Verification of the Geomagnetic Field Models Using Historical Satellite Measurements Obtained in 1964 and 1970

In 2019, the WDC for Solar-Terrestrial Physics in Moscow digitized the archive of observations of the Earth’s magnetic field carried out by the Soviet satellites Kosmos-49 (1964) and Kosmos-321 (1970). As a result, the scientific community for the first time obtained access to a unique digital data set, which was registered at the very beginning of the scientific space era. This article sets out three objectives. First, the quality of the obtained measurements is assessed by their comparison with the IGRF reference field model. Secondly, we assess the quality of the models, which at that time were derived from the data of these two satellites and ground-based observations. Thirdly, we propose a new, improved model of the geomagnetic field secular variation based on the scalar measurements of the Kosmos-49 and Kosmos-321 satellites using modern mathematical methods.

Paleomagnetic secular variation for a 21,000-year sediment sequence from Cascade Lake, north-central Brooks Range, Arctic Alaska

Two > 5-m-long sediment cores from Cascade Lake (68.38° N, 154.60° W), Arctic Alaska, were analyzed to quantify their paleomagnetic properties over the past 21,000 years. Alternating-field demagnetization of the natural remanent magnetization, anhysteretic remanent magnetization, isothermal remanent magnetization, and hysteresis experiments reveal a strong, well-defined characteristic remanent magnetization carried by a low coercivity magnetic component that increases up core. Maximum angular deviation values average < 2°, and average inclination values are within 4° of the geocentric axial dipole prediction. Radiometric ages based on 210Pb and 14C were used to correlate the major inclination features of the resulting paleomagnetic secular variation (PSV) record with those of other regional PSV records, including two geomagnetic field models and the longer series from Burial Lake, located 200 km to the west. Following around 6 ka (cal BP), the ages of PSV fluctuations in Cascade Lake begin to diverge from those of the regional records, reaching a maximum offset of about 2000 years at around 4 ka. Several correlated cryptotephra ages from this section (reported in a companion paper by Davies et al., this volume) support the regional PSV-based chronology and indicate that some of the 14C ages at Cascade Lake are variably too old.

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

&lt;p&gt;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 &amp;#8220;VirES for Swarm&amp;#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 &quot;Virtual Research Environment&quot; (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.&lt;/p&gt;&lt;p&gt;[1] https://vires.services&lt;/p&gt;

Investigation of Regional Geomagnetic Field Modelling in Indonesia

<p>This thesis deals with the application of the Spherical Cap Harmonic Analysis (SCHA) modelling technique to obtain geomagnetic field models for Indonesia, which have better resolution and accuracy than the International Geomagnetic Reference Field (IGRF). B-splines basis function and autoregressive forecasting are applied to improve estimates of secular variation and its forecast over the Indonesian region. The modelling technique is applied to geomagnetic observation data compiled from 68 geomagnetic repeat stations in Indonesia covering the period 1985 - 2015 from BMKG (Badan Meteorologi Klimatologi dan Geofisika / Agency for Meteorology, Climatology, and Geophysics) Indonesia, definitive data from five BMKG geomagnetic observatories and 13 INTERMAGNET (The International Real-Time Magnetic Observatory Network) observatories. Synthetic cartesian X, Y, and Z components at sea level at 17 fixed locations, calculated from IGRF-13, are also used. The area covered by the models in this thesis is the Indonesian region with a spherical cap half-angle of 30° and with the coordinate of the spherical cap pole at 122°E and 3°S. From statistical analysis and comparison with the IGRF, the SCHA model with index k = 7 is considered as the best SCHA model, both in resolution and accuracy. Compared with the root mean square deviation (RMSD) of the IGRF model, the RMSD of the SCHA model with index k = 7 is lower by 28 nT, 11 nT, and 34 nT for X, Y, and Z components, respectively. A model from interpolation of the SCHA with index k = 7 using the B-splines basis function for the year 1985.5 – 2015.5 shows that the SCHA model gives better results than the IGRF. The forecasting calculation for the year 2015.5 – 2020.5 suggests that the autoregressive order 3 of the SCHA with index k = 7 gives better results than the forecasting of the IGRF model, especially in the X, Z, and F components. However, in the Y component, the IGRF is still better than the SCHA model. The RMSD of the forecasted SCHA model is 154.92 nT, 200.87 nT, 104.39 nT, and 135.81 nT for X, Y, Z, and F components, respectively, while the RMSD of the IGRF model is 172.62 nT, 95.52 nT, 117.55 nT, and 162.38 nT for X, Y, Z, and F components. Thus, the forecasted SCHA model is suitable for data reduction of geomagnetic surveys in the Indonesian region but not preferable for navigation.</p>

Investigation of Regional Geomagnetic Field Modelling in Indonesia

<p>This thesis deals with the application of the Spherical Cap Harmonic Analysis (SCHA) modelling technique to obtain geomagnetic field models for Indonesia, which have better resolution and accuracy than the International Geomagnetic Reference Field (IGRF). B-splines basis function and autoregressive forecasting are applied to improve estimates of secular variation and its forecast over the Indonesian region. The modelling technique is applied to geomagnetic observation data compiled from 68 geomagnetic repeat stations in Indonesia covering the period 1985 - 2015 from BMKG (Badan Meteorologi Klimatologi dan Geofisika / Agency for Meteorology, Climatology, and Geophysics) Indonesia, definitive data from five BMKG geomagnetic observatories and 13 INTERMAGNET (The International Real-Time Magnetic Observatory Network) observatories. Synthetic cartesian X, Y, and Z components at sea level at 17 fixed locations, calculated from IGRF-13, are also used. The area covered by the models in this thesis is the Indonesian region with a spherical cap half-angle of 30° and with the coordinate of the spherical cap pole at 122°E and 3°S. From statistical analysis and comparison with the IGRF, the SCHA model with index k = 7 is considered as the best SCHA model, both in resolution and accuracy. Compared with the root mean square deviation (RMSD) of the IGRF model, the RMSD of the SCHA model with index k = 7 is lower by 28 nT, 11 nT, and 34 nT for X, Y, and Z components, respectively. A model from interpolation of the SCHA with index k = 7 using the B-splines basis function for the year 1985.5 – 2015.5 shows that the SCHA model gives better results than the IGRF. The forecasting calculation for the year 2015.5 – 2020.5 suggests that the autoregressive order 3 of the SCHA with index k = 7 gives better results than the forecasting of the IGRF model, especially in the X, Z, and F components. However, in the Y component, the IGRF is still better than the SCHA model. The RMSD of the forecasted SCHA model is 154.92 nT, 200.87 nT, 104.39 nT, and 135.81 nT for X, Y, Z, and F components, respectively, while the RMSD of the IGRF model is 172.62 nT, 95.52 nT, 117.55 nT, and 162.38 nT for X, Y, Z, and F components. Thus, the forecasted SCHA model is suitable for data reduction of geomagnetic surveys in the Indonesian region but not preferable for navigation.</p>

Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

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

Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

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.

Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

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.

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

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.

Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman fllter: A candidate model for IGRF 2020

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 512 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 forecasts. The algorithm was validated on the time period 2010-2015 by comparing with the 2015 IGRF before being applied to the 2020-2025 time period. This forecast has been submitted as a candidate model for IGRF 2025.