Articles | Volume 12, issue 12
Atmos. Meas. Tech., 12, 6619–6634, 2019
https://doi.org/10.5194/amt-12-6619-2019

Special issue: TROPOMI on Sentinel-5 Precursor: first year in operation (AMT/ACP...

Atmos. Meas. Tech., 12, 6619–6634, 2019
https://doi.org/10.5194/amt-12-6619-2019

Research article 13 Dec 2019

Research article | 13 Dec 2019

A neural network radiative transfer model approach applied to the Tropospheric Monitoring Instrument aerosol height algorithm

Swadhin Nanda et al.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Swadhin Nanda on behalf of the Authors (12 Jul 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (31 Jul 2019) by Jhoon Kim
RR by Anonymous Referee #3 (09 Sep 2019)
ED: Publish subject to minor revisions (review by editor) (21 Sep 2019) by Jhoon Kim
AR by Swadhin Nanda on behalf of the Authors (25 Sep 2019)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (13 Oct 2019) by Jhoon Kim
AR by Swadhin Nanda on behalf of the Authors (20 Oct 2019)  Author's response    Manuscript
ED: Publish as is (03 Nov 2019) by Jhoon Kim
Short summary
This paper discusses a neural network forward model used by the operational aerosol layer height (ALH) retrieval algorithm for the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Sentinel-5 Precursor satellite mission. This model replaces online radiative transfer calculations within the oxygen A-band, improving the speed of the algorithm by 3 orders of magnitude. With this advancement in the algorithm's speed, TROPOMI is set to deliver the ALH product operationally.