Improving predictability of high ozone episodes through dynamicboundary conditions, emission refresh and chemical dataassimilation during the Long Island Sound Tropospheric OzoneStudy (LISTOS) field campaign

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.