Evaluation of candidate geomagnetic field models for the 10th generation of IGRF

Maus, Stefan; Macmillan, Susan; Lowes, F. J.; Bondar, T. N.

doi:10.1186/bf03351901

Cited by 26 publications

(26 citation statements)

References 8 publications

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“…The RMS difference between the IGRF-10 model and the DGRF model for 2005 is approximately 13 nT. The misfit of the IGRF-10 model coefficients to the best available estimate of the internal magnetic field is thus slightly larger than the 5 nT suggested in Maus et al (2005).…”

Section: Forecasting Abilitymentioning

confidence: 81%

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Forecasting secular variation using core flows

Beggan

Whaler

2010

Earth Planet Sp

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Over the past ten years satellite measurements in combination with data from ground-based observatories have allowed very detailed models of the secular variation (SV) of the Earth's magnetic field to be constructed. However, forecasting the change of the main field still remains a challenge, primarily because the core processes controlling SV are not sufficiently well understood. Hence, most forecasts do not appeal to any physical modelling constraints but use, for example, polynomial extrapolation from previous measurements. We attempt to apply a physical model to forecast the average SV during 2010-2015 by developing a core flow model. This steady flow model, derived from SV data during 2004.5 to 2009.5, generates a set of Gauss SV coefficients which are used to advect the large scale magnetic field forwards in time. Although this model has not been submitted as a candidate for IGRF-11, we present our SV prediction model and compare it to other candidate IGRF-11 SV models. In addition, we examine the use of the Ensemble Kalman filter to optimally assimilate field models derived from (1) forecast methods and (2) noisy data measurements. Such a scenario might conceivably arise if high quality satellite data with global coverage are not available for a significant period of time. We show that the overall misfit of the assimilated model to the actual field can be lower than the individual misfits of the input models, provided the uncertainties of each model are reasonably well known.

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Section: Forecasting Abilitymentioning

confidence: 81%

“…IGRF-10 in 2005 plus the sum of the predicted annual SV for five years) and the IGRF-11 model is 119 nT. This is an average RMS misfit of about 24 nT/yr, which is slightly larger than the error estimate of 20 nT/yr given in Maus et al (2005). Figure 1 summarises the RMS difference relationships.…”

Section: Forecasting Abilitymentioning

confidence: 85%

Forecasting secular variation using core flows

Beggan

Whaler

2010

Earth Planet Sp

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“…In May 2009 a call for IGRF-11 candidate models was agreed on by the task force and issued. This requested main field (MF) candidate models for the Definitive Geomagnetic Reference Field for epoch 2005.0 (DGRF-2005, for a provisional IGRF model for epoch 2010.0 (IGRF-2010) both to spherical har-monic degree 13, and for a prediction of the average SV over the upcoming five years (SV-2010(SV- -2015 to degree 8. An update of progress towards IGRF-11 was given by the task force chair at a business meeting of IAGA Div V, WG V-MOD in Sopron in August 2009.…”

Section: Introductionmentioning

confidence: 99%

“…We follow closely the strategy adopted in previous evaluations (see, for example, Maus et al, 2005) focusing on statistical comparisons between the candidate models and various mean models, and utilizing wellestablished diagnostic tools in both the spectral and physical domains. One limitation of this approach is that a good statistical agreement between models does not necessarily mean these models are the most realistic; it can also be a consequence of the use of very similar data selection or modelling techniques.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of candidate geomagnetic field models for IGRF-11

et al. 2010

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Here, we report the evaluations of candidate models carried out by the IGRF-11 task force during October/November 2009 and describe the weightings used to derive the new IGRF-11 model. The evaluations include calculations of root mean square vector field differences between the candidates, comparisons of the power spectra, and degree correlations between the candidates and a mean model. Coefficient by coefficient analysis including determination of weighting factors used in a robust estimation of mean coefficients is also reported. Maps of differences in the vertical field intensity at Earth's surface between the candidates and weighted mean models are presented. Candidates with anomalous aspects are identified and efforts made to pinpoint both troublesome coefficients and geographical regions where large variations between candidates originate. A retrospective analysis of IGRF-10 main field candidates for epoch 2005.0 and predictive secular variation candidates for 2005.0-2010.0 using the new IGRF-11 models as a reference is also reported. The high quality and consistency of main field models derived using vector satellite data is demonstrated; based on internal consistency DGRF-2005 has a formal root mean square vector field error over Earth's surface of 1.0 nT. Difficulties nevertheless remain in accurately forecasting field evolution only five years into the future.

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“…doi:10.5047/eps.2010.06.004 Lowes et al, 2000), was criticized and even discarded during the preparation of the 10th generation of IGRF (Maus et al, 2005), because attempts to use it were found inconclusive. The reasons invoked were the limited availability of independent data, the need to remove contributions from other sources than the core prior to comparing measured data with candidate models, and the absence of measurements during the time interval of the secular variation candidates.…”

Section: Introductionmentioning

confidence: 99%

Testing IGRF-11 candidate models against CHAMP data and quasi-definitive observatory data

Chulliat

Thébault

2010

Earth Planet Sp

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As part of the evaluation of IGRF-11 candidate models, we compared candidate models and actual measurements. We first carried out a residual analysis between main field candidates and CHAMP data, which were pre-processed and corrected for the secular variation and the lithospheric, external and oceanic fields. For epoch 2005.0, one model (D) is abnormally far from the testing dataset, while four models (A, B, F, G) have the smallest data residuals. For 2010.0, three models (B, F, G) have smaller data residuals than other models. These results, although biased toward models relying on datasets close to the testing datasets (B, F), usefully complement the results of intercomparisons between models. We next tested secular variation candidate models for 2010-2015 against annual differences of (a) definitive monthly means in 2007 and 2008 at 86 observatories, and (b) quasidefinitive monthly means from January to October 2009 at nine observatories where this new type of data was produced. Quasi-definitive data are found to significantly improve the discriminating effect of the test, favoring models obtained at epochs close to the end of 2009 (B, F) and penalizing some extrapolated models (G). They also enable a truly independent validation of the candidate models.

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Evaluation of candidate geomagnetic field models for the 10th generation of IGRF

Cited by 26 publications

References 8 publications

Forecasting secular variation using core flows

Forecasting secular variation using core flows

Evaluation of candidate geomagnetic field models for IGRF-11

Testing IGRF-11 candidate models against CHAMP data and quasi-definitive observatory data

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