scholarly journals The CSES global geomagnetic field model (CGGM): an IGRF-type global geomagnetic field model based on data from the China Seismo-Electromagnetic Satellite

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Yanyan Yang ◽  
Gauthier Hulot ◽  
Pierre Vigneron ◽  
Xuhui Shen ◽  
Zeren Zhima ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Field Model ◽  
Magnetic Data ◽  
Candidate Model ◽  
Main Field ◽  
Data Set ◽  
Geomagnetic Field Model ◽  
Time Period

AbstractUsing magnetic field data from the China Seismo-Electromagnetic Satellite (CSES) mission, we derive a global geomagnetic field model, which we call the CSES Global Geomagnetic Field Model (CGGM). This model describes the Earth’s magnetic main field and its linear temporal evolution over the time period between March 2018 and September 2019. As the CSES mission was not originally designed for main field modelling, we carefully assess the ability of the CSES orbits and data to provide relevant data for such a purpose. A number of issues are identified, and an appropriate modelling approach is found to mitigate these. The resulting CGGM model appears to be of high enough quality, and it is next used as a parent model to produce a main field model extrapolated to epoch 2020.0, which was eventually submitted on October 1, 2019 as one of the IGRF-13 2020 candidate models. This CGGM candidate model, the first ever produced by a Chinese-led team, is also the only one relying on a data set completely independent from that used by all other candidate models. A successful validation of this candidate model is performed by comparison with the final (now published) IGRF-13 2020 model and all other candidate models. Comparisons of the secular variation predicted by the CGGM parent model with the final IGRF-13 2020–2025 predictive secular variation also reveal a remarkable agreement. This shows that, despite their current limitations, CSES magnetic data can already be used to produce useful IGRF 2020 and 2020–2025 secular variation candidate models to contribute to the official IGRF-13 2020 and predictive secular variation models for the coming 2020–2025 time period. These very encouraging results show that additional efforts to improve the CSES magnetic data quality could make these data very useful for long-term monitoring of the main field and possibly other magnetic field sources, in complement to the data provided by missions such as the ESA Swarm mission.

scholarly journals Co-estimating geomagnetic field and calibration parameters: modeling Earth’s magnetic field with platform magnetometer data

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Clemens Kloss ◽  
Christopher C. Finlay ◽  
Nils Olsen
Keyword(s):  
Magnetic Field ◽  
Geomagnetic Field ◽  
Field Model ◽  
Magnetic Data ◽  
Magnetic Survey ◽  
Modeling Framework ◽  
Geomagnetic Field Model ◽  
Field Modeling ◽  
Magnetometer Data ◽  
Calibration Parameters

AbstractModels of the geomagnetic field rely on magnetic data of high spatial and temporal resolution to give an accurate picture of the Earth’s internal magnetic field and its time-dependence. The magnetic data from low-Earth orbit satellites of dedicated magnetic survey missions such as CHAMP and Swarm play a key role in the construction of such models. Unfortunately, there are no magnetic data available from such satellites after the end of the CHAMP mission in 2010 and before the launch of the Swarm mission in late 2013. This limits our ability to recover signals on timescales of 3 years and less during this gap period. The magnetic data from platform magnetometers carried by satellites for navigational purposes may help address this data gap provided that they are carefully calibrated. Earlier studies have demonstrated that platform magnetometer data can be calibrated using a fixed geomagnetic field model as reference. However, this approach can lead to biased calibration parameters. An alternative approach has been developed in the form of a co-estimation scheme which consists of simultaneously estimating both the calibration parameters and a model of the internal part of the geomagnetic field. Here, we go further and develop a scheme, based on the CHAOS field modeling framework, that involves co-estimation of both internal and external geomagnetic field models along with calibration parameters of platform magnetometer data. Using our implementation, we are able to derive a geomagnetic field model spanning 2008 to 2018 with satellite magnetic data from CHAMP, Swarm, secular variation data from ground observatories, and platform magnetometer data from CryoSat-2 and the GRACE satellite pair. Through a number of experiments, we explore correlations between the estimates of the geomagnetic field and the calibration parameters, and suggest how these may be avoided. We find evidence that platform magnetometer data provide additional information on the secular acceleration, especially in the Pacific during the gap between CHAMP and Swarm. This study adds to the evidence that it is beneficial to use platform magnetometer data in geomagnetic field modeling.

