Preprints
https://doi.org/10.5194/acp-2020-1291
https://doi.org/10.5194/acp-2020-1291

  22 Feb 2021

22 Feb 2021

Review status: this preprint is currently under review for the journal ACP.

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 Ma1,2, Daniel Tong1,3, Lok Lamsal4,5, Julian Wang6, Xuelei Zhang3, Youhua Tang3,6, Rick Saylor6, Tianfeng Chai4, Pius Lee6, Patrick Campbell3,6, Barry Baker3,6, Shobha Kondragunta7, Laura Judd8, Timothy A. Berkoff8, Scott J. Janz4, and Ivanka Stajner9 Siqi Ma et al.
  • 1Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030 USA
  • 2National Research Council, hosted by the National Oceanic and Atmospheric Administration Air Resources Lab, College Park, MD 20740 USA
  • 3Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030 USA
  • 4Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, MD 20771 USA
  • 5Universities Space Research Association, Columbia, MD 21046 USA
  • 6National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, College Park, MD 22030 USA
  • 7NOAA National Environmental Satellite Data and Information Service, College Park, MD 20740 USA
  • 8NASA Langley Research Center, Hampton, VA, 23681 USA
  • 9NOAA National Weather Service National Centers for Environmental Prediction, College Park, MD 20740 USA

Abstract. Although air quality in the United States improved remarkably in the past decades, ground-level ozone (O3) rises often in exceedance of the national ambient air quality standard in nonattainment areas, including the Long Island Sound (LIS) and its surrounding areas. Accurate prediction of high ozone episodes is needed to assist government agencies and the public in mitigating harmful effects of air pollution. In this study, we have developed a suite of potential forecast improvements, including dynamic boundary conditions, rapid emission refresh and chemical data assimilation, in a 3 km resolution Community Multi-scale Air Quality (CMAQ) modeling system. The purpose is to evaluate and assess the effectiveness of these forecasting techniques, individually or in combination, in improving forecast guidance for two major air pollutants: surface O3 and nitrogen dioxide (NO2). Experiments were conducted for a high O3 episode (August 28–29, 2018) during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, which provides abundant observations for evaluating model performance. The results show that these forecast system updates are useful in enhancing the capability of the forecasting model with varying effectiveness for different pollutants. For O3 prediction, the most significant improvement comes from the dynamic boundary conditions derived from NOAA National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) from 0.81 to 0.93 and reduces the Root Mean Square Error (RMSE) from 14.97 ppbv to 8.22 ppbv, compared to that with the static boundary conditions. The NO2 from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, highlighting the importance of spatially resolved emission and meteorology inputs for the prediction of short-lived pollutants. The effectiveness of improved initial concentrations through optimal interpolation (OI) is shown to be high in urban areas with high emission density. The influence of OI adjustment, however, is maintained for a longer period in rural areas where emissions and chemical transformation make a smaller contribution to the O3 budget than that in high emission areas. Following the assessment of individual forecast system updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations, and emission adjustment, to simulate the high ozone episode over the Long Island Sound region. The newly developed forecasting system significantly reduces the bias of surface NO2 concentration. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), the new system is able to reproduce the NO2 VCD with a higher correlation (0.74), lower normalized mean bias (40 %) and normalized mean error (61 %) than NAQFC (0.57, 45 % and 76 %, respectively). The new system captures magnitude and timing of surface O3 peaks and valleys better. In comparison with LIDAR O3 profile variability of the vertical O3 is captured better by the new system (correlation coefficient of 0.71) than by NAQFC (correlation coefficient of 0.54). Although the experiments are limited to one pollution episode over the Long Island Sound, this study demonstrates feasible approaches to improve the predictability of high O3 episodes in contemporary urban environments.

Siqi Ma et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2020-1291', Anonymous Referee #1, 30 Mar 2021
  • RC2: 'Comment on acp-2020-1291', Anonymous Referee #2, 07 Apr 2021

Siqi Ma et al.

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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.
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