Using Ensemble KalmanFiltering to improve magnetic field models during vector satellite data ‘gaps’?

Kalmanfiltering can be used to combine data optimally from different sources assuming that the error or variance of each data type is suitably understood. Typically a physical model is combined with occasional real measurements. Ensemble KalmanFilters (EnKF) extend this idea by making multiple simulations with randomly perturbed models drawn from probability distribution of fixed variance. Here we use EnKFto combine steady core surface flow models of the fluid outer core with magnetic field models derived from periods when no vector satellite data were available. We test if there is an optimal combination of flow and field that minimises the overall root-mean-square misfit to a ‘true’ magnetic field calculated after the resumption of satellite vector measurements

Forecasting changes of the magnetic field in the UK from L1 Lagrange solar wind measurements

Extreme space weather events can have large impacts on ground-based infrastructure important to technology-based societies. Machine learning techniques based on interplanetary observations have proven successful as a tool for forecasting global geomagnetic indices, however, few studies have examined local ground magnetic field perturbations. Nowcast and forecast models which predict the magnitude of the horizontal geomagnetic field, |BH|, and its time derivative, ∣∣dBHdt∣∣, at ground level would be valuable for assessing the potential space weather hazard. We attempt to predict the variation of the magnetic field at the three United Kingdom observatories (Eskdalemuir, Hartland and Lerwick) driven by L1 solar wind parameters. The horizontal magnetic field component and its time derivative are predicted from solar wind plasma and interplanetary magnetic field observations using Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network-LSTM models. A 5-fold grid search cross-validation is used for tuning the hyperparameters in each model. Forecasts were made with 5, 15 and 30-min lead times. Models were trained and validated with geomagnetic storm-only data from 1997 to 2016; their outputs were evaluated with the 7–9th September 2017 storms. The forecast models are only able to predict the directly driven parts of geomagnetic storms (not the substorms) and LSTM models generally perform best. We find the |BH| 15- and 30-min forecasts at Lerwick and Eskdalemuir have some predictive power. The 5-min |BH| forecast as well as all the ∣∣dBHdt∣∣ models for Eskdalemuir and all the Hartland models were found to have little or no predictive power. This suggests that the machine learning models have better forecasting power at higher latitude (closer to the auroral zones), where the ground magnetic variation field is larger and during storm onset, which is directly driven by changes in the solar wind

Investigating the location and strength of the auroral electrojets using Swarm

The auroral electrojets are a key space weather phenomenon. They are formed by horizontal Hall currents that flow within the ionospheric polar regions at an altitude of around 115 km. They form ovals around the magnetic poles but their latitudinal position, width, and strength are highly variable. These are governed by geomagnetic activity and solar wind conditions, along with a global ordering by the main magnetic field. Typically, greater geomagnetic activity will cause the electrojets to intensify and move equatorward. This is associated with greater auroral displays in more populated areas but also with potentially severe consequences both on Earth and in space: - geomagnetically Induced Currents (GICs) - disturbance to radio communications and GNSS signals - disruption to navigation applications - increased drag on satellites due to expansion of the atmosphere The auroral electrojet system can be described by the AE activity indices derived from measurements at ground-based magnetic observatories. The accuracy of the AE indices is limited by the observatories' fixed positions, which inhibits the ability to consistently locate the electrojets. Significantly, the indices only cover the Northern hemisphere so do not capture the differences between the Northern and Southern systems. Polar low-Earth orbit satellite observations offer the opportunity to overcome these limitations, by providing excellent latitudinal resolution and coverage equally over both poles. There have been several demonstrations of using satellites to monitor the auroral electrojets: Olsen (1996) using Magsat; Moretto et al (2002) using Oersted, CHAMP, and SAC-C; Juusola et al. (2009) and Vennerstrom and Moretto (2013) using CHAMP; and Hamilton and Macmillan (2013) using Magsat and CHAMP. The results presented here apply the method of Vennerstrom and Moretto (2013) to data from the Swarm mission

Investigating the Predictive Power of Magnetohydrodynamic Models for Geomagnetically Induced Currents in the UK

