Articles | Volume 13, issue 2
Atmos. Meas. Tech., 13, 985–999, 2020
https://doi.org/10.5194/amt-13-985-2020
Atmos. Meas. Tech., 13, 985–999, 2020
https://doi.org/10.5194/amt-13-985-2020

Research article 02 Mar 2020

Research article | 02 Mar 2020

Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) daily maps from UVN satellite measurements

Diego G. Loyola et al.

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Interactive discussion

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 Anna Mirena Feist-Polner on behalf of the Authors (27 Nov 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (27 Nov 2019) by Michel Van Roozendael
RR by Anonymous Referee #1 (12 Dec 2019)
RR by Anonymous Referee #2 (13 Dec 2019)
ED: Publish subject to minor revisions (review by editor) (14 Dec 2019) by Michel Van Roozendael
AR by Diego Loyola on behalf of the Authors (19 Dec 2019)  Author's response
ED: Publish as is (14 Jan 2020) by Michel Van Roozendael
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Short summary
In this paper we present a novel algorithm for the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) from UVN sensors based on the full-physics inverse learning machine (FP_ILM) retrieval. The GE_LER retrieval is optimized for the trace gas retrievals using the DOAS technique and the large amount of data of TROPOMI on board the EU/ESA Sentinel-5 Precursor mission.