Influence of satellite-derived photolysis rates and NO_x emissions on Texas ozone modeling

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 NO_x emissions to identify and reduce uncertainties in bottom-up NO_x 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 NO_x 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 NO_x lifetime and adding an artificial amount of NO₂ in the upper troposphere. The region-based DKF inversion suggests increasing NO_x 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 NO_x 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 NO_x 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 NO_x 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 NO_x emissions in the O₃ non-attainment areas.