Rapid magnetic field fluctuations associated with space weather events can induce geomagnetically induced currents (GICs) in conductive structures on the Earth’s surface. Since space weather and GICs can be damaging to various technological systems and human activity, a good forecasting capability is important in order to mitigate their impacts. We used currently available magnetohydrodynamic (MHD) models of the magnetosphere and ionosphere, (SWMF, SWMF coupled with RCM, GUMICS-4 and Gorgon) to simulate ground magnetic field variations for the 7/8 September 2017 event, based on solar wind parameters propagated to the simulation domain. Modelled values of the northward (Bx) and eastward (By) magnetic field components show differences in both amplitude and temporal variability compared to the corresponding measurements, acquired via INTERMAGNET, from three UK observatories: Hartland, Eskdalemuir and Lerwick. Results indicate the accuracy of ground magnetic field forecast decreases with increasing latitude. By component tends to be predicted more accurately than Bx. It was found that the SWMF performs best in Bx forecast. Coupling with the Rice Convection Model (RCM) overestimates the field value causing poor agreement with measurements. By is best predicted by the Gorgon model. Despite being the most accurate in terms of Bx, the SWMF shows the largest error in By forecast. The resulting northward and eastward geoelectric field components were calculated from the magnetic field values using magnetotelluric transfer functions, which were then extrapolated to compute the GIC for the high-voltage UK power network. The GIC generated by a uniform electric field of 1 V/km shows that substations (nodes) located near coastlines are affected the most. Those nodes were then selected for further investigation. Error analysis suggests that GICs computed from modelled values are in closer agreement with GICs computed from our estimates of the actual geoelectric field for nodes at higher latitudes, where the SWMF performs the best. The GICs computed for substations at lower latitudes show larger discrepancies. Here, values inferred from GUMICS-4 tend to be the most accurate, despite its rather average performance in B-field forecast. Since the accuracy of the simulation of ground B-fields by MHD models included in this study is rather unsatisfactory, attempts to improve their prediction ability will be considered. Applying a method, commonly used in climate modelling, known as downscaling may potentially enhance the forecast accuracy by introducing smaller scale local variations in global variables simulated by each model

An investigation into Geoelectric tides at three sites in the UK

Electric fields are created by the motions of sea water through the geomagnetic field. Continuous geoelectric field monitoring began at the UK magnetic observatories in 2012/2013 alongside the standard geomagnetic field measurements. The three observatories are in quite different settings in relation to the seas surrounding the British Isles and the new data allow investigation of any tidally generated signals. More generally, the new electric field measurements will provide ground-truth data to test the accuracy of electric field estimates calculated using the geomagnetic field data and models of the electrical conductivity structure beneath the observatories for space weather studies. In this work, an investigation into the effects of periodic phenomena has been carried out, revealing both solar and lunar signals. Firstly, superposed epoch analysis has been performed. The results for Hartland observatory are consistent with the findings of previous experiments in the English Channel, with regard to the magnitude of solar and lunar semi-diurnal (S2 and M2) variations. Secondly, frequency analysis, using the fast Fourier transform has been used to find the dominant frequencies present in the electric field data again identifying known Sq-harmonics and the dominant motion-induced M2 tidal period at each station. There are difficulties in carrying out conventional Fourier analysis because of gaps in the data. This limits the length of continuous input data sets and so the frequency resolution. To overcome this problem, we have also used the Lomb-Scargle periodogram to investigate the spectrum as this method permits gaps in the data. We have also calculated the correlation between the geoelectric field components and data from closest tidal gauge stations to each site

Application of spherical Slepian functions to aeromagnetic data and in crustal field modelling

Models of the crustal magnetic field are typically represented using spherical harmonic coefficients. Rather than spherical harmonics, spherical Slepian functions (hereafter just Slepian functions) can be employed to produce a locally and also globally orthogonal basis in which to optimally represent the data in a region up to a given degree. The region can have any arbitrary shape and size. In this poster we show some of the possible applications of Slepian functions to aeromagnetic data studies: optimally separate a crustal field model into its oceanic and continental regions in order to investigate the spectral content; compactly describe regional spherical harmonics in a sparse manner; reconstruct a smooth function from a series of input data point

Core flow modelling from satellite-derived 'Virtual Observatories'

The last decade has seen a significant improvement in the capability to observe the global field at high spatial resolution using data from satellite missions including CHAMP, Oersted and SAC-C. These data complement the existing record of ground-based observatories, which have continuous temporal coverage at a single point. We wish to exploit these new data to model the secular variation (SV) globally and improve the flow models that have been constructed to date. Using the approach developed by Mandea and Olsen (2006) we create a set of 648 evenly distributed ‘Virtual Observatories’ (VO), at 400km above the Earth’s surface, encompassing satellite measurements from the CHAMP satellite over five years (2001-2005). We invert 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