气候变化领域集成服务门户(CCPortal): 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

CCPortal

DOI	10.5194/acp-21-16531-2021
	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
	Ma S.; Tong D.; Lamsal L.; Wang J.; Zhang X.; Tang Y.; Saylor R.; Chai T.; Lee P.; Campbell P.; Baker B.; Kondragunta S.; Judd L.; Berkoff T.A.; Janz S.J.; Stajner I.
发表日期	2021
ISSN	1680-7316
起始页码	16531
结束页码	16553
卷号	21 期号:21
英文摘要	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, 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 O3 and nitrogen dioxide (NO2). Experiments were conducted for a high-O3 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 O3 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 (R) 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 NO2 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 O3 budget than 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 NO2 prediction. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), this 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 3 km 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. © 2021 Siqi Ma et al.
语种	英语
scopus关键词	air quality; atmospheric pollution; boundary condition; concentration (composition); data assimilation; forecasting method; ozone; prediction; Long Island Sound; United States
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/246448
作者单位	Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030, United States; National Research Council, Hosted by the National Oceanic and Atmospheric Administration Air Resources Lab, College Park, MD 20740, United States; Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, United States; Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight CenterMD 20771, United States; Universities Space Research Association, Columbia, MD 21046, United States; National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, College Park, MD 22030, United States; NOAA National Environmental Satellite Data and Information Service, College Park, MD 20740, United States; NASA Langley Research Center, Hampton, VA 23681, United States; NOAA National Weather Service National Centers for Environmental Prediction, College Park, MD 20740, United States
推荐引用方式 GB/T 7714	Ma S.,Tong D.,Lamsal L.,et al. 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[J],2021,21(21).
APA	Ma S..,Tong D..,Lamsal L..,Wang J..,Zhang X..,...&Stajner I..(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.ATMOSPHERIC CHEMISTRY AND PHYSICS,21(21).
MLA	Ma S.,et al."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 21.21(2021).