Automatic detection of ionospheric Alfvén resonances using signal and image processing techniques

Induction coils permit the measurement of small and very rapid changes of the magnetic field. A new set of induction coils in the UK (at L = 3.2) record magnetic field changes over an effective frequency range of 0.1–40 Hz, encompassing phenomena such as the Schumann resonances, magnetospheric pulsations and ionospheric Alfvén resonances (IARs). The IARs typically manifest themselves as a series of spectral resonance structures (SRSs) within the 1–10 Hz frequency range, usually appearing as fine bands or fringes in spectrogram plots and occurring almost daily during local night-time, disappearing during the daylight hours. The behaviour of the occurrence in frequency (f) and the difference in frequency between fringes (delta f) varies throughout the year. In order to quantify the daily, seasonal and annual changes of the SRSs, we developed a new method based on signal and image processing techniques to identify the fringes and to quantify the values of f , delta f and other relevant parameters in the data set. The technique is relatively robust to noise though requires tuning of threshold parameters. We analyse 18 months of induction coil data to demonstrate the utility of the method

Validation of geomagnetically induced current modelling code

A paper by Horton et al. (2012) details a test grid containing 8 substations with 15 transformers connected by 15 lines. The paper gives detailed information about location, resistances and connections including features such as capacitors, delta and composite transformer types. The model output for 1 V/km in the north-south and east-west directions are provided. In early 2017, the BGS Geomagnetism team produced an equivalent model using the Nodal Admittance Method and proved their model to be consistent with Horton et al. This report outlines our approach and results

Differential Magnetometer Measurements of Geomagnetically Induced Currents in a Complex High Voltage Network

Space weather poses a hazard to grounded electrical infrastructure such as high voltage (HV) transformers, through the induction of geomagnetically induced currents (GICs). Modelling GIC requires knowledge of the source magnetic field and the Earth's electrical conductivity structure, in order to calculate the geoelectric fields generated during magnetic storms, as well as knowledge of the topology of the HV network. Direct measurement of GICs at the ground neutral in substations is possible with a Hall‐effect probe, but such data are not widely available. To validate our HV network model, we use the Differential Magnetometer Method (DMM) to measure GICs in the 400 kV grid of Great Britain. We present DMM measurements for the 26 August 2018 storm at a site in eastern Scotland with up to 20 A recorded. The line GIC correlate well with Hall probe measurements at a local transformer, though they differ in amplitude by an order of magnitude (a maximum of ~2 A). We deployed a long‐period magnetotelluric (MT) instrument to derive the local impedance tensor which can be used to predict the geoelectric field from the recorded magnetic field. Using the MT‐derived electric field estimates, we model GICs within the network, accounting for the difference in magnitude between the DMM‐measured line currents and earth currents at the local substation. We find the measured line and earth GICs match the expected GICs from our network model, confirming that detailed knowledge of the complex network topology and its resistance parameters is essential for accurately computing GICs

Developing a UK new ground electric field model for SWIMMR N4 (SAGE) : interim report

This interim report describes progress made to date in the SWIMMR N4 (SAGE) project regarding the update of geoelectric field modelling in Britain during magnetic storms. We describe research efforts to understand how the present thin-sheet method of computing the geoelectric field from magnetic variation data compares to the measured field at the three UK observatories. We then examine how measured magnetotelluric (MT) impedances can be used to improve the modelled geoelectric field during space weather events. Next, we describe the fieldwork campaign to collect new high quality magnetotelluric data in Britain. As of August 2022, BGS have collected magnetic and electric field measurements at 32 sites across England, southern Scotland, and Wales. Around 18 remain to be completed by March 2023. We present results from all the sites collected thus far. The measurements have been used to compute MT impedances which demonstrate large variability attributed to the underlying geology across Britain. We use the new MT data to re-evaluate the geoelectric field during the September 2017 storm and find large differences, for example, in central Yorkshire the electric field estimates are about one-tenth the magnitude observed in Lincolnshire around 100 km distant. In the future the MT measurements will be included in a new 3D model of the conductivity of Britain for space weather hazard purposes

Global Dynamical Network of the Spatially Correlated Pc2 Wave Response for the 2015 St. Patrick's Day Storm

