|dc.description.abstract||The Earth’s magnetic field is generated by fluid motion of liquid iron in the outer core.
Flows at the top of the outer core are believed to be responsible for the secular variation
(SV) observed at the surface of the Earth. Modelling of this flow is open to considerable
ambiguity, though methods adopting different physical assumptions do lead to similar
flow velocity regimes. Some aspects of the ambiguities are investigated in this thesis.
The last decade has seen a significant improvement in the capability to observe the
global field at high spatial resolution. Several satellite missions have been launched,
providing a rich new set of scalar and vector magnetic measurements from which to
model the global field in detail. These data complement the existing record of groundbased
observatories, which have continuous temporal coverage at a single point. I exploit
these new data to model the secular variation (SV) globally and attempt to improve
the core flow models that have been constructed to date.
Using the approach developed by Mandea and Olsen (2006) I create a set of evenly
distributed ‘Virtual Observatories’ (VO), at 400km above the Earth’s surface, encompassing
satellite measurements from the CHAMP satellite over seven years (2001-2007),
inverting the SV calculated at each VO to infer flow along the core-mantle boundary.
Direct comparison of the SV generated by the flow model to the SV at individual VO
can be made. Thus, the residual differences can be investigated in detail. Comparisons
of residuals from flow models generated from a number of VO datasets provide evidence
that they are consistent with internal and external field effects in the satellite
data. I also show that the binning and processing of the VO data can induce artefacts,
including sectorial banding, into the residuals.
By employing the core flows from the inversion of SV data it may be possible to
forecast the change of the present magnetic field (as measured) forwards in time for a
short time period (e.g. less than five years) within an acceptable error budget. Using
simple advection of steady or non-steady flows to forecast magnetic field change gives
reasonably good fit to field models such as GRIMM, POMME or xCHAOS (< 50nT
root mean square difference after five years).
The forecast of the magnetic field change can be improved by optimally assimilating
measurements of the field into the forecast from flow models at discrete points in time
(e.g. annually). To achieve this, an Ensemble Kalman Filter (EnKF) can be used to
the capture non-linearity of the model and delineate the error bounds by means of a
Monte Carlo representation of the field evolution over time. In the EnKF model, an
ensemble of probable state vectors (Gauss coefficients) evolve over time, driven by SV
derived from core flows. The SV is randomly perturbed at each step before addition to
the state vectors. The mean of the ensemble is chosen as the most likely state (i.e. field
model) and the error associated with the estimate can be gauged from the standard
deviation from the mean.
I show an implementation of the EnKF for steady and non-steady flows generated
from ‘Virtual Observatory’ field models, compared to the field models GRIMM and
xCHAOS over the period 2002–2008. Using the EnKF, the maximum difference never
exceeds 25nT over the period. This promising approach allows measurements to be
included into model predictions to improve the forecast.||en