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

Related authors

State-wide California 2020 carbon dioxide budget estimated with OCO-2 and OCO-3 satellite data
Matthew S. Johnson, Sofia D. Hamilton, Seongeun Jeong, Yu Yan Cui, Dien Wu, Alex Turner, and Marc Fischer
Atmos. Chem. Phys., 25, 8475–8492, https://doi.org/10.5194/acp-25-8475-2025,https://doi.org/10.5194/acp-25-8475-2025, 2025
Short summary
Theoretical assessment of the ability of the MicroCarb satellite city-scan observing mode to estimate urban CO2 emissions
Kai Wu, Paul I. Palmer, Dien Wu, Denis Jouglet, Liang Feng, and Tom Oda
Atmos. Meas. Tech., 16, 581–602, https://doi.org/10.5194/amt-16-581-2023,https://doi.org/10.5194/amt-16-581-2023, 2023
Short summary
Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO2 and CO
Dien Wu, Junjie Liu, Paul O. Wennberg, Paul I. Palmer, Robert R. Nelson, Matthäus Kiel, and Annmarie Eldering
Atmos. Chem. Phys., 22, 14547–14570, https://doi.org/10.5194/acp-22-14547-2022,https://doi.org/10.5194/acp-22-14547-2022, 2022
Short summary
The Information Content of Dense Carbon Dioxide Measurements from Space: A High-Resolution Inversion Approach with Synthetic Data from the OCO-3 Instrument
Dustin Roten, John C. Lin, Lewis Kunik, Derek Mallia, Dien Wu, Tomohiro Oda, and Eric A. Kort
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-315,https://doi.org/10.5194/acp-2022-315, 2022
Revised manuscript not accepted
Short summary
A model for urban biogenic CO2 fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)
Dien Wu, John C. Lin, Henrique F. Duarte, Vineet Yadav, Nicholas C. Parazoo, Tomohiro Oda, and Eric A. Kort
Geosci. Model Dev., 14, 3633–3661, https://doi.org/10.5194/gmd-14-3633-2021,https://doi.org/10.5194/gmd-14-3633-2021, 2021
Short summary

Related subject area

Atmospheric sciences
Development of the CMA-GFS-AERO 4D-Var assimilation system v1.0 – Part 1: System description and preliminary experimental results
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev., 18, 4855–4876, https://doi.org/10.5194/gmd-18-4855-2025,https://doi.org/10.5194/gmd-18-4855-2025, 2025
Short summary
Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Geosci. Model Dev., 18, 4667–4684, https://doi.org/10.5194/gmd-18-4667-2025,https://doi.org/10.5194/gmd-18-4667-2025, 2025
Short summary
Interpolating turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau using artificial intelligence models
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
Geosci. Model Dev., 18, 4625–4641, https://doi.org/10.5194/gmd-18-4625-2025,https://doi.org/10.5194/gmd-18-4625-2025, 2025
Short summary
Carbon dioxide plume dispersion simulated at the hectometer scale using DALES: model formulation and observational evaluation
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart J. H. van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
Geosci. Model Dev., 18, 4571–4599, https://doi.org/10.5194/gmd-18-4571-2025,https://doi.org/10.5194/gmd-18-4571-2025, 2025
Short summary
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025,https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary

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
Download
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.
Share