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

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.

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

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.

scholarly journals Geomagnetic core field models and secular variation forecasts for the 13th International Geomagnetic Reference Field (IGRF-13)

2020 ◽  
Author(s):  
Ingo Wardinski ◽  
Diana Saturnino ◽  
Hagay Amit ◽  
Aude Chambodut ◽  
Benoit Langlais ◽  
...  
Keyword(s):  
Secular Variation ◽  
Geomagnetic Field ◽  
Singular Spectrum Analysis ◽  
Reference Field ◽  
Candidate Model ◽  
Main Field ◽  
International Geomagnetic Reference Field ◽  
Core Field ◽  
International Geomagnetic Reference ◽  
Continuous Models

Abstract Observations of the geomagnetic field taken at Earth's surface and at satellite altitude were combined to construct continuous models of the geomagnetic field and its secular variation from 1957 to 2020. From these parent models, we derive candidate main field models for the epochs 2015 and 2020 to the 13th generation of the International Geomagnetic Reference Field (IGRF). The secular variation candidate model for the period 2020 - 2025 is derived from a forecast of the secular variation in 2022.5, which results from a multi-variate singular spectrum analysis of the secular variation from 1957 to 2020.

scholarly journals Recent geomagnetic secular variation from Swarm and ground observatories as estimated in the CHAOS-6 geomagnetic field model

2016 ◽  
Vol 68 (1) ◽  
Author(s):  
Christopher C. Finlay ◽  
Nils Olsen ◽  
Stavros Kotsiaros ◽  
Nicolas Gillet ◽  
Lars Tøffner-Clausen
Keyword(s):  
Secular Variation ◽  
Geomagnetic Field ◽  
Field Model ◽  
Geomagnetic Secular Variation ◽  
Geomagnetic Field Model

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

2020 ◽  
Author(s):  
Andrew Tangborn ◽  
Weijia Kuang ◽  
Terence Sabaka ◽  
Ce Yi
Keyword(s):  
Secular Variation ◽  
Ensemble Kalman Filter ◽  
Geomagnetic Field ◽  
Candidate Model ◽  
Model Bias ◽  
Geomagnetic Secular Variation ◽  
Forecast Period ◽  
Time Period ◽  
Correction Scheme ◽  
Geomagnetic Field Models

Abstract 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.

scholarly journals Geomagnetic core field models and secular variation forecasts for the 13th International Geomagnetic Reference Field (IGRF-13)

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
I. Wardinski ◽  
D. Saturnino ◽  
H. Amit ◽  
A. Chambodut ◽  
B. Langlais ◽  
...  
Keyword(s):  
Secular Variation ◽  
Geomagnetic Field ◽  
Singular Spectrum Analysis ◽  
Reference Field ◽  
Candidate Model ◽  
Main Field ◽  
International Geomagnetic Reference Field ◽  
Core Field ◽  
International Geomagnetic Reference ◽  
Continuous Models

Abstract Observations of the geomagnetic field taken at Earth’s surface and at satellite altitude are combined to construct continuous models of the geomagnetic field and its secular variation from 1957 to 2020. From these parent models, we derive candidate main field models for the epochs 2015 and 2020 to the 13th generation of the International Geomagnetic Reference Field (IGRF). The secular variation candidate model for the period 2020–2025 is derived from a forecast of the secular variation in 2022.5, which results from a multi-variate singular spectrum analysis of the secular variation from 1957 to 2020.

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

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Takuto Minami ◽  
Shin’ya Nakano ◽  
Vincent Lesur ◽  
Futoshi Takahashi ◽  
Masaki Matsushima ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Field Model ◽  
Weighted Sum ◽  
Candidate Model ◽  
Core Surface ◽  
The Core ◽  
Mhd Dynamo ◽  
Assimilation Scheme

Abstract 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.

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