scholarly journals Prediction of geomagnetic field with data assimilation: a candidate secular variation model for IGRF-11

2010 ◽  
Vol 62 (10) ◽  
pp. 775-785 ◽  
Author(s):  
Weijia Kuang ◽  
Zigang Wei ◽  
Richard Holme ◽  
Andrew Tangborn
Keyword(s):  
Data Assimilation ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Secular Variation Model ◽  
Variation Model

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 On the possibility of extending the IGRF predictive secular variation model to a higher SH degree

2010 ◽  
Vol 62 (10) ◽  
pp. 815-820 ◽  
Author(s):  
Luís Silva ◽  
Stefan Maus ◽  
Gauthier Hulot ◽  
Erwan Thébault
Keyword(s):  
Secular Variation ◽  
Secular Variation Model ◽  
Variation Model

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.

Sign in / Sign up

Export Citation Format

Share Document