S32 Evaluation of High Resolution Rapid Refresh-Smoke (HRRR-Smoke) model products for a case study using surface PM2.5 observations

Sunday, 22 January 2017
4E (Washington State Convention Center )
Lauren Deanes, UCAR, Boulder, CO; and R. Ahmadov, K. L. Manross, S. A. McKeen, G. Grell, and E. P. James

Wildfires are increasing in number and size in the western United States as climate change contributes to warmer and drier conditions in this region. These fires lead to poor air quality and diminished visibility. The High Resolution Rapid Refresh-Smoke modeling system (HRRR-Smoke) is designed to simulate fire emissions and smoke transport with high resolution. The model is based on the Weather Research and Forecasting model, coupled with chemistry (WRF-Chem) and uses fire detection data from the Visible Infrared and Imaging Radiometer Suite (VIIRS) satellite instrument to simulate wildfire emissions and their plume rise. HRRR-Smoke is used in both real-time applications and case studies. In this study, we evaluate the HRRR-Smoke for August 2015, during one of the worst wildfire seasons on record in the United States, by focusing on wildfires that occurred in the northwestern US. We compare HRRR-Smoke simulations with hourly fine particulate matter (PM2.5) observations from the Air Quality System (https://www.epa.gov/aqs) from multiple air quality monitoring sites in Washington state. PM2.5 data includes measurements from urban, suburban and remote sites in the state. We discuss the model performance in capturing large PM2.5 enhancements detected at surface sites due to wildfires. We present various statistical parameters to demonstrate HRRR-Smoke’s performance in simulating surface PM2.5 levels.
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