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
<p>Although air quality in the United States has improved remarkably in the past decades, ground-level ozone (O<span class="inline-formula"><sub>3</sub>)</span> often rises in exceedance of the national ambient air quality standard in nonattainment areas, incl...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-11-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/21/16531/2021/acp-21-16531-2021.pdf |
| _version_ | 1819294072467095552 |
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| author | S. Ma S. Ma D. Tong D. Tong L. Lamsal L. Lamsal J. Wang X. Zhang Y. Tang Y. Tang R. Saylor T. Chai P. Lee P. Campbell P. Campbell B. Baker B. Baker S. Kondragunta L. Judd T. A. Berkoff S. J. Janz I. Stajner |
| author_facet | S. Ma S. Ma D. Tong D. Tong L. Lamsal L. Lamsal J. Wang X. Zhang Y. Tang Y. Tang R. Saylor T. Chai P. Lee P. Campbell P. Campbell B. Baker B. Baker S. Kondragunta L. Judd T. A. Berkoff S. J. Janz I. Stajner |
| author_sort | S. Ma |
| collection | DOAJ |
| description | <p>Although air quality in the United States has improved remarkably in
the past decades, ground-level ozone (O<span class="inline-formula"><sub>3</sub>)</span> often rises 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 Multiscale 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 O<span class="inline-formula"><sub>3</sub></span> and nitrogen
dioxide (NO<span class="inline-formula"><sub>2</sub>)</span>. Experiments were conducted for a high-O<span class="inline-formula"><sub>3</sub></span> episode
(28–29 August 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 this 3 km forecasting model with varying effectiveness for different pollutants. For O<span class="inline-formula"><sub>3</sub></span> prediction, the most significant improvement comes from the dynamic boundary conditions derived from the NOAA operational forecast system, National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (<span class="inline-formula"><i>R</i></span>) from 0.81 to 0.93 and reduces the root mean square error (RMSE) from 14.97 to 8.22 ppbv, compared to that with the static boundary conditions (BCs). The NO<span class="inline-formula"><sub>2</sub></span> from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, regardless of the BCs used, 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 O<span class="inline-formula"><sub>3</sub></span> budget than<span id="page16532"/> that in high-emission areas. Following the assessment of individual updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations and emission adjustment, to simulate a high-ozone episode during the 2018 LISTOS
field campaign. The newly developed forecasting system significantly reduces the bias of surface NO<span class="inline-formula"><sub>2</sub></span> prediction. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), this system is able to reproduce the NO<span class="inline-formula"><sub>2</sub></span> 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 3 km system captures magnitude and timing of surface O<span class="inline-formula"><sub>3</sub></span> peaks and valleys better. In comparison with lidar, O<span class="inline-formula"><sub>3</sub></span> profile variability of the vertical O<span class="inline-formula"><sub>3</sub></span> 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-O<span class="inline-formula"><sub>3</sub></span> episodes in contemporary urban environments.</p> |
| first_indexed | 2024-12-24T04:20:30Z |
| format | Article |
| id | doaj.art-3ba2ae4c3fdc4e0ab30117a3d13e7e3b |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-24T04:20:30Z |
| publishDate | 2021-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-3ba2ae4c3fdc4e0ab30117a3d13e7e3b2022-12-21T17:15:49ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242021-11-0121165311655310.5194/acp-21-16531-2021Improving 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 campaignS. Ma0S. Ma1D. Tong2D. Tong3L. Lamsal4L. Lamsal5J. Wang6X. Zhang7Y. Tang8Y. Tang9R. Saylor10T. Chai11P. Lee12P. Campbell13P. Campbell14B. Baker15B. Baker16S. Kondragunta17L. Judd18T. A. Berkoff19S. J. Janz20I. Stajner21Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030, USANational Research Council, hosted by the National Oceanic and Atmospheric Administration Air Resources Lab, College Park, MD 20740, USADepartment of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USAAtmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, MD 20771, USAUniversities Space Research Association, Columbia, MD 21046, USANational Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, College Park, MD 22030, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USANational Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, College Park, MD 22030, USANational Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, College Park, MD 22030, USAAtmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, MD 20771, USANational Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, College Park, MD 22030, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USANational Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, College Park, MD 22030, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USANational Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, College Park, MD 22030, USANOAA National Environmental Satellite Data and Information Service, College Park, MD 20740, USANASA Langley Research Center, Hampton, VA 23681, USANASA Langley Research Center, Hampton, VA 23681, USAAtmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, MD 20771, USANOAA National Weather Service National Centers for Environmental Prediction, College Park, MD 20740, USA<p>Although air quality in the United States has improved remarkably in the past decades, ground-level ozone (O<span class="inline-formula"><sub>3</sub>)</span> often rises 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 Multiscale 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 O<span class="inline-formula"><sub>3</sub></span> and nitrogen dioxide (NO<span class="inline-formula"><sub>2</sub>)</span>. Experiments were conducted for a high-O<span class="inline-formula"><sub>3</sub></span> episode (28–29 August 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 this 3 km forecasting model with varying effectiveness for different pollutants. For O<span class="inline-formula"><sub>3</sub></span> prediction, the most significant improvement comes from the dynamic boundary conditions derived from the NOAA operational forecast system, National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (<span class="inline-formula"><i>R</i></span>) from 0.81 to 0.93 and reduces the root mean square error (RMSE) from 14.97 to 8.22 ppbv, compared to that with the static boundary conditions (BCs). The NO<span class="inline-formula"><sub>2</sub></span> from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, regardless of the BCs used, 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 O<span class="inline-formula"><sub>3</sub></span> budget than<span id="page16532"/> that in high-emission areas. Following the assessment of individual updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations and emission adjustment, to simulate a high-ozone episode during the 2018 LISTOS field campaign. The newly developed forecasting system significantly reduces the bias of surface NO<span class="inline-formula"><sub>2</sub></span> prediction. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), this system is able to reproduce the NO<span class="inline-formula"><sub>2</sub></span> 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 3 km system captures magnitude and timing of surface O<span class="inline-formula"><sub>3</sub></span> peaks and valleys better. In comparison with lidar, O<span class="inline-formula"><sub>3</sub></span> profile variability of the vertical O<span class="inline-formula"><sub>3</sub></span> 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-O<span class="inline-formula"><sub>3</sub></span> episodes in contemporary urban environments.</p>https://acp.copernicus.org/articles/21/16531/2021/acp-21-16531-2021.pdf |
| spellingShingle | S. Ma S. Ma D. Tong D. Tong L. Lamsal L. Lamsal J. Wang X. Zhang Y. Tang Y. Tang R. Saylor T. Chai P. Lee P. Campbell P. Campbell B. Baker B. Baker S. Kondragunta L. Judd T. A. Berkoff S. J. Janz I. Stajner 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 Atmospheric Chemistry and Physics |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://acp.copernicus.org/articles/21/16531/2021/acp-21-16531-2021.pdf |
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