Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-483-2025
https://doi.org/10.5194/gmd-18-483-2025
Development and technical paper
 | 
28 Jan 2025
Development and technical paper |  | 28 Jan 2025

Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation

Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling

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TROPOMI Level 3 tropospheric NO2 Dataset with Advanced Uncertainty Analysis from the ESA CCI+ ECV Precursor Project
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-616,https://doi.org/10.5194/essd-2024-616, 2025
Revised manuscript accepted for ESSD
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Cited articles

Bédard, J. and Buehner, M.: A practical assimilation approach to extract smaller-scale information from observations with spatially correlated errors: An idealized study, Q. J. Roy. Meteor. Soc., 146, 468–482, https://doi.org/10.1002/qj.3687, 2020. a
Berger, H. and Forsythe, M.: Satellite Wind Superobbing, Tech. rep., Met Office, Exeter UK, https://digital.nmla.metoffice.gov.uk/download/file/IO_ea4b7eb9-cac6-4519-9253-cdcddf38bdba (last access: December 2023), 2003. a, b
Boersma, K. F., Eskes, H. J., and Brinksma, E. J.: Error analysis for tropospheric NO2 retrieval from space, J. Geophys. Res.-Atmos., 109, D04311, https://doi.org/10.1029/2003JD003962, 2004. a, b, c, d
Boersma, K. F., Eskes, H. J., Dirksen, R. J., van der A, R. J., Veefkind, J. P., Stammes, P., Huijnen, V., Kleipool, Q. L., Sneep, M., Claas, J., Leitão, J., Richter, A., Zhou, Y., and Brunner, D.: An improved tropospheric NO2 column retrieval algorithm for the Ozone Monitoring Instrument, Atmos. Meas. Tech., 4, 1905–1928, https://doi.org/10.5194/amt-4-1905-2011, 2011. a, b
Boersma, K. F., Vinken, G. C. M., and Eskes, H. J.: Representativeness errors in comparing chemistry transport and chemistry climate models with satellite UV–Vis tropospheric column retrievals, Geosci. Model Dev., 9, 875–898, https://doi.org/10.5194/gmd-9-875-2016, 2016. a, b, c, d, e, f, g
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Short summary
Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.

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