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Extremely Fast Retrieval of Volcanic SO2 Layer Heights from UV Satellite Data Using Inverse Learning Machines

Hedelt, Pascal and Koukouli, Maria-Elissavet and Taylor, Isabelle and Balis, Dimitris and Grainger, Don and Inness, Antje and Efremenko, Dmitry S. and Loyola, Diego and Azoulay, Alon (2020) Extremely Fast Retrieval of Volcanic SO2 Layer Heights from UV Satellite Data Using Inverse Learning Machines. AGU 2020, 1.-17.12.2020, San Francisco, USA.

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Abstract

Precise knowledge of the location and height of the volcanic sulfur dioxide (SO2) plume is essential for accurate determination of SO2 emitted by volcanic eruptions. So far, UV based SO2 plume height retrieval algorithms are very time-consuming and therefore not suitable for near-real-time applications like aviation control, although the SO2 LH is essential for accurate determination of SO2 emitted by volcanic eruptions. We have therefore developed the Full-Physics Inverse Learning Machine (FP_ILM) algorithm using a combined principal components analysis (PCA) and neural network approach (NN) to extract the information about the volcanic SO2 LH from high-resolution UV backscatter measurement of TROPOMI aboard Sentinel-5 Precursor. The FP_ILM approach enables for the first time to extract the SO2 LH information in a matter of seconds for an entire S5P orbit and is thus applicable in NRT applications. In this presentation, we will present the FP-ILM algorithm and show results of recent volcanic eruptions. The SO2 layer height product is developed in the framework of the SO2 Layer Height (S5P+I: SO2 LH) project, which is part of ESA Sentinel-5p+ Innovation project (S5P+I). The S5P+I project aims to develop novel scientific and operational products to exploit the potential of the S5P/TROPOMI capabilities. The S5P+I: SO2 LH project is dedicated to the generation of an SO2 LH product and its extensive verification with collocated ground- and space-born measurements.

Item URL in elib:https://elib.dlr.de/137717/
Document Type:Conference or Workshop Item (Speech)
Title:Extremely Fast Retrieval of Volcanic SO2 Layer Heights from UV Satellite Data Using Inverse Learning Machines
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hedelt, PascalPascal.Hedelt (at) dlr.dehttps://orcid.org/0000-0002-1752-0040
Koukouli, Maria-ElissavetAUTHhttps://orcid.org/0000-0002-7509-4027
Taylor, IsabelleAtmospheric, Oceanic and Planetary Physics, Oxford University, U.K.UNSPECIFIED
Balis, DimitrisAristotle University of Thessalonikihttps://orcid.org/0000-0003-1161-7746
Grainger, DonAtmospheric, Oceanic and Planetary Physics, Oxford University, U.K.UNSPECIFIED
Inness, AntjeEuropean Centre for Medium-range Weather Forecasts, ECMWF, Shinfield Park, Reading RG2 9AX, UKUNSPECIFIED
Efremenko, Dmitry S.dmitry.efremenko (at) dlr.dehttps://orcid.org/0000-0002-7449-5072
Loyola, DiegoDiego.Loyola (at) dlr.dehttps://orcid.org/0000-0002-8547-9350
Azoulay, Alonalon.azoulay (at) dlr.deUNSPECIFIED
Date:December 2020
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:SO2LH, Machine Learning, SO2, Volcanoes
Event Title:AGU 2020
Event Location:San Francisco, USA
Event Type:international Conference
Event Dates:1.-17.12.2020
Organizer:AGU
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):Vorhaben Spectroscopic Methods in Remote Sensing (old)
Location: Berlin-Adlershof , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Hedelt, Pascal
Deposited On:19 Nov 2020 09:14
Last Modified:18 Dec 2020 17:31

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