Spatial Structure and Asymmetries of Magnetospheric Currents Inferred from High-Resolution Empirical Geomagnetic Field Models

High-resolution 21-cm line observations in a region aroundlII= 120°,b11= +15°, have revealed four types of structure in the interstellar hydrogen: a smooth background, large sheets of density 2 atoms cm-3, clouds occurring mostly in groups, and ‘Cloudlets’ of a few solar masses and a few parsecs in size; the velocity dispersion in the Cloudlets is only 1 km/sec. Strong temperature variations in the gas are in evidence.

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Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations

Earth Planets and Space ◽

10.1186/s40623-020-01279-y ◽

2020 ◽

Vol 72 (1) ◽

Author(s):

Sabrina Sanchez ◽

Johannes Wicht ◽

Julien Bärenzung

Keyword(s):

Magnetic Field ◽

Secular Variation ◽

Geomagnetic Field ◽

Wave Number ◽

Main Magnetic Field ◽

Candidate Model ◽

Uncertainty Estimates ◽

Geomagnetic Field Models ◽

Dipole Configuration

Abstract The IGRF offers an important incentive for testing algorithms predicting the Earth’s magnetic field changes, known as secular variation (SV), in a 5-year range. Here, we present a SV candidate model for the 13th IGRF that stems from a sequential ensemble data assimilation approach (EnKF). The ensemble consists of a number of parallel-running 3D-dynamo simulations. The assimilated data are geomagnetic field snapshots covering the years 1840 to 2000 from the COV-OBS.x1 model and for 2001 to 2020 from the Kalmag model. A spectral covariance localization method, considering the couplings between spherical harmonics of the same equatorial symmetry and same azimuthal wave number, allows decreasing the ensemble size to about a 100 while maintaining the stability of the assimilation. The quality of 5-year predictions is tested for the past two decades. These tests show that the assimilation scheme is able to reconstruct the overall SV evolution. They also suggest that a better 5-year forecast is obtained keeping the SV constant compared to the dynamically evolving SV. However, the quality of the dynamical forecast steadily improves over the full assimilation window (180 years). We therefore propose the instantaneous SV estimate for 2020 from our assimilation as a candidate model for the IGRF-13. The ensemble approach provides uncertainty estimates, which closely match the residual differences with respect to the IGRF-13. Longer term predictions for the evolution of the main magnetic field features over a 50-year range are also presented. We observe the further decrease of the axial dipole at a mean rate of 8 nT/year as well as a deepening and broadening of the South Atlantic Anomaly. The magnetic dip poles are seen to approach an eccentric dipole configuration.

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Simple models of fluid flow at the core surface derived from geomagnetic field models

Geophysical Journal International ◽

10.1111/j.1365-246x.1989.tb02022.x ◽

1989 ◽

Vol 99 (1) ◽

pp. 173-182 ◽

Cited By ~ 77

Author(s):

Jeremy Bloxham

Keyword(s):

Fluid Flow ◽

Geomagnetic Field ◽

Core Surface ◽

The Core ◽

Geomagnetic Field Models ◽

Simple Models

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Mapping of steady-state electric fields and convective drifts in geomagnetic fields – Part 1: Elementary models

Annales Geophysicae ◽

10.5194/angeo-34-55-2016 ◽

2016 ◽

Vol 34 (1) ◽

pp. 55-65 ◽

Cited By ~ 4

Author(s):

A. D. M. Walker ◽

G. J. Sofko

Keyword(s):

Magnetic Field ◽

Steady State ◽

Electric Field ◽

Electric Fields ◽

Magnetic Field Line ◽

Geomagnetic Field ◽

Geomagnetic Field Models ◽

Spatial Derivatives ◽

Field Lines ◽

The Magnetic Field

Abstract. When studying magnetospheric convection, it is often necessary to map the steady-state electric field, measured at some point on a magnetic field line, to a magnetically conjugate point in the other hemisphere, or the equatorial plane, or at the position of a satellite. Such mapping is relatively easy in a dipole field although the appropriate formulae are not easily accessible. They are derived and reviewed here with some examples. It is not possible to derive such formulae in more realistic geomagnetic field models. A new method is described in this paper for accurate mapping of electric fields along field lines, which can be used for any field model in which the magnetic field and its spatial derivatives can be computed. From the spatial derivatives of the magnetic field three first order differential equations are derived for the components of the normalized element of separation of two closely spaced field lines. These can be integrated along with the magnetic field tracing equations and Faraday's law used to obtain the electric field as a function of distance measured along the magnetic field line. The method is tested in a simple model consisting of a dipole field plus a magnetotail model. The method is shown to be accurate, convenient, and suitable for use with more realistic geomagnetic field models.

