Correlation‐based modeling and separation of geomagnetic field components

Baerenzung

et al. 2020

Preprint

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Abstract We describe a new, original approach to the modelling of the Earth's magnetic field. The overall objective of this study is to reliably render fast variations of the core field and its secular variation. This method combines a sequential modelling approach, a Kalman filter, and a correlation based modelling step. All sources that most significantly contribute to the field measured at the surface of the Earth are modelled. Their separation is based on strong prior information on their spatial and temporal behaviours. We obtain a time series of model distributions which display behaviours similar to those of recent models based on more classic approaches, particularly at large temporal and spatial scales. Interesting new features and periodicities are visible in our models at smaller time and spatial scales. An important aspect of our method is to yield reliable error bars for all model parameters. These errors, however, are only as reliable as the description of the different sources and the prior information used are realistic. Finally, we used a slightly different version of our method to produce candidate models for the IGRF-13.

Section: Modelling Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sequential modelling of the Earth's core magnetic field

Ropp

Baerenzung

et al. 2020

Preprint

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“…These data have been selected depending on their local time and for magnetically quiet periods (c.f., Lesur et al 2008). The model is made of a series of snapshot models, 3 months apart, that have been built through a Kalman filter approach combined with a correlation-based modeling technique for the analysis step (Holschneider et al 2016). Each snapshot model is parameterized in terms of spherical harmonics and includes static core field and secular variation contributions.…”

Section: Data 1: Poloidal Scalar Potential At the Cmb Obtained From Tmentioning

confidence: 99%

A candidate secular variation model for IGRF-13 based on MHD dynamo simulation and 4DEnVar data assimilation

Minami

Nakano

et al. 2020

Earth Planets Space

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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.

“…Two types of spectra are used for the model, flat ones, with E ∞ i ( ) = A 2 i where A i is a magnitude, and spectra of the form E ∞ i ( ) = A 2 i (2 + 1)F ( ), referred as C-based spectra, making equation 6 equivalent to the correlation kernels proposed by Holschneider et al (2016). Only 2 sources are characterized by a flat spectrum, the core field and the induced / residual ionospheric field.…”

Section: Prior Characterization Of Spatial and Temporal Correlationsmentioning

confidence: 99%

The Kalmag Model as a Candidate for IGRF-13

Baerenzung

Holschneider

et al. 2020

Preprint

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Abstract We present a new model of the Geomagnetic field spanning the last 20 years and called Kalmag. Deriving from the assimilation of CHAMP and SWARM vector field measurements, it separates the different contributions to the observable field through parameterized prior covariance matrices. To make the inverse problem numerically feasible it has been sequentialized in time though the combination of a Kalman filter and a smoothing algorithm. The model provides reliable estimates of past, present and future mean fields and associated uncertainties. The version presented here is an update of our IGRF candidates, the amount of assimilated data has been doubled and the considered time window has been extended from [2000.5,2019.74] to [2000.5,2020.33].