- Open Access
The BGS magnetic field candidate models for the 11th generation IGRF
© NERC 2010
- Received: 30 November 2009
- Accepted: 20 May 2010
- Published: 31 December 2010
We describe the British Geological Survey’s 11th generation International Geomagnetic Reference Field candidate models. These models are based on a ‘parent model’ consisting of a degree and order 60 spherical harmonic expansion of selected vector and scalar magnetic field data from satellite and observatory sources within the period 1999.0 to 2010.0. The parent model’s internal field time dependence for degrees 1 to 13 is represented by linear spline with knots 400 days apart. The parent model’s degree 1 external field time dependence is described by periodic functions for the annual and semi-annual signals, and by dependence on the 20-minute Vector Magnetic Disturbance index. Signals induced by these external fields are also parameterised. Satellite data are weighted according to two noise estimators. Firstly by standard deviation along segments of the satellite track and secondly a larger-scale noise estimator defined in terms of a vector activity measure at the geographically closest magnetic observatories to the sample point.
- geomagnetic field
- geomagnetic secular variation
The International Geomagnetic Reference Field (IGRF) is a widely used model of the Earth’s main magnetic field and is produced every 5 years by a collaboration of modelling teams at the invitation of the International Association of Geomagnetism and Aeronomy (IAGA) Division V committee. The current IGRF (version 10, Macmillan and Maus, 2006) is now due for revision. IGRF-11, the 11th generation model, will include three new sets of coefficients: two main field (MF) models to degree and order 13 at epochs 2005.0 and 2010.0 and an average secular variation (SV) model valid from 2010.0 to 2015.0 to degree and order 8. The production of our BGS candidate models was carried out in three steps: selection of data from Ørsted, CHAMP, and ground observatories; fitting and evaluating the parent model; and finally extraction of the IGRF-11 candidate MF and SV coefficients. Since the production of the BGS IGRF-10 candidate models (Lesur et al., 2005), the most significant changes to our parent model have been to include more detailed parametrisation of external sources and the use of vector data at all latitudes with appropriate noise estimation.
From the time Ørsted was launched in 1999 and CHAMP the following year, a large number of vector and scalar measurements with good geographic coverage have been made. Over the same period, at ground level, a global network of observatories has been collecting vector measurements at fixed geographic locations. Together these data provide a large data set for MF modelling. However, in addition to the core and large scale lithospheric sources that are the focus of the IGRF-11 modelling effort, this large compilation of satellite and observatory data contain fields produced from other sources. In particular, the day-side ionospheric current system, ring-current, partial ring current, and induced currents in the Earth can be difficult to co-estimate and separate from the desired signals. To avoid contamination, measurements significantly affected by these sources must be removed from the data set through careful selection. We use a combination of local-time, magnetic indices, and measurements of the interplanetary magnetic field (IMF) and solar wind speed in the near-Earth environment to reject data likely to be contaminated by unwanted sources. Unfortunately, the local-time (night-side) data selection tends to result in gaps in the satellite time-series. It is conceivable that this uneven temporal distribution could result in spurious signals appearing in the time parametrisation of the parent model. We seek to address this problem by including observatory data, which helps provide a more continuous time-series.
However, even with rigorous data selection, contamination from unwanted sources remains a problem, particularly at high latitudes. For this reason, it has often been deemed preferable to use only scalar data at high latitudes rather than use vector components that are more sensitive to high latitude current systems. We still use only observatory pseudo-scalar data (vector data projected onto an a priori main field model) at higher latitudes, which also has the effect of making the co-estimation of the observatory biases easier by maintaining the linear relationship between data and model coefficients. However, we take a different approach with satellite data and use vector components in addition to scalar data at all latitudes wherever they are available. At the same time, we quantify the increase in noise in the data when we fit our parent model. This is achieved by using a combination of two noise estimators: along-track standard deviations calculated for each component at intervals from short segments of the satellite data, and a larger scale estimator of the vector disturbance using observatories nearest to the data sample. This allows us to produce a robust parent model without the need for a particularly complex model parametrisation, regularisation, or prior data correction to remove estimates of unmodelled source fields.
The parent model’s internal field time-dependence for degrees 1 to 13 is by linear spline with knots 400 days apart starting from mid-1999. The parent model’s degree 1 external field time-dependence is also modelled by the same linear spline arrangement but includes periodic functions for the annual and semi-annual signals, and a dependence on the 20-minute Vector Magnetic Disturbance index (VMD, Thomson and Lesur, 2007). Signals induced by these external fields are also parametrised. Observatory biases are co-estimated when fitting the parent model.
