degradation with associated costs to human health and society. The relationship between extreme weather and air quality is poorly understood, and relatively untested in models. Given expected changes to climate, we will quantify this hazard based on the observational record and verify with what fidelity models reproduce the relationships between extreme weather and air quality for present day and then project how these might change in the future.
Description:
This proposed project will result in fundamentally new insights into the connections between extreme weather and air quality. This will include probabilistic relationships between pollutants (PM2.5 and O3) and important meteorological drivers regionally within the United States based on the observational record. Furthermore we will comprehensively evaluate model skill in reproducing and projecting these relationships for current and future climate, providing much needed insight as to the fidelity of extreme-event hazard prediction with climate models. This work directly addresses several of the goals of the solicitation to apply techniques to look at the historical data on extreme events and their impact on air quality and identify how air quality models can be enhanced to represent these events.