19 citations as recorded by crossref.

1. Geomagnetic Core Field Secular Variation Models N. Gillet et al. https://doi.org/10.1007/s11214-009-9586-6
2. Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13 A. Tangborn et al. https://doi.org/10.1186/s40623-020-01324-w
3. A testbed for geomagnetic data assimilation K. Gwirtz et al. https://doi.org/10.1093/gji/ggab327
4. Dynamic responses of the Earth’s outer core to assimilation of observed geomagnetic secular variation W. Kuang & A. Tangborn https://doi.org/10.1186/s40645-015-0071-4
5. Observing system simulation experiments in geomagnetic data assimilation D. Liu et al. https://doi.org/10.1029/2006JB004691
6. Geodynamo model and error parameter estimation using geomagnetic data assimilation A. Tangborn & W. Kuang https://doi.org/10.1093/gji/ggu409
7. Variational data assimilation for the initial-value dynamo problem K. Li et al. https://doi.org/10.1103/PhysRevE.84.056321
8. Forward and adjoint quasi‐geostrophic models of the geomagnetic secular variation E. Canet et al. https://doi.org/10.1029/2008JB006189
9. Prediction of geomagnetic field with data assimilation: a candidate secular variation model for IGRF-11 W. Kuang et al. https://doi.org/10.5047/eps.2010.07.008
10. Forecasting secular variation using core flows C. Beggan & K. Whaler https://doi.org/10.5047/eps.2010.07.004
11. A secular variation candidate for IGRF-14 based on core-flow inversion via an ensemble Kalman smoother K. Gwirtz et al. https://doi.org/10.1186/s40623-025-02289-4
12. Impact of localization and inflation on geomagnetic data assimilation K. Gwirtz et al. https://doi.org/10.1016/j.pepi.2024.107237
13. Continuous data assimilation for the 2D magnetohydrodynamic equations using one component of the velocity and magnetic fields A. Biswas et al. https://doi.org/10.3233/ASY-171454
14. Constraining a numerical geodynamo model with 100 years of surface observations W. Kuang et al. https://doi.org/10.1111/j.1365-246X.2009.04376.x
15. Variational data assimilation for a forced, inertia-free magnetohydrodynamic dynamo model K. Li et al. https://doi.org/10.1093/gji/ggu260
16. Quantitative comparison of variational and sequential data assimilation techniques for one-dimensional initial-value problems of ideal MHD J. Arnal & C. Groth https://doi.org/10.1016/j.compfluid.2024.106373
17. An Introduction to Data Assimilation and Predictability in Geomagnetism A. Fournier et al. https://doi.org/10.1007/s11214-010-9669-4
18. Coarse predictions of dipole reversals by low-dimensional modeling and data assimilation M. Morzfeld et al. https://doi.org/10.1016/j.pepi.2016.10.007
19. Implicit particle filtering for models with partial noise, and an application to geomagnetic data assimilation M. Morzfeld & A. Chorin https://doi.org/10.5194/npg-19-365-2012