TY - JOUR
T1 - A candidate secular variation model for IGRF-13 based on MHD dynamo simulation and 4DEnVar data assimilation
AU - Minami, Takuto
AU - Nakano, Shin’ya
AU - Lesur, Vincent
AU - Takahashi, Futoshi
AU - Matsushima, Masaki
AU - Shimizu, Hisayoshi
AU - Nakashima, Ryosuke
AU - Taniguchi, Hinami
AU - Toh, Hiroaki
N1 - Funding Information:
We are grateful to J. Aubert, A. Fournier, and G. Hulot for their helpful comments and discussion on our data assimilation scheme and SV forecast. We deeply thank the two reviewers, P. Livermore and A. Fournier, whose comments improved the manuscript considerably. Numerical calculation of this study was carried out on Supercomputer System for Statistical Science at ISM under the ISM Cooperative Research Program (2019-ISMCRP-1030), and the computer facilities at the Research Institute for Information Technology, Kyushu University.
Funding Information:
This study was financially supported by PRC JSPS CNRS, Bilateral Joint Research Project “Forecasting the geomagnetic secular variation based on data assimilation”, and ERI JURP 2019-G-04. Acknowledgements
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.[Figure not available: see fulltext.].
AB - 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.[Figure not available: see fulltext.].
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U2 - 10.1186/s40623-020-01253-8
DO - 10.1186/s40623-020-01253-8
M3 - Article
AN - SCOPUS:85091292372
VL - 72
JO - Earth, Planets and Space
JF - Earth, Planets and Space
SN - 1343-8832
IS - 1
M1 - 136
ER -