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Neural network aided fast pointing information determination approach for occultation payloads from in-flight measurements: Algorithm design and assessment

Zhu, Songyan and Li, Xiaoying and Xu, Jian and Cheng, Tianhai and Zhang, Xingying and Wang, Hongmei and Wang, Yapeng and Miao, Jing (2019) Neural network aided fast pointing information determination approach for occultation payloads from in-flight measurements: Algorithm design and assessment. Advances in Space Research, 63 (8), pp. 2323-2336. Elsevier. DOI: 10.1016/j.asr.2019.01.041 ISSN 0273-1177

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Official URL: https://www.sciencedirect.com/science/article/pii/S0273117719300766

Abstract

Pointing information is decisive to solving precise profile retrieval issues from occultation measurements. Research regarding tratospheric O3 hole in Antarctic and surface O3 pollution would significantly benefit from massive occultation measurements. A neural network aided pointing information determination approach, in terms of tangent heights, is proposed to address issues requiring fast and easy-to-use determined tangent heights. The geometrical triangular iteration (GTI) algorithm in this work is based on N2 absorption microwindows, and several treatments (e.g., tangential stride generator and triangular-net optimization) are adopted. In addition, LSTM is employed to reduce time consumption and increase accuracy. Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) in-flight measurements are used to assess this approach. The comparison between the proposed algorithm and eight official products indicates a promising performance. Correlation coefficient for each orbit is greater than 0.99. The processing time is about 16.6 min per orbit with an average cost of 0.06. The introduction of LSTM technique demonstrates an approximate 28.49% better result, with less computation time. It costed less than 30 s to determine eight orbit tangent heights. In general, although minor issues remain, this LSTM-aided GTI algorithm is applicable in industry.

Item URL in elib:https://elib.dlr.de/126832/
Document Type:Article
Title:Neural network aided fast pointing information determination approach for occultation payloads from in-flight measurements: Algorithm design and assessment
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Zhu, SongyanChina Centre for Resources Satellite Data and ApplicationUNSPECIFIED
Li, XiaoyingState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesUNSPECIFIED
Xu, Jianjian.xu (at) dlr.dehttps://orcid.org/0000-0003-2348-125X
Cheng, TianhaiState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesUNSPECIFIED
Zhang, XingyingNational Satellite Meteorological Center, China Meteorological AdministrationUNSPECIFIED
Wang, HongmeiState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesUNSPECIFIED
Wang, YapengState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesUNSPECIFIED
Miao, JingState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesUNSPECIFIED
Date:April 2019
Journal or Publication Title:Advances in Space Research
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:63
DOI :10.1016/j.asr.2019.01.041
Page Range:pp. 2323-2336
Editors:
EditorsEmailEditor's ORCID iD
Willis, Pascalpascal.willis@ipgp.frUNSPECIFIED
Publisher:Elsevier
ISSN:0273-1177
Status:Published
Keywords:Occultation observation, Pointing information, N2 absorption, Tangential strides, Triangular iteration, LSTM
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
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Xu, Dr.-Ing. Jian
Deposited On:15 Mar 2019 13:13
Last Modified:01 May 2020 03:00

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