Articles | Volume 16, issue 21
https://doi.org/10.5194/gmd-16-6161-2023
https://doi.org/10.5194/gmd-16-6161-2023
Model description paper
 | 
01 Nov 2023
Model description paper |  | 01 Nov 2023

A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx

Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg

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Cited articles

Beirle, S., Boersma, K. F., Platt, U., Lawrence, M. G., and Wagner, T.: Megacity emissions and lifetimes of nitrogen oxides probed from space, Science, 333, 1737–1739, https://doi.org/10.1126/science.1207824, 2011. a, b, c
Beirle, S., Borger, C., Dörner, S., Li, A., Hu, Z., Liu, F., Wang, Y., and Wagner, T.: Pinpointing nitrogen oxide emissions from space, Science Advances, 5, eaax9800, https://doi.org/10.1126/sciadv.aax9800, 2019. a
Beirle, S., Borger, C., Dörner, S., Eskes, H., Kumar, V., de Laat, A., and Wagner, T.: Catalog of NOx emissions from point sources as derived from the divergence of the NO2 flux for TROPOMI, Earth Syst. Sci. Data, 13, 2995–3012, https://doi.org/10.5194/essd-13-2995-2021, 2021. a
Brunner, D.: Atmospheric chemistry in lagrangian models – overview, in: Lagrangian Modeling of the Atmosphere, edited by: Lin, J. C., Brunner, D., Gerbig, C., Stohl, A., Luchar, A., and Webley, P., Geophysical Monograph Series, 200, https://doi.org/10.1029/2012GM001431, 2012. a
Buchholz, R., Emmons, L., and Tilmes, S.: The CESM2 Development Team: CESM2.1/CAM-chem Instantaneous Output for Boundary Conditions, UCAR/NCAR–Atmospheric Chemistry Observations and Modeling Laboratory, Subset used January 2020–December 2020, 2019. a
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Short summary
To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and assess its performance against TROPOMI v2 over power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind directions and prior emissions.