Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-7297-2021
https://doi.org/10.5194/amt-14-7297-2021
Research article
 | 
22 Nov 2021
Research article |  | 22 Nov 2021

An improved TROPOMI tropospheric NO2 research product over Europe

Song Liu, Pieter Valks, Gaia Pinardi, Jian Xu, Ka Lok Chan, Athina Argyrouli, Ronny Lutz, Steffen Beirle, Ehsan Khorsandi, Frank Baier, Vincent Huijnen, Alkiviadis Bais, Sebastian Donner, Steffen Dörner, Myrto Gratsea, François Hendrick, Dimitris Karagkiozidis, Kezia Lange, Ankie J. M. Piters, Julia Remmers, Andreas Richter, Michel Van Roozendael, Thomas Wagner, Mark Wenig, and Diego G. Loyola

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Cited articles

Bauwens, M., Compernolle, S., Stavrakou, T., Müller, J.-F., van Gent, J., Eskes, H., Levelt, P. F., van der A, R., Veefkind, J. P., Vlietinck, J., Yu, H., and Zehner, C.: Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations, Geophys. Res. Lett., 47, e2020GL087978, https://doi.org/10.1029/2020GL087978, 2020. a
Beirle, S., Hörmann, C., Jöckel, P., Liu, S., Penning de Vries, M., Pozzer, A., Sihler, H., Valks, P., and Wagner, T.: The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO2 from nadir-viewing satellites by weighted convolution, 9, 2753–2779, https://doi.org/10.1126/sciadv.aax9800, 2016. a, b, c, d, e, f, g
Beirle, S., Borger, C., Dörner, S., Li, A., Hu, Z., Liu, F., Wang, Y., and Wagner, T.: Pinpointing nitrogen oxide emissions from space, Sci. Adv., 5, eaax9800, https://doi.org/10.1126/sciadv.aax9800, 2019. a
Belmonte Rivas, M., Veefkind, P., Boersma, F., Levelt, P., Eskes, H., and Gille, J.: Intercomparison of daytime stratospheric NO2 satellite retrievals and model simulations, Atmos. Meas. Tech., 7, 2203–2225, https://doi.org/10.5194/amt-7-2203-2014, 2014. a
Bergemann, C., Meyer-Arnek, J., and Baier, F.: Estimation and causes of uncertainty of air quality forecasts for the Blackforest region, in: Wiss. Mitteil. Inst. f. Meteorol. Univ. Leipzig Band 49(2012), Tagungsband der METTOOLS VIII, Leipzig, Germany, 2012. a, b
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Short summary
In this work, an improved tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented with correction for the dependency of the stratospheric NO2 on the viewing geometry. The AMF calculation is implemented using improved surface albedo, a priori NO2 profiles, and cloud correction. The improved tropospheric NO2 data show good correlations with ground-based MAX-DOAS measurements.