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

Although air quality in the United States has improved remarkably in the past decades, ground-level ozone (O3) often rises in exceedance of the national ambient air quality standard in nonattainment areas, incl...

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Autores principales:	S. Ma, D. Tong, L. Lamsal, J. Wang, X. Zhang, Y. Tang, R. Saylor, T. Chai, P. Lee, P. Campbell, B. Baker, S. Kondragunta, L. Judd, T. A. Berkoff, S. J. Janz, I. Stajner
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/3ba2ae4c3fdc4e0ab30117a3d13e7e3b
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id	oai:doaj.org-article:3ba2ae4c3fdc4e0ab30117a3d13e7e3b
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Chemistry QD1-999
spellingShingle	Physics QC1-999 Chemistry QD1-999 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
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>
format	article
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
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_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_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_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
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/3ba2ae4c3fdc4e0ab30117a3d13e7e3b
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spelling	oai:doaj.org-article:3ba2ae4c3fdc4e0ab30117a3d13e7e3b2021-11-11T15:23:38ZImproving 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 campaign10.5194/acp-21-16531-20211680-73161680-7324https://doaj.org/article/3ba2ae4c3fdc4e0ab30117a3d13e7e3b2021-11-01T00:00:00Zhttps://acp.copernicus.org/articles/21/16531/2021/acp-21-16531-2021.pdfhttps://doaj.org/toc/1680-7316https://doaj.org/toc/1680-7324<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>S. MaS. MaD. TongD. TongL. LamsalL. LamsalJ. WangX. ZhangY. TangY. TangR. SaylorT. ChaiP. LeeP. CampbellP. CampbellB. BakerB. BakerS. KondraguntaL. JuddT. A. BerkoffS. J. JanzI. StajnerCopernicus PublicationsarticlePhysicsQC1-999ChemistryQD1-999ENAtmospheric Chemistry and Physics, Vol 21, Pp 16531-16553 (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

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