Articles | Volume 9, issue 3
https://doi.org/10.5194/gmd-9-1111-2016
https://doi.org/10.5194/gmd-9-1111-2016
Development and technical paper
 | 
22 Mar 2016
Development and technical paper |  | 22 Mar 2016

OMI NO2 column densities over North American urban cities: the effect of satellite footprint resolution

Hyun Cheol Kim, Pius Lee, Laura Judd, Li Pan, and Barry Lefer

Related authors

Quantitative assessment of changes in surface particulate matter concentrations and precursor emissions over China during the COVID-19 pandemic and their implications for Chinese economic activity
Hyun Cheol Kim, Soontae Kim, Mark Cohen, Changhan Bae, Dasom Lee, Rick Saylor, Minah Bae, Eunhye Kim, Byeong-Uk Kim, Jin-Ho Yoon, and Ariel Stein
Atmos. Chem. Phys., 21, 10065–10080, https://doi.org/10.5194/acp-21-10065-2021,https://doi.org/10.5194/acp-21-10065-2021, 2021
Short summary
Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations
Hyun Cheol Kim, Tianfeng Chai, Ariel Stein, and Shobha Kondragunta
Atmos. Chem. Phys., 20, 10259–10277, https://doi.org/10.5194/acp-20-10259-2020,https://doi.org/10.5194/acp-20-10259-2020, 2020
Short summary
Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign
Li Pan, HyunCheol Kim, Pius Lee, Rick Saylor, YouHua Tang, Daniel Tong, Barry Baker, Shobha Kondragunta, Chuanyu Xu, Mark G. Ruminski, Weiwei Chen, Jeff Mcqueen, and Ivanka Stajner
Geosci. Model Dev., 13, 2169–2184, https://doi.org/10.5194/gmd-13-2169-2020,https://doi.org/10.5194/gmd-13-2169-2020, 2020
Short summary
A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods
Youhua Tang, Mariusz Pagowski, Tianfeng Chai, Li Pan, Pius Lee, Barry Baker, Rajesh Kumar, Luca Delle Monache, Daniel Tong, and Hyun-Cheol Kim
Geosci. Model Dev., 10, 4743–4758, https://doi.org/10.5194/gmd-10-4743-2017,https://doi.org/10.5194/gmd-10-4743-2017, 2017
Short summary
Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign
Li Pan, Hyun Cheol Kim, Pius Lee, Rick Saylor, YouHua Tang, Daniel Tong, Barry Baker, Shobha Kondragunta, Chuanyu Xu, Mark G. Ruminski, Weiwei Chen, Jeff Mcqueen, and Ivanka Stajner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-207,https://doi.org/10.5194/gmd-2017-207, 2017
Revised manuscript not accepted
Short summary

Related subject area

Atmospheric sciences
GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024,https://doi.org/10.5194/gmd-17-399-2024, 2024
Short summary
Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024,https://doi.org/10.5194/gmd-17-431-2024, 2024
Short summary
A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024,https://doi.org/10.5194/gmd-17-381-2024, 2024
Short summary
BoundaryLayerDynamics.jl v1.0: a modern codebase for atmospheric boundary-layer simulations
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024,https://doi.org/10.5194/gmd-17-321-2024, 2024
Short summary
The wave-age-dependent stress parameterisation (WASP) for momentum and heat turbulent fluxes at sea in SURFEX v8.1
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024,https://doi.org/10.5194/gmd-17-117-2024, 2024
Short summary

Cited articles

Beirle, S., Platt, U., Wenig, M., and Wagner, T.: Highly resolved global distribution of tropospheric NO2 using GOME narrow swath mode data, Atmos. Chem. Phys., 4, 1913–1924, https://doi.org/10.5194/acp-4-1913-2004, 2004.
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.
Boersma, K. F., Eskes, H. J., and Brinksma, E. J.: Error analysis for tropospheric NO2 retrieval from space, J. Geophys. Res., 109, D04311, https://doi.org/10.1029/2003JD003962, 2004.
Boersma, K. F., Eskes, H. J., Veefkind, J. P., Brinksma, E. J., van der A, R. J., Sneep, M., van den Oord, G. H. J., Levelt, P. F., Stammes, P., Gleason, J. F., and Bucsela, E. J.: Near-real time retrieval of tropospheric NO2 from OMI, Atmos. Chem. Phys., 7, 2103–2118, https://doi.org/10.5194/acp-7-2103-2007, 2007.
Bucsela, E. J., Perring, A. E., Cohen, R. C., Boersma, K. F., Celarier, E. A., Gleason, J. F., Gleason, J. F., Wenig, M. O., Bertram, T. H., Wooldridge, P. J., Dirksen, R., and Veefkind, J. P.: Comparison of tropospheric NO2 from in situ aircraft measurements with near real-time and standard product data from OMI, J. Geophys. Res., 113, D16S31, https://doi.org/10.1029/2007JD008838, 2008.
Download
Short summary
Fair comparison between satellite- and modeled urban NO2 column densities is important in emission inventory evaluation and regulation policy making. This study focuses on the impact of satellite footprint resolution geometry. Since OMI NO2 pixels are too coarse to resolve fine-scale urban plumes, it may cause 20–30 % bias over major cities. We introduce approaches to adjust spatial and vertical structure (downscaling & averaging kernel), and demonstrate improved agreement between sat. and model.