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Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13
ABSTRACT: We have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7878246/ https://www.ncbi.nlm.nih.gov/pubmed/33628082 http://dx.doi.org/10.1186/s40623-020-01324-w |
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author | Tangborn, Andrew Kuang, Weijia Sabaka, Terence J. Yi, Ce |
author_facet | Tangborn, Andrew Kuang, Weijia Sabaka, Terence J. Yi, Ce |
author_sort | Tangborn, Andrew |
collection | PubMed |
description | ABSTRACT: We have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590–1960), CM4 (1961–2000) and CM6 (2001–2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020–2025 time period. This forecast has been submitted as a candidate predictive model of IGRF-13 for the period 2020–2025. GRAPHICAL ABSTRACT: [Image: see text] |
format | Online Article Text |
id | pubmed-7878246 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-78782462021-02-22 Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13 Tangborn, Andrew Kuang, Weijia Sabaka, Terence J. Yi, Ce Earth Planets Space Full Paper ABSTRACT: We have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590–1960), CM4 (1961–2000) and CM6 (2001–2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020–2025 time period. This forecast has been submitted as a candidate predictive model of IGRF-13 for the period 2020–2025. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2021-02-11 2021 /pmc/articles/PMC7878246/ /pubmed/33628082 http://dx.doi.org/10.1186/s40623-020-01324-w Text en © The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Full Paper Tangborn, Andrew Kuang, Weijia Sabaka, Terence J. Yi, Ce Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13 |
title | Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13 |
title_full | Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13 |
title_fullStr | Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13 |
title_full_unstemmed | Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13 |
title_short | Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13 |
title_sort | geomagnetic secular variation forecast using the nasa gems ensemble kalman filter: a candidate sv model for igrf-13 |
topic | Full Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7878246/ https://www.ncbi.nlm.nih.gov/pubmed/33628082 http://dx.doi.org/10.1186/s40623-020-01324-w |
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