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Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA

Ionospheric total electron content (TEC) derived from multi-frequency Global Navigation Satellite System (GNSS) signals and the relevant products have become one of the most utilized parameters in the space weather and ionospheric research community. However, there are a couple of challenges in usin...

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Autores principales: Sun, Hu, Chen, Yang, Zou, Shasha, Ren, Jiaen, Chang, Yurui, Wang, Zihan, Coster, Anthea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130027/
https://www.ncbi.nlm.nih.gov/pubmed/37185767
http://dx.doi.org/10.1038/s41597-023-02138-7
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author Sun, Hu
Chen, Yang
Zou, Shasha
Ren, Jiaen
Chang, Yurui
Wang, Zihan
Coster, Anthea
author_facet Sun, Hu
Chen, Yang
Zou, Shasha
Ren, Jiaen
Chang, Yurui
Wang, Zihan
Coster, Anthea
author_sort Sun, Hu
collection PubMed
description Ionospheric total electron content (TEC) derived from multi-frequency Global Navigation Satellite System (GNSS) signals and the relevant products have become one of the most utilized parameters in the space weather and ionospheric research community. However, there are a couple of challenges in using the global TEC map data including large data gaps over oceans and the potential of losing meso-scale ionospheric structures when applying traditional reconstruction and smoothing algorithms. In this paper, we describe and release a global TEC map database, constructed and completed based on the Madrigal TEC database with a novel video imputation algorithm called VISTA (Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data). The complete TEC maps reveal important large-scale TEC structures and preserve the observed meso-scale structures. Basic ideas and the pipeline of the video imputation algorithm are introduced briefly, followed by discussions on the computational costs and fine tuning of the adopted algorithm. Discussions on potential usages of the complete TEC database are given, together with a concrete example of applying this database.
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spelling pubmed-101300272023-04-27 Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA Sun, Hu Chen, Yang Zou, Shasha Ren, Jiaen Chang, Yurui Wang, Zihan Coster, Anthea Sci Data Data Descriptor Ionospheric total electron content (TEC) derived from multi-frequency Global Navigation Satellite System (GNSS) signals and the relevant products have become one of the most utilized parameters in the space weather and ionospheric research community. However, there are a couple of challenges in using the global TEC map data including large data gaps over oceans and the potential of losing meso-scale ionospheric structures when applying traditional reconstruction and smoothing algorithms. In this paper, we describe and release a global TEC map database, constructed and completed based on the Madrigal TEC database with a novel video imputation algorithm called VISTA (Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data). The complete TEC maps reveal important large-scale TEC structures and preserve the observed meso-scale structures. Basic ideas and the pipeline of the video imputation algorithm are introduced briefly, followed by discussions on the computational costs and fine tuning of the adopted algorithm. Discussions on potential usages of the complete TEC database are given, together with a concrete example of applying this database. Nature Publishing Group UK 2023-04-25 /pmc/articles/PMC10130027/ /pubmed/37185767 http://dx.doi.org/10.1038/s41597-023-02138-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Sun, Hu
Chen, Yang
Zou, Shasha
Ren, Jiaen
Chang, Yurui
Wang, Zihan
Coster, Anthea
Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA
title Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA
title_full Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA
title_fullStr Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA
title_full_unstemmed Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA
title_short Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA
title_sort complete global total electron content map dataset based on a video imputation algorithm vista
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130027/
https://www.ncbi.nlm.nih.gov/pubmed/37185767
http://dx.doi.org/10.1038/s41597-023-02138-7
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