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
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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

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Details:

  • Journal Title:
    Journal of Geophysical Research: Atmospheres
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    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 multisatellite 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 nonrural 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 nonrural 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 nonrural areas in near real time, and be readily implemented at state levels.
  • Source:
    Journal of Geophysical Research: Atmospheres, 127(1)
  • DOI:
  • ISSN:
    2169-897X;2169-8996;
  • Format:
    PDF
  • Publisher:
    American Geophysical Union (AGU)
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA19OAR4310178
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
  • Download URL:
  • File Type:

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