Articles | Volume 14, issue 5
Atmos. Meas. Tech., 14, 3673–3691, 2021
https://doi.org/10.5194/amt-14-3673-2021
Atmos. Meas. Tech., 14, 3673–3691, 2021
https://doi.org/10.5194/amt-14-3673-2021
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 et al.

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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
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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.