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Volcanic SO2 Effective Layer Height Retrieval for OMI Using a Machine Learning Approach

Fedkin, Nikita and Li, Can and Krotkov, Nickolay and Hedelt, Pascal and Loyola, Diego and Dickerson, Russell R. and Spurr, Robert (2021) Volcanic SO2 Effective Layer Height Retrieval for OMI Using a Machine Learning Approach. Atmospheric Measurement Techniques (AMT), 14 (5), pp. 3673-3691. Copernicus Publications. doi: 10.5194/amt-14-3673-2021. ISSN 1867-1381.

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Official URL: https://amt.copernicus.org/articles/14/3673/2021/


Information about the height and loading of sulfur dioxide (SO2) plumes from volcanic eruptions is crucial for aviation safety and for assessing the effect of sulfate aerosols on climate. While SO2 layer height has been successfully retrieved from backscattered Earthshine ultraviolet (UV) radiances measured by the Ozone Monitoring Instrument (OMI), previously demonstrated techniques are computationally intensive and not suitable for near-real-time applications. In this study, we introduce a new OMI algorithm for fast retrievals of effective volcanic SO2 layer height. We apply the Full-Physics Inverse Learning Machine (FP_ILM) algorithm to OMI radiances in the spectral range of 310–330 nm. This approach consists of a training phase that utilizes extensive radiative transfer calculations to generate a large dataset of synthetic radiance spectra for geophysical parameters representing the OMI measurement conditions. The principal components of the spectra from this dataset in addition to a few geophysical parameters are used to train a neural network to solve the inverse problem and predict the SO2 layer height. This is followed by applying the trained inverse model to real OMI measurements to retrieve the effective SO2 plume heights. The algorithm has been tested on several major eruptions during the OMI data record. The results for the 2008 Kasatochi, 2014 Kelud, 2015 Calbuco, and 2019 Raikoke eruption cases are presented here and compared with volcanic plume heights estimated with other satellite sensors. For the most part, OMI-retrieved effective SO2 heights agree well with the lidar measurements of aerosol layer height from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and thermal infrared retrievals of SO2 heights from the infrared atmospheric sounding interferometer (IASI). The errors in OMI-retrieved SO2 heights are estimated to be 1–1.5 km for plumes with relatively large SO2 signals (>40 DU). The algorithm is very fast and retrieves plume height in less than 10 min for an entire OMI orbit.

Item URL in elib:https://elib.dlr.de/137725/
Document Type:Article
Title:Volcanic SO2 Effective Layer Height Retrieval for OMI Using a Machine Learning Approach
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Fedkin, NikitaDepartment of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USAUNSPECIFIEDUNSPECIFIED
Li, CanNASA Goddard Space Flight Center, Greenbelt, MD 20771, USAUNSPECIFIEDUNSPECIFIED
Krotkov, NickolayUniv. of Maryland at Baltimore County, Baltimore, MD USAhttps://orcid.org/0000-0001-6170-6750UNSPECIFIED
Hedelt, PascalUNSPECIFIEDhttps://orcid.org/0000-0002-1752-0040UNSPECIFIED
Loyola, DiegoUNSPECIFIEDhttps://orcid.org/0000-0002-8547-9350UNSPECIFIED
Dickerson, Russell R.Univ. Maryland, College Park, MD, USAhttps://orcid.org/0000-0003-0206-3083UNSPECIFIED
Spurr, RobertRT Solutions, Cambridge, MA, USAUNSPECIFIEDUNSPECIFIED
Date:20 May 2021
Journal or Publication Title:Atmospheric Measurement Techniques (AMT)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:pp. 3673-3691
Publisher:Copernicus Publications
Keywords:Volcanoes, SO2, SO2 Layer height, Machine learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Spectroscopic methods of the atmosphere
Location: Berlin-Adlershof , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Hedelt, Pascal
Deposited On:19 Nov 2020 11:21
Last Modified:28 May 2021 16:59

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