Development and Implementation of a Remote- Sensing and In-situ Data Assimilating Version of CMAQ for Operational PM2.5 Forecasting Part 1: MODIS Aerosol Optical Depth (AOD) Data- Assimilation Design and Testing
Date of this Version
John N. McHenry, Jeffery M. Vukovich & N. Christina Hsu (2015): Development and Implementation of a Remote-Sensing and In-situ Data Assimilating Version of CMAQ for Operational PM2.5 Forecasting Part 1: MODIS Aerosol Optical Depth (AOD) Data- Assimilation Design and Testing, Journal of the Air & Waste Management Association
Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ridesharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.