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

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 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.

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

10.21203/rs.3.rs-45638/v3 ◽

2020 ◽

Author(s):

Andrew Tangborn ◽

Weijia Kuang ◽

Terence Sabaka ◽

Ce Ye

Keyword(s):

Kalman Filter ◽

Secular Variation ◽

Ensemble Kalman Filter ◽

Geomagnetic Field ◽

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 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.

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

10.21203/rs.3.rs-45638/v1 ◽

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.

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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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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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A comparison of geomagnetic secular variation as recorded by historical, archaeomagnetic and palaeomagnetic measurements

Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences ◽

10.1098/rsta.1982.0070 ◽

1982 ◽

Vol 306 (1492) ◽

pp. 103-112 ◽

Cited By ~ 15

Keyword(s):

Magnetic Field ◽

Lake Sediments ◽

Secular Variation ◽

Geomagnetic Field ◽

Secular Change ◽

Mathematical Approach ◽

Geomagnetic Secular Variation ◽

The Past ◽

Palaeomagnetic Data ◽

Field Behaviour

Palaeomagnetic methods can extend the documentary record of changes in the Earth’s magnetic field far into the past. Tolerable agreement is found between various methods, demonstrating the geophysical value of palaeomagnetic experiments. Combining results from the different approaches of investigating secular change can lead to a better perspective and to superior models of geomagnetic field behaviour. Lake sediments have recently been found to hold remarkably detailed signatures of past field changes. A mathematical approach to formulating an empirical description of global geomagnetic field behaviour is proposed and applied to palaeomagnetic data spanning the last 10 ka.

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Dynamic Calibration with an Ensemble Kalman Filter Based Data Assimilation Approach for Root-Zone Moisture Predictions

Journal of Hydrometeorology ◽

10.1175/jhm582.1 ◽

2007 ◽

Vol 8 (4) ◽

pp. 910-921 ◽

Cited By ~ 16

Author(s):

Nicola Montaldo ◽

John D. Albertson ◽

Marco Mancini

Keyword(s):

Soil Moisture ◽

Kalman Filter ◽

Ensemble Kalman Filter ◽

Root Zone ◽

Sensitivity Analyses ◽

Surface Model ◽

Early Model ◽

Model Errors ◽

Model Bias ◽

Near Surface

Abstract In the presence of uncertain initial conditions and soil hydraulic properties, land surface model (LSM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the near-surface soil moisture (θg), as observed from a remote platform. In this paper the possibility of merging observations and the model optimally for providing robust predictions of root-zone soil moisture (θ2) is demonstrated. An assimilation approach that assimilates θg through the ensemble Kalman filter (EnKF) and provides a physics-based update of θ2 is developed. This approach, as with other common soil moisture assimilation approaches, may fail when a key LSM parameter, for example, the saturated hydraulic conductivity (ks), is estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach is developed that accepts this violation in the early model run times and dynamically calibrates all the components of the ks ensemble as a function of the persistent bias in root-zone soil moisture, allowing one to remove the model bias, restore the fidelity to the EnKF requirements, and reduce the model uncertainty. The robustness of the proposed approach is also examined in sensitivity analyses.

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Correlation of Late Pleistocene Glaciolacustrine and Marine Deposits by Means of Geomagnetic Secular Variation, with Examples from Northern New York and Southern Ontario

Quaternary Research ◽

10.1006/qres.1994.1078 ◽

1994 ◽

Vol 42 (3) ◽

pp. 277-287 ◽

Cited By ~ 3

Author(s):

Donald L. Pair ◽

Ernest H. Muller ◽

Peter W. Plumley

Keyword(s):

New York ◽

Marine Sediments ◽

Secular Variation ◽

Stratigraphic Correlation ◽

Geomagnetic Secular Variation ◽

Southern Ontario ◽

Marine Deposits ◽

Time Period ◽

Variation Curve ◽

Lake Cores

AbstractThe geomagnetic secular variation record retained by glaciolacustrine and marine sediments at nine sites in northern New York and southern Ontario provides a means for stratigraphic correlation of glacial deposits for the time period between about 12,600 to 9900 14C yr B.P. Measurement of the depositional remanent magnetism of sediments deposited in Glacial Lake Iroquois and the Champlain Sea has produced a geomagnetic secular variation curve that represents the time period immediately following deglaciation about 12,600 14 C yr B.P. The curve varies from about 358° to 344° declination and 51° to 61° inclination over approximately 180 valve years. Marine sediments of the Champlain Sea have preserved a record approximately 1500 yr long that varies from about 2° to 29° declination and 47° to 60° inclination. These combined glacial-paleomagnetic records may also correlate with those from glacial sequences beyond our study area. The shape and amplitude of the secular variation record in glaciolacustrine and marine sediments from the western Adirondack borderland show agreement with other glacial varve secular variation records and suggest possible correlations with secular variation curves from lake cores.

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