In Section 2 we describe the data used, their selection criteria, and briefly outline the data weighting scheme. In Section 3 we describe the parent model and the process used to fit its parameters, while in Section 4 we evaluate the model coefficients. In Section 5 we extrapolate forwards in time and extract the IGRF-11 MF and SV candidate models. We provide concluding remarks in Section 6.
We use CHAMP (calibration level version 51) scalar and vector data between 2000.6 and 2009.6. Ørsted scalar and vector data are used between 1999.2 and 2009.4. For Ørsted after 2005.9 only scalar data are available. To reduce the quantity of data to manageable levels, the satellite data were sub-sampled at every 60th datum giving a sample frequency of approximately once every 68 seconds.
Magnetic indices: Kp and Kp for previous 3 hours both ≤ 2−; | dVMD/dt |≤ 5nT/hour; IE ≤ 30 nT; PC ≤ 0.2 mV/m;
Solar wind data: 0 ≤ IMFBz ≤ +6nT; −3 ≤ IMFB y ≤ +3nT; −10 ≤ IMFB x < +10nT; solar wind speed ≤ 450 km/s;
Other: 22:30 ≤ local time (hour: min) ≤ 05:00, |Bobs − Ba |≤ 100nT; |FOver − Fflux |≤ 2nT
Magnetic indices: Kp ≤ 2+, | dDst/dt |≤ 5nT/hour
Solar wind data: IMFBz ≥ 0 nT
Other: night-time (01:00 to 02:00 LT + solar zenith angle test at 110 km altitude above observatory)
For high geomagnetic latitudes (>50 and < −50 degrees) observatory data were projected onto an a priori model vector and the resulting pseudo-scalar data were used in the inversion. For other latitudes, vector data were used.
Figure 1(c) shows the time-distribution of the final selected observatory data set. It can be seen that the observatory data provide a more continuous time-series than the satellite data, which is desirable for the robust time parametrisation of the model.
The final data set consists of 186,468 Ørsted data, 654,720 CHAMP data, and 357,175 observatory data giving a total of 1,198,363 individual data points (counting each vector component as one data point). The temporal distribution of the combined data set is shown in Fig. 1(d).
The selected satellite data were individually weighted using two “noise” estimators (for a detailed description, see Thomson et al., 2010). Firstly, a measure of local magnetic activity using the standard deviation (SD) along short segments (60 samples, approximately 500 km) of satellite track. Secondly, a larger-scale noise estimator derived from activity measured at the geographically closest magnetic observatories to the sample point. This results in the down-weighting of the vector data at high latitudes, particularly in the auroral zones. We find the technique produces a core and lithospheric field model, MEME08, that compares well with other similar models such as xCHAOS (Olsen and Mandea, 2008), GRIMM (Lesur et al., 2008), and MF6 (Maus et al., 2008) up to about degree and order 60. We also apply weighting based on instrument accuracy and zenith angle (to account for ionospheric field contamination) up to a maximum of 10 nT, and also one-degree tesseral weighting to account for different spatial data densities, in the same manner as Lesur et al. (2005).
For the observatory data weighting, we use a simpler scheme based on a scaling of the instrument accuracy and zenith angle weighting. The application of LAVA and along-track SD has the effect of significantly down-weighting the satellite data. To prevent the observatory data dominating the model post LAVA/SD, we apply a scaling to the observatory weights. This scaling is chosen such that the mean satellite-observatory data weighting post LAVA/SD is the same as that found before LAVA and along-track SD are included.
As a rough estimate of the weight carried by each data source (CHAMP, Ørsted, observatories) when fitting the model, we divide the number of data from each source that meet the selection criteria (defined above) by the mean of the diagonal entries of the data covariance matrix for that source (Ørsted’s covariance matrix contains off-diagonal entries to account for the anisotropic error introduced due to the presence of only one star camera, see Lesur et al., 2005). From this we determine the ratio of the weight of (CHAMP: Ørsted: observatories) data in the model to be roughly (2.4:1:2.9). This ratio is provided as a rough guide to the combined effect of data number and weighting but the precise influence of data from different sources will vary with time and model parameters. With this caveat in mind we can see that of the three sources, CHAMP and observatories contribute most to the model estimation. Overall, satellite data carries more weight than observatory data when fitting the parent model. Observatory data plays an important role in the time-parameterisation of our model and we have given them a significant weighting. However, the optimum balance between satellite and observatory data remains an issue and will be considered further in future work.
Mean and root mean square misfits between parent model estimate and input data, shown separately for data sources and by geomagnetic latitude. Mid and low geomagnetic latitudes are defined as being between ±50 degrees, with high latitudes defined as being outside this range. Satellite F misfits are calculated with respect to scalar input data. Observatory F misfits are calculated with respect to vector data projected onto an apriori model. The number of data quoted counts each component separately.
