Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction with Satellite Data for Rural Areas

• Published in: Journal of Geophysical Research - Atmospheres 2022-01, Vol.127 (1), p.1-n/a
• Main Authors: Zhang, Huanxin, Wang, Jun, Garcia, Lorena Castro, Zhou, Meng, Ge, Cui, Plessel, Todd, Szykman, James, Levy, Robert C., Murphy, Benjamin, Spero, Tanya L.
• Format: Article
• Language: English
• Subjects: chemistry transport model; ensemble Kalman filter; Geosciences (General); IMPROVE; PM2.5 air quality forecast; rural areas; satellite observation; surface observation
• ISSN: 0148-0227; 2169-897X; 2156-2202; 2169-8996
• DOI: 10.1029/2021JD035563

This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS-Chem, WRF-Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non rural areas, referred to as “extended ground truth” or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two-part study develops a multimodel and multi-AOD bias-correction technique that has the potential to improve PM2.5 forecasts in both rural and non rural areas in near real time, and be readily implemented at state levels.