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Satellite remote sensing of ozone using a Full-Physics Inverse Learning Machine

Xu, Jian and Heue, Klaus-Peter and Loyola, Diego and Efremenko, Dmitry (2019) Satellite remote sensing of ozone using a Full-Physics Inverse Learning Machine. In: 2019 Conference on Big Data from Space (BiDS'19), pp. 165-168. Joint Research Centre (JRC). The 2019 Conference on Big Data from Space (BiDS'19), 19.-21. Feb. 2019, Munich, Germany. DOI: 10.2760/848593 ISBN 978-92-76-00034-1 ISSN 1831-9424

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Official URL: https://www.bigdatafromspace2019.org/QuickEventWebsitePortal/2019-conference-on-big-data-from-space-bids19/bids-2019

Abstract

The new generation of environmental satellites with increased spatial and spectral resolutions imposes critical challenges for the processing of the Big Data. This work employs the newly-developed full-physics inverse learning machine (FP-ILM) to estimate vertical distributions of ozone from Global Ozone Monitoring Experiment - 2 (GOME-2) measurements and analyzed its performance. The obtained ozone profile shapes are further used to derive the vertical column density of ozone. The main advantage of FP-ILM is that, unlike classical retrieval algorithms, the ozone profile retrieval is formulated as a classification problem, producing a significant speed-up and reliable accuracy. The time-consuming radiative transfer computations and neural network training are performed off-line and do not introduce additional performance bottlenecks in the whole processing chain. Therefore FP-ILMs are suitable for processing remote sensing Big Data.

Item URL in elib:https://elib.dlr.de/126642/
Document Type:Conference or Workshop Item (Poster)
Title:Satellite remote sensing of ozone using a Full-Physics Inverse Learning Machine
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Xu, Jianjian.xu (at) dlr.dehttps://orcid.org/0000-0003-2348-125X
Heue, Klaus-PeterKlaus-Peter.Heue (at) dlr.dehttps://orcid.org/0000-0001-8823-7712
Loyola, DiegoDiego.Loyola (at) dlr.dehttps://orcid.org/0000-0002-8547-9350
Efremenko, DmitryDmitry.Efremenko (at) dlr.deUNSPECIFIED
Date:2019
Journal or Publication Title:2019 Conference on Big Data from Space (BiDS'19)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.2760/848593
Page Range:pp. 165-168
Editors:
EditorsEmail
Soille, PierreUNSPECIFIED
Loekken, SveinungUNSPECIFIED
Albani, SergioUNSPECIFIED
Publisher:Joint Research Centre (JRC)
ISSN:1831-9424
ISBN:978-92-76-00034-1
Status:Published
Keywords:Atmospheric remote sensing, ozone, FP-ILM, machine learning
Event Title:The 2019 Conference on Big Data from Space (BiDS'19)
Event Location:Munich, Germany
Event Type:international Conference
Event Dates:19.-21. Feb. 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience, Vorhaben Spectroscopic Methods in Remote Sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Xu, Dr.-Ing. Jian
Deposited On:25 Feb 2019 11:54
Last Modified:25 Feb 2019 11:58

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