Influence of satellite-derived photolysis rates and NO<sub>x</sub> emissions on Texas ozone modeling

Abstract

Abstract. Uncertain photolysis rates and emission inventory impair the accuracy of state-level ozone (O3) regulatory modeling. Past studies have separately used satellite-observed clouds to correct the model-predicted photolysis rates, or satellite-constrained top-down NOx emissions to identify and reduce uncertainties in bottom-up NOx emissions. However, the joint application of multiple satellite-derived model inputs to improve O3 state implementation plan (SIP) modeling has rarely been explored. In this study, Geostationary Operational Environmental Satellite (GOES) observations of clouds are applied to derive the photolysis rates, replacing those used in Texas SIP modeling. This changes modeled O3 concentrations by up to 80 ppb and improves O3 simulations by reducing modeled normalized mean bias (NMB) and normalized mean error (NME) by up to 0.1. A sector-based discrete Kalman filter (DKF) inversion approach is incorporated with the Comprehensive Air Quality Model with extensions (CAMx)–decoupled direct method (DDM) model to adjust Texas NOx emissions using a high-resolution Ozone Monitoring Instrument (OMI) NO2 product. The discrepancy between OMI and CAMx NO2 vertical column densities (VCDs) is further reduced by increasing modeled NOx lifetime and adding an artificial amount of NO2 in the upper troposphere. The region-based DKF inversion suggests increasing NOx emissions by 10–50% in most regions, deteriorating the model performance in predicting ground NO2 and O3, while the sector-based DKF inversion tends to scale down area and nonroad NOx emissions by 50%, leading to a 2–5 ppb decrease in ground 8 h O3 predictions. Model performance in simulating ground NO2 and O3 are improved using sector-based inversion-constrained NOx emissions, with 0.25 and 0.04 reductions in NMBs and 0.13 and 0.04 reductions in NMEs, respectively. Using both GOES-derived photolysis rates and OMI-constrained NOx emissions together reduces modeled NMB and NME by 0.05, increases the model correlation with ground measurement in O3 simulations, and makes O3 more sensitive to NOx emissions in the O3 non-attainment areas.