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Abstract. Although air quality in the United States has improved remarkably inthe past decades, ground-level ozone (O3) often rises in exceedance ofthe national ambient air quality standard in nonattainment areas, includingthe Long Island Sound (LIS) and its surrounding areas. Accurate predictionof high-ozone episodes is needed to assist government agencies and thepublic in mitigating harmful effects of air pollution. In this study, wehave developed a suite of potential forecast improvements, including dynamicboundary conditions, rapid emission refresh and chemical data assimilation,in a 3 km resolution Community Multiscale Air Quality (CMAQ) modelingsystem. The purpose is to evaluate and assess the effectiveness of theseforecasting techniques, individually or in combination, in improvingforecast guidance for two major air pollutants: surface O3 and nitrogendioxide (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 LISTOSfield 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.

atmospheric chemistry and physics

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