Improving surface PM 2.5 forecasts in the United States using an ensemble of chemical transport model outputs: 2. bias correction with satellite data for rural areas

Huanxin Zhang; Jun Wang; Lorena Castro Garcia; Meng Zhou; Cui Ge; Todd Plessel; James Szykman; Robert C Levy; Benjamin Murphy; Tanya L Spero

doi:10.1029/2021JD035563

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Improving surface PM 2.5 forecasts in the United States using an ensemble of chemical transport model outputs: 2. bias correction with satellite data for rural areas

Journal article

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Improving surface PM 2.5 forecasts in the United States using an ensemble of chemical transport model outputs: 2. bias correction with satellite data for rural areas

Huanxin Zhang, Jun Wang, Lorena Castro Garcia, Meng Zhou, Cui Ge, Todd Plessel, James Szykman, Robert C Levy, Benjamin Murphy and Tanya L Spero

Journal of geophysical research. Atmospheres, Vol.127(1), e2021JD035563

12/2021

DOI: 10.1029/2021JD035563

PMCID: PMC10953817

PMID: 38511152

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Abstract

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.

Details

Title: Subtitle: Improving surface PM 2.5 forecasts in the United States using an ensemble of chemical transport model outputs: 2. bias correction with satellite data for rural areas
Creators: Huanxin Zhang - University of Iowa
Jun Wang - University of Iowa
Lorena Castro Garcia - University of Iowa
Meng Zhou - University of Iowa
Cui Ge - University of Iowa
Todd Plessel - General Dynamics Information Technology RTP NC USA
James Szykman - U.S. Environmental Protection Agency RTP NC USA
Robert C Levy - Goddard Space Flight Center
Benjamin Murphy - Environmental Protection Agency
Tanya L Spero - U.S. Environmental Protection Agency RTP NC USA
Resource Type: Journal article
Publication Details: Journal of geophysical research. Atmospheres, Vol.127(1), e2021JD035563
DOI: 10.1029/2021JD035563
PMID: 38511152
PMCID: PMC10953817
NLM abbreviation: J Geophys Res Atmos
ISSN: 2169-897X
eISSN: 2169-8996
Language: English
Date published: 12/2021
Academic Unit: Civil and Environmental Engineering; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering
Record Identifier: 9984199859202771

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