Root mean square (nT)
No. of data
Mid and low latitudes
Direct comparison between CHAMP Ørsted and observatory misfits is not straightforward. Although vector and scalar data are used at all latitudes for satellite data scalar data constitute a larger proportion of the total after Ørsted vector data becomes unavailable (see Fig. 1). In contrast, CHAMP and observatory scalar and vector data are available for all time but unlike satellite data, observatory scalar and vector data are confined to high and mid/low latitudes respectively. However, it is worth commenting on some patterns in the misfits. As expected, the mean and RMS satellite misfits are larger at high geomagnetic latitudes (>50 or <−50 degrees) than at mid and low latitudes (≥ −50 and ≤50 degrees). Contamination from un-modelled auroral current systems will increase the high-latitude misfits but so too will the downweighting described in Section 2. At high latitudes the satellite Z misfits are significantly lower than those of the X or Y components due to the effects of field-aligned currents. Although observatory biases are co-estimated in the parent model this does not mean that we should expect zero mean observatory misfits, since an L1-norm is used when fitting the model.
On the evidence presented above, the main field seems robust to degree and order 13 and we extract a set of MF coefficients at 2005.0 as a candidate model. To derive the 2010 main field candidate model, we project the behaviour of the parent model between the final two spline knots forward to 2010.0, up to degree and order 13. To do this we extract a degree and order 13 main field model at 2009.0 and use the SV estimate from the same period up to degree and order 13. We believe that using the full degree 13 SV estimate is justified given the apparent lower noise in the most recent SV behaviour seen in Fig. 3 and the relatively short time step. Given the longer time span of the average SV from 2010.0 to 2015.0, we did not rely on a single extraction of SV from the parent model. Instead we used an average of the parent model’s SV up to degree and order 8 between 2005.0 to 2009.0. The period over which the SV average was calculated, 2005 to 2009, was chosen because it represented the most recent part of the parent model covering a time-range similar to the 5 year life-time of the SV prediction.
We have calculated MF and SV coefficients for use in the derivation of IGRF-11. These models are based on a large data set selected from both satellite and observatory data that have been carefully selected to remove unwanted sources. Unlike previous models, we include weighted vector satellite data at all latitudes by utilising two noise estimators. We believe our IGRF candidate models are a robust contribution to IGRF-11.
We would like to acknowledge the Ørsted and champ science data centres, INTERMAGNET (at http://www.intermagnet.org), our colleagues in BGS who run the World Data Centre for Geomagnetism, and the many individual institutes operating observatories. We would also like to thank colleagues within the other IGRF-11 teams for their comments on our work and thank the referees for comments and suggestions that have improved the paper. This paper is published with the permission of the Executive Director, BGS (NERC) and the work was supported by NERC grant NER/O/S/2003/00677 (GEOSPACE).
- Lesur, V., S. Macmillan, and A. Thomson, The BGS magnetic field candidate models for the 10th generation IGRF, Earth Planets Space, 57, 1157–1163, 2005.View ArticleGoogle Scholar
- Lesur, V., I. Wardinski, M. Rother, and M. Mandea, GRIMM: the GFZ Reference Internal Magnetic Model based on vector satellite and observatory data, Geophys. J. Int., 173, 382–394, 2008.View ArticleGoogle Scholar
- Macmillan, S. and S. Maus, International Geomagnetic Reference Field— the tenth generation, Earth Planets Space, 57, 1135–1140, 2006.View ArticleGoogle Scholar
- Maus, S., F. Yin, H. Lühr, C. Manoj, M. Rother, J. Rauberg, I. Michaelis, C. Stolle, and R. D. Müller, Resolution of direction of oceanic magnetic lineations by the sixth-generation lithospheric magnetic field model from CHAMP satellite magnetic measurements, Geochem. Geophys. Geosyst., 9(7), 2008.Google Scholar
- Olsen, N. and M. Mandea, Rapidly changing flows in the Earth’s core, Nature Geosci., 1, 390–394, 2008.View ArticleGoogle Scholar
- Thomson, A. W. P. and V. Lesur, An improved geomagnetic data selection algorithm for global geomagnetic field modelling, Geophys. J. Int., 169, 951–963, 2007.View ArticleGoogle Scholar
- Thomson, A. W. P., B. Hamilton, S. Macmillan, and S. Reay, A novel weighting method for satellite magnetic data and a new global magnetic field model, Geophys. J. Int., 181, 250–260, 2010.View ArticleGoogle Scholar