Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

ABSTRACT

A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O₃) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O₃ pollution. Comparisons to vertical O₃ profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O₃ concentrations on high O₃ days and overestimate on low O₃ days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O₃ levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM_2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O₃ levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system.

Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

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Data availability

For access to WRF-Chem forecast data files, reach out to J. Jerrold M. Acdan ([email protected]). For access to RAQMS forecast data files, contact R. Bradley Pierce ([email protected]). Downloading WACCM6 forecast files requires registration at https://www.acom.ucar.edu/waccm/register.shtml. GFS meteorological data can be downloaded from https://www.nco.ncep.noaa.gov/pmb/products/gfs/. More information about NEMO 1-km anthropogenic emissions, including how to download the dataset, can be found at http://air.csiss.gmu.edu/aq/US01emis/. U.S. EPA NEI 2017 point source emissions can be downloaded from https://www.acom.ucar.edu/wrf-chem/EPA_2017/. Information about pre-processors for WRF-Chem inputs can be accessed at https://www2.acom.ucar.edu/wrf-chem/wrf-chem-tools-community. Surface pollution concentrations (e.g., O₃ and PM_2.5) and co-located weather data (e.g., wind speed and direction) from the U.S. EPA Air Quality System (AQS) can be downloaded as raw hourly files from AirNow Tech (https://files.airnowtech.org/?prefix=airnow/) and as pre-generated data files from the AirData website (https://aqs.epa.gov/aqsweb/airdata/download_files.html). RO₃QET O₃ lidar data can be downloaded from https://tolnet.larc.nasa.gov/download. GOES-16 satellite products can be downloaded from https://home.chpc.utah.edu/~u0553130/Brian_Blaylock/cgi-bin/goes16_download.cgi. The web-based NOAA HYSPLIT model can be accessed at https://www.ready.noaa.gov/HYSPLIT.php.

Acknowledgements

We acknowledge Alison Eyth and Barron H. Henderson at the U.S. Environmental Protection Agency (EPA) for making SMOKE outputs available as well as Gabriele Pfister and Stacy Walters at the National Center for Atmospheric Research (NCAR) and Stu McKeen at the National Oceanic and Atmospheric Administration (NOAA) for developing and providing tools to integrate SMOKE emissions into WRF-Chem. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (https://www.ready.noaa.gov) used in this publication. We acknowledge the Wisconsin Department of Natural Resources (WI DNR) for sharing the summer 2023 Chiwaukee Prairie 1-minute air quality monitoring dataset that was used for analyses in this study. We acknowledge the University of Utah, Brian Blaylock, and Julien Chastang for providing webpages for downloading GOES-16 satellite data and instructions for creating true color images.

Disclosure of interest

The authors have no relevant financial or non-financial competing interests to report.

Additional information

Funding

Juanito Jerrold Mariano Acdan was supported by a NASA FINESST grant (80NSSC22K1435). Real-time forecasting efforts for the NOAA AEROMMA 2023 field campaign was supported by the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Research (STAR). Shi Kuang’s work was supported by NASA’s TOLNet program with a grant code of 80NSSC19K0247. The NSF National Center for Atmospheric Research (NCAR) is sponsored by the National Science Foundation (NSF). The development of the NEMO dataset is supported by the NOAA Weather Program Office and the NASA Health and Air Quality Program.

Funding

This work was supported by the Earth Sciences Division [FINESST grant 80NSSC22K1435]; National Aeronautics and Space Administration [TOLNet Program grant 80NSSC19K0247].