Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 5
Articles | Volume 14, issue 5
https://doi.org/10.5194/amt-14-3673-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-3673-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 5
Research article
 | 
20 May 2021
Research article |  | 20 May 2021

Volcanic SO2 effective layer height retrieval for the Ozone Monitoring Instrument (OMI) using a machine-learning approach

Nikita M. Fedkin, Can Li, Nickolay A. Krotkov, Pascal Hedelt, Diego G. Loyola, Russell R. Dickerson, and Robert Spurr

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Nikita Fedkin on behalf of the Authors (09 Mar 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (12 Mar 2021) by Helen Worden
RR by Anonymous Referee #1 (29 Mar 2021)
ED: Publish as is (30 Mar 2021) by Helen Worden
AR by Nikita Fedkin on behalf of the Authors (09 Apr 2021)
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Short summary
This study presents a new volcanic sulfur dioxide (SO2) layer height retrieval algorithm for the Ozone Monitoring Instrument (OMI). We generated a large spectral dataset with a radiative transfer model and used it to train neural networks to predict SO2 height from OMI radiance data. The algorithm is fast and takes less than 10 min for a single orbit. Retrievals were tested on four eruption cases, and results had reasonable agreement (within 2 km) with other retrievals and previous studies.
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