A candidate secular variation model for IGRF-13 based on MHD dynamo simulation and 4DEnVar data assimilation
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- 資料種別
- 記事
- 著者・編者
- Takuto MinamiShin’ya NakanoVincent Lesur
- 出版年月日等
- 2020-09-21
- 出版年（W3CDTF）
- 2020-09-21
- タイトル（掲載誌）
- EPS : Earth, Planets and Space
- 巻号年月日等（掲載誌）
- 72(136)
- 掲載巻
- 72(136)
- ISSN（掲載誌）
- 1880-5981
- ISSN-L（掲載誌）
- 1343-8832
- 本文の言語コード
- eng
- DOI
- 10.1186/s40623-020-01253-8
- 国立国会図書館永続的識別子
- info:ndljp/pid/11616221
- コレクション（共通）
- コレクション（障害者向け資料：レベル1）
- コレクション（個別）
- 国立国会図書館デジタルコレクション > 電子書籍・電子雑誌 > その他
- 収集根拠
- オンライン資料収集制度
- 受理日（W3CDTF）
- 2021-01-13T14:13:05+09:00
- 保存日（W3CDTF）
- 2021-01-13
- 記録形式（IMT）
- application/pdf
- オンライン閲覧公開範囲
- 国立国会図書館内限定公開
- デジタル化資料送信
- 図書館・個人送信対象外
- 遠隔複写可否（NDL）
- 可
- 掲載誌（国立国会図書館永続的識別子）
- info:ndljp/pid/11467692
- 連携機関・データベース
- 国立国会図書館 : 国立国会図書館デジタルコレクション
- 要約等
- We have submitted a secular variation (SV) candidate model for the thirteenth generation of International Geomagnetic Reference Field model (IGRF-13) using a data assimilation scheme and a magnetohydrodynamic (MHD) dynamo simulation code. This is the first contribution to the IGRF community from research groups in Japan. A geomagnetic field model derived from magnetic observatory hourly means, and CHAMP and Swarm-A satellite data, has been used as input data to the assimilation scheme. We adopt an ensemble-based assimilation scheme, called four-dimensional ensemble-based variational method (4DEnVar), which linearizes outputs of MHD dynamo simulation with respect to the deviation from a dynamo state vector at an initial condition. The data vector for the assimilation consists of the poloidal scalar potential of the geomagnetic field at the core surface and flow velocity field slightly below the core surface. Dimensionless time of numerical geodynamo is adjusted to the actual time by comparison of secular variation time scales. For SV prediction, we first generate an ensemble of dynamo simulation results from a free dynamo run. We then assimilate the ensemble to the data with a 10-year assimilation window through iterations, and finally forecast future SV by the weighted sum of the future extension parts of the ensemble members. Hindcast of the method for the assimilation window from 2004.50 to 2014.25 confirms that the linear approximation holds for 10-year assimilation window with our iterative ensemble renewal method. We demonstrate that the forecast performance of our data assimilation and forecast scheme is comparable with that of IGRF-12 by comparing data misfits 4.5 years after the release epoch. For estimation of our IGRF-13SV candidate model, we set assimilation window from 2009.50 to 2019.50. We generate our final SV candidate model by linear fitting for the weighted sum of the ensemble MHD dynamo simulation members from 2019.50 to 2025.00. We derive errors of our SV candidate model by one standard deviation of SV histograms based on all the ensemble members.
- DOI
- 10.1186/s40623-020-01253-8
- オンライン閲覧公開範囲
- インターネット公開
- 著作権情報
- © The Author(s) 2020.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 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/.
- 関連情報
- A candidate secular variation model for IGRF-13 based on MHD dynamo simulation and 4DEnVar data assimilation
- 関連情報（URI）
- 参照
- Short-term prediction of geomagnetic secular variation with an echo state networkBehavior of the iterative ensemble-based variational method in nonlinear problemsInternational Geomagnetic Reference Field : the thirteenth generation
- 参照
- An Introduction to Data Assimilation and Predictability in GeomagnetismMean-square values on sphere of spherical harmonic vector fieldsVariational data assimilation for the initial-value dynamo problemPower requirement of the geodynamo from ohmic losses in numerical and laboratory dynamosImplementation of a high-order combined compact difference scheme in problems of thermally driven convection and dynamo in rotating spherical shellsAn Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary TestBehavior of the iterative ensemble-based variational method in nonlinear problemsDouble diffusive convection in the Earth’s core and the morphology of the geomagnetic fieldJoint state and parameter estimation with an iterative ensemble Kalman smootherAn Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data AssimilationDipole collapse and reversal precursors in a numerical dynamoVariational data assimilation for a forced, inertia-free magnetohydrodynamic dynamo modelAdaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical AspectsVector Errors in Spherical Harmonic Analysis of Scalar DataDerivation and use of core surface flows for forecasting secular variationGRIMM: the GFZ Reference Internal Magnetic Model based on vector satellite and observatory dataCore surface flow modelling with geomagnetic diffusion in a boundary layerEarth's dynamo limit of predictability controlled by magnetic dissipationBottom-up control of geomagnetic secular variation by the Earth’s inner coreEarth's dynamo limit of predictabilityA case for variational geomagnetic data assimilation: insights from a one-dimensional, nonlinear, and sparsely observed MHD systemCorrelation‐based modeling and separation of geomagnetic field componentsThe geomagnetic secular‐variation timescale in observations and numerical dynamo modelsEvidence for a new geomagnetic jerk in 2014Large-Scale Flow in the CoreAssimialtion of observation, an introductionInternational Geomagnetic Reference Field : the 12th generationEvaluation of candidate geomagnetic field models for IGRF-12Sequential modelling of the Earth’s core magnetic fieldEffect of core electrical conductivity on core surface flow modelsPrediction of geomagnetic field with data assimilation : a candidate secular variation model for IGRF-11A candidate secular variation model for IGRF-12 based on Swarm data and inverse geodynamo modellingParent magnetic field models for the IGRF-12 GFZ-candidates
- 連携機関・データベース
- 国立情報学研究所 : CiNii Research
- 提供元機関・データベース
- 学術機関リポジトリデータベース雑誌記事索引データベースCrossrefCiNii Articles科学研究費助成事業データベース科学研究費助成事業データベース学術機関リポジトリデータベースCrossrefCrossrefCrossref
- 書誌ID（NDLBibID）
- 11616221
- NII論文ID
- 120006892083