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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...

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Autores principales: Tangborn, Andrew, Kuang, Weijia, Sabaka, Terence J., Yi, Ce
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
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]
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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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