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A novel ozone profile shape retrieval using full-physics inverse learning machine (FP-ILM)

Xu, Jian and Schüssler, Olena and Loyola, Diego and Romahn, Fabian and Doicu, Adrian (2017) A novel ozone profile shape retrieval using full-physics inverse learning machine (FP-ILM). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (12), pp. 5442-5457. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2017.2740168 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/document/8023748/

Abstract

Identifying ozone profile shapes from nadir-viewing satellite sensors is a critical yet challenging task for accurate reconstruction of vertical distributions of ozone relevant to climate change and air quality. Motivated by the need to develop a methodology to fast, reliably, and efficiently exploit ozone distributions and inspired by the success of machine learning, this paper introduces a novel algorithm for estimating ozone profile shapes from satellite ultraviolet absorption spectra. The Full-Physics Inverse Learning Machine (FP-ILM) algorithm successfully characterizes ozone profile shapes using machine learning approaches. Its implementation mainly consists of a clustering process based on a semi-supervised agglomerative algorithm, a classification process based on full-physics radiative transfer simulations and a neural network (NN), and a profile scaling process based on a NN ensemble. The classification model has been trained with synthetic data generated by a forward model in conjunction with “smart sampling,” while the scaling model corresponding to each cluster requires total ozone information. The main innovation of FP-ILM is that, unlike conventional inversion methods, the ozone profile retrieval is formulated as a classification problem, leading to a noteworthy speed-up and accuracy when dealing with applications of satellite data. An outstanding retrieval performance with errors of less than 10% over 100–1 hPa has been obtained for synthetic measurements. Furthermore, the ozone profiles retrieved from the Global Ozone Monitoring Experiment–2 data using FP-ILM and the optimal estimation method reach an encouraging agreement (the differences are less than 6 Dobson Units or within 5%–20%).

Item URL in elib:https://elib.dlr.de/113958/
Document Type:Article
Title:A novel ozone profile shape retrieval using full-physics inverse learning machine (FP-ILM)
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Xu, Jianjian.xu (at) dlr.dehttps://orcid.org/0000-0003-2348-125X
Schüssler, Olenaolena.schuessler (at) dlr.deUNSPECIFIED
Loyola, DiegoDiego.Loyola (at) dlr.dehttps://orcid.org/0000-0002-8547-9350
Romahn, Fabianfabian.romahn (at) dlr.deUNSPECIFIED
Doicu, Adrianadrian.doicu (at) dlr.deUNSPECIFIED
Date:December 2017
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.1109/JSTARS.2017.2740168
Page Range:pp. 5442-5457
Editors:
EditorsEmail
Du, QianDu@ece.msstate.edu
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Atmospheric composition measurements, machine learning, ozone profiles
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, R - Vorhaben Atmosphären- und Klimaforschung, R - Project Climatic relevance of atmospheric tracer gases, aerosols and clouds
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Xu, Dr.-Ing. Jian
Deposited On:08 Sep 2017 12:47
Last Modified:08 Mar 2018 18:32

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