Jordan Munson, Joseph K. Vaughan, Brian K. Lamb, Yunha Lee 

The Air Indicator Report for Public Awareness and Community Tracking (AIRPACT) is a comprehensive, automated air quality forecast system that provides 48-hr in-advance air quality over the Pacific Northwest region (http://lar.wsu.edu/airpact/). Since 2001, the AIRPACT forecasting system has been successfully operated by Washington State University, with the financial support from the Northwest International Air Quality and Environmental Science consortium (NW-AIRQUEST). AIRPACT consists of the Sparse Matrix Operator Kernel Emissions (SMOKE) model to provide temporal and spatial emissions, the Community Multiscale Air Quality (CMAQ) model to simulate hourly ozone, particulate matter and related precursor concentrations over the Pacific Northwest region, and the Weather Research and Forecasting (WRF) model to simulate meteorology fields which are inputs for CMAQ: WRF is run by University of Washington and their outputs are transferred to Washington State University. AIRPACT is one of the longest operational regional air quality forecast system in the US that is based on a chemical transport modeling. In this paper, we have evaluated AIRPACT forecasts for the last ten years (2009-2018) against quality-controlled EPA Air Quality System observations, with particular focus on examining how overall air quality forecast skill has changed as the AIRPACT system has evolved. During this period, AIRPACT has been intermittently updated with improved physical and chemical processes as well as newer emissions and higher resolution model domains. Our evaluation results show that AIRPACT’s skill at forecasting ozone (O3) has improved over time. However, the fine particulate matter (PM2.5), forecast performance has decreased over time. The PM2.5 forecasts in the most recent version of AIRPACT were underpredicted to a larger degree than the previous version, partly because elevated PM2.5 concentrations during the wildfire season in the years 2015 and 2018 were underestimated. In order to improve overall air quality forecast accuracy, our future efforts should focus on building a more reliable forecast system to handle extreme air quality events in combination with using new techniques for data-assimilation, ensemble forecasting, and statistical post-processing.