We show the global dynamics of spatial correlation of Pc2 wave activity can track the evolution of the 2015 St. Patrick's Day geomagnetic storm for an 8 hr time window around onset. The global spatially coherent response is tracked by forming a dynamical network from 1 s data using the full set of 100+ ground-based magnetometer stations collated by SuperMAG and Intermagnet. The pattern of spatial coherence is captured by network parameters which in turn track the evolution of the storm. At onset interplanetary magnetic field (IMF) Bz > 0 and Pc2 power increases, we find a global response with stations correlated over both local and global distances. Following onset, whilst Bz > 0, the network response is confined to the day-side. When IMF Bz < 0, there is a strong local response at high latitudes, consistent with the onset of polar cap convection driven by day-side reconnection. The spatially coherent response as revealed by the network grows and is maximal when auroral (SuperMAG electrojet) and ring current (SuperMAG ring current) 1 min resolution geomagnetic indices peak, consistent with an active electrojet and ring-current. Throughout the storm there is a coherent response both in stations located along lines of constant geomagnetic longitude, between hemispheres, and across magnetic local time. The network does not simply track average Pc2 wave power, it is characterized by network parameters which track the storm evolution. This is the first study to parameterize global Pc2 wave correlation and offers the possibility of statistical studies across multiple events and comparison with, and validation of, space weather models

Climatology of the harmonic frequency separation of ionospheric Alfvén Resonances at Eskdalemuir Observatory, UK

We extracted the harmonic frequency separation (Δf) of Ionospheric Alfvén Resonances (IAR) observed in the Eskdalemuir induction coil magnetometer data for the 9 year data set of 2013–2021. To obtain Δf values, we used a machine learning technique that identifies the harmonics and from this we calculated the average separation. To investigate the climatology of the IAR, we have modeled the Δf of the IAR for the data set using a time of flight calculation with model Alfvén velocity profiles. When analyzing Δf from the model and data, we found that in general they follow the same trends. The modeled Δf and Δf from the data both show an inverse correlation with foF2, which confirms that the frequencies of the IAR are controlled by electron density. It follows that Δf is greater around midnight and during the winter months, due to the decrease in plasma mass density. Variability is also reflected when comparing yearly trends in Δf with the sunspot number; higher frequencies are observed and modeled at low sunspot number. It is difficult to examine trends with instantaneous geomagnetic activity as IAR are not visible in spectrograms when geomagnetic activity is high. We find cases where the difference in measured and modeled Δf is significant, suggesting that the model does not capture short term variations in plasma mass density that influence the IAR during these days. We plan to undertake further modeling of Δf on shorter timescales

Modeling and Observations of the Effects of the Alfvén Velocity Profile on the Ionospheric Alfvén Resonator

We have modeled the individual harmonic frequencies of the Ionospheric Alfvén Resonator (IAR) at Eskdalemuir by solving a one-dimensional wave equation and using non-uniform modeled Alfvén velocity profiles. By comparing the results of the modeling alongside harmonics obtained from the Eskdalemuir, UK, data set from 2013 to 2021, the effects of the non-uniformity of the Alfvén velocity profile on the IAR are considered. We calculated the offset between the fundamental frequency and the harmonic frequency separation and found that this is not constant. From this parameter, we infer that the lower boundary condition of the electric field of the IAR is closest to a node, which agrees with previous studies. We compare the results of the non-uniform model with previous uniform models and evaluate their interpretations and the implications for the lower boundary condition

Differential magnetometer measurements in the UK high-voltage power grid

Extreme space weather events can pose risks to ground based infrastructure like high voltage (HV) transformers, railways and gas pipelines through the induction of geomagnetically induced currents(GICs). Modelling GICs requires knowledge about the source magnetic field and the electrical conductivity structure of the Earth. We use the Differential Magnetometer Method (DMM) to indirectly measure GIC in HV lines rather than GIC through ground points

Geomagnetically induced current model validation from New Zealand's South Island

Geomagnetically induced currents (GICs) during a space weather event have previously caused transformer damage in New Zealand. During the 2015 St. Patrick's Day Storm, Transpower NZ Ltd has reliable GIC measurements at 23 different transformers across New Zealand's South Island. These observed GICs show large variability, spatially and within a substation. We compare these GICs with those calculated from a modeled geolectric field using a network model of the transmission network with industry‐provided line, earthing, and transformer resistances. We calculate the modeled geoelectric field from the spectra of magnetic field variations interpolated from measurements during this storm and ground conductance using a thin‐sheet model. Modeled and observed GIC spectra are similar, and coherence exceeds the 95% confidence threshold, for most valid frequencies at 18 of the 23 transformers. Sensitivity analysis shows that modeled GICs are most sensitive to variation in magnetic field input, followed by the variation in land conductivity. The assumption that transmission lines follow straight lines or getting the network resistances exactly right is less significant. Comparing modeled and measured GIC time series highlights that this modeling approach is useful for reconstructing the timing, duration, and relative magnitude of GIC peaks during sudden commencement and substorms. However, the model significantly underestimates the magnitude of these peaks, even for a transformer with good spectral match. This is because of the limited range of frequencies for which the thin‐sheet model is valid and severely limits the usefulness of this modeling approach for accurate prediction of peak GICs

Validating a UK geomagnetically induced current model using differential magnetometer measurements

Extreme space weather can damage ground-based infrastructure such as power lines