Improving predictability of high-ozone episodes through dynamic boundary conditions, emission refresh and chemical data assimilation during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign

Ma, Siqi; Tong, Daniel; Lamsal, Lok; Wang, Julian; Zhang, Xuelei; Tang, Youhua; Saylor, Rick; Chai, Tianfeng; Lee, Pius; Campbell, Patrick; Baker, Barry; Kondragunta, Shobha; Judd, Laura; Berkoff, Timothy A.; Janz, Scott J.; Stajner, Ivanka

doi:https://doi.org/10.5194/acp-21-16531-2021

Articles | Volume 21, issue 21

https://doi.org/10.5194/acp-21-16531-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-21-16531-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 21

Research article

|

11 Nov 2021

Research article |

| 11 Nov 2021

Improving predictability of high-ozone episodes through dynamic boundary conditions, emission refresh and chemical data assimilation during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign

Siqi Ma, Daniel Tong, Lok Lamsal, Julian Wang, Xuelei Zhang, Youhua Tang, Rick Saylor, Tianfeng Chai, Pius Lee, Patrick Campbell, Barry Baker, Shobha Kondragunta, Laura Judd, Timothy A. Berkoff, Scott J. Janz, and Ivanka Stajner

Supplement

https://doi.org/10.5194/acp-21-16531-2021-supplement

Data sets

AirNow dataset US EPA https://files.airnowtech.org/?prefix=airnow

Air Quality System (AQS) US EPA https://aqs.epa.gov/aqsweb/airdata/download_files.html#Raw

LISTOS -- Long Island Sound Tropospheric Ozone Study National Aeronautics and Space Administration (NASA) https://www-air.larc.nasa.gov/cgi-bin/ArcView/listos

Chemistry and Dynamics Branch National Aeronautics and Space Administration (NASA), Goddard Space Flight Center (GSFC) https://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/OMI

Model code and software

WRF source codes WRF Development and Support Team https://www2.mmm.ucar.edu/wrf/users/download/get_source.html

Community Multiscale Air Quality (CMAQ) Model Version 5.3.1 Community Multiscale Air Quality (CMAQ) Model Version 5.3.1 https://www.cmascenter.org/download/software/cmaq/cmaq_5-3-1.cfm?DB=TRUE

Sparse Matrix Operator Kerner Emissions (SMOKE) Modeling System Version 4.7 Community Modeling and Analysis System (CMAS) Center https://www.cmascenter.org/download/software/smoke/smoke_4-7.cfm?DB=TRUE

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Short summary

Predicting high ozone gets more challenging as urban emissions decrease. How can different techniques be used to foretell the quality of air to better protect human health? We tested four techniques with the CMAQ model against observations during a field campaign over New York City. The new system proves to better predict the magnitude and timing of high ozone. These approaches can be extended to other regions to improve the predictability of high-O₃ episodes in contemporary urban environments.