Beyond binary maps from HCHO∕NO 2 : a deep neural network approach to global daily mapping of net ozone production rates and sensitivities constrained by satellite observations (2005–2023)

Abstract

Abstract. Previous studies on net ozone production rates (PO3) and their sensitivities to precursors relied on limited in-situ data, often coarse and uncertain chemical transport models (CTMs), and ozone indicators like the formaldehyde-to-nitrogen dioxide ratio (FNR). However, FNR fails to fully capture PO3's complex relationships with pollution, light, and water vapor. To address this, we refine the satellite-based PO3 product from Souri et al. (2025) with key advancements: (i) a deep neural network to parametrize high-dimensional non-linear ozone chemistry without the need for empirical linearization of atmospheric conditions, (ii) incorporation of water vapor, (iii) improved error characterization, and (iv) the application of a finer CTM to dynamically convert column retrievals into near-surface mixing ratios. Our PO3 sensitivity maps surpass traditional FNR-based assessments by quantifying sensitivity magnitudes – factoring in photolysis rates and water vapor – with greater spatial information. Our new product provides daily near-clear sky PO3 and sensitivity maps using bias-corrected OMI (2005–2019, 0.25° × 0.25°) and TROPOMI (2018–2023, 0.1° × 0.1°), with values aligning within 10 %. High PO3 rates (> 8 ppbv h−1) appear in urban and biomass-burning regions under strong photochemical activity, including during a heatwave in the northeastern U.S. Photolysis rates are the dominant factor dictating the seasonality of PO3 magnitudes and sensitivities. The stability and long-term records of OMI retrievals (2005–2019) enable us to provide the first global maps of PO3 linear trends showing a surge of > 30 % over China, the Middle East, and India, while a reduction in the eastern U.S., southern Europe, and several regions in Africa.