Improvements of Ozone and PM 2.5 Forecasting for the Community Multiscale Air Quality (CMAQ) Model.

An analog-based air quality forecast bias-correction technique, developed through years of collaboration between NOAA/PSL, NCAR, and NCEP, still has some room for improvement, especially for random high pollution events like wild fires. Generating reliable forecasts of extreme values of ozone and PM2.5 is an important aspect of the CMAQ post-processing system.

Several recent changes to the post-processing code, including approaches for fire-associated PM2.5 predictions include:

• a short training period approach for cases of high PM 2.5 related to intense wild fires is implemented and evaluated on several examples including extreme fire event in the western states of Washington, Oregon and California in September, 2020. Several possible training scenarios are discussed.
• developing a new more flexible code that allows for the use of input observational data in both BUFR and CSV format. The new observational data files in CSV format have the advantages of more monitors for both ozone and PM 2.5, and that the file refresh rate is much faster.
• quality control procedures, for both ozone and PM 2.5 observation data, are modified according to new specifications of the environment including more intense wild fires as well as the use of improved monitoring devices.

All these improvements are evaluated with the goal of implementing them in the next generation air quality (AQ) prediction system for the United States being developed at NCEP.