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Evaluation of candidate geomagnetic field models for IGRF-11

Earth Planets and Space ◽

10.5047/eps.2010.11.005 ◽

2010 ◽

Vol 62 (10) ◽

pp. 787-804 ◽

Cited By ~ 42

Author(s):

C. C. Finlay ◽

S. Maus ◽

C. D. Beggan ◽

M. Hamoudi ◽

F. J. Lowes ◽

...

Keyword(s):

Geomagnetic Field ◽

Geomagnetic Field Models

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Storm-time magnetospheric currents inferred from mid-latitude geomagnetic field variations.

Journal of geomagnetism and geoelectricity ◽

10.5636/jgg.42.1249 ◽

1990 ◽

Vol 42 (11) ◽

pp. 1249-1265 ◽

Cited By ~ 199

Author(s):

Toshihiko IYEMORI

Keyword(s):

Geomagnetic Field ◽

Magnetospheric Currents

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VirES for Swarm & Virtual Research Environment: Software, guides & infrastructure to boost accessibility of Swarm

10.5194/egusphere-egu21-9272 ◽

2021 ◽

Author(s):

Ashley Smith ◽

Martin Pačes

Keyword(s):

Community Involvement ◽

Geomagnetic Field ◽

Data Retrieval ◽

Product Portfolio ◽

It Services ◽

Research Environment ◽

Wide Range ◽

Geomagnetic Field Models ◽

Virtual Research Environment ◽

Model Residuals

<p>ESA's Swarm mission continues to deliver excellent data providing insight into a wide range of geophysical phenomena. The mission is an important asset whose data are used within a number of critical resources, from geomagnetic field models to space weather services. As the product portfolio grows to better deliver on the mission's scientific goals, we face increasing complexity in accessing, processing, and visualising the data and models. ESA provides &#8220;VirES for Swarm&#8221; [1] (developed by EOX IT Services) to help solve this problem. VirES is a web-based data retrieval and visualisation tool where the majority of Swarm products are available. VirES has a graphical interface but also a machine-to-machine interface (API) for programmable use (a Python client is provided). The VirES API also provides access to geomagnetic ground observatory data, as well as forwards evaluation of geomagnetic field models to give data-model residuals. The "Virtual Research Environment" (VRE) adds utility to VirES with a free cloud-based JupyterLab interface allowing scientists to immediately program their own analysis of Swarm products using the Python ecosystem. We are augmenting this with a suite of Jupyter notebooks and dashboards, each targeting a specific use case, and seek community involvement to grow this resource.</p><p>[1] https://vires.services</p>

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Bulgarian Geomagnetic Reference Field (BulGRF) for 2015.0 and secular variation prediction model up to 2020

Annales Geophysicae ◽

10.5194/angeo-35-1085-2017 ◽

2017 ◽

Vol 35 (5) ◽

pp. 1085-1092

Author(s):

Metodi Metodiev ◽

Petya Trifonova

Keyword(s):

Secular Variation ◽

Geomagnetic Field ◽

Field Model ◽

Field Measurements ◽

Reference Field ◽

Main Field ◽

Close Match ◽

Secular Variation Model ◽

Geomagnetic Field Models ◽

Annual Means

Abstract. The Bulgarian Geomagnetic Reference Field (BulGRF) for 2015.0 epoch and its secular variation model prediction up to 2020.0 is produced and presented in this paper. The main field model is based on the well-known polynomial approximation in latitude and longitude of the geomagnetic field elements. The challenge in our modelling strategy was to update the absolute field geomagnetic data from 1980.0 up to 2015.0 using secular measurements unevenly distributed in time and space. As a result, our model gives a set of six coefficients for the horizontal H, vertical Z, total field F, and declination D elements of the geomagnetic field. The extrapolation of BulGRF to 2020 is based on an autoregressive forecasting of the Panagyurishte observatory annual means. Comparison of the field values predicted by the model with Panagyurishte (PAG) observatory annual mean data and two vector field measurements performed in 2015 shows a close match with IGRF-12 values and some difference with the real (measured) values, which is probably due to the influence of crustal sources. BulGRF proves to be a reliable alternative to the global geomagnetic field models which together with its simplicity makes it a useful tool for reducing magnetic surveys to a common epoch carried out over the Bulgarian territory up to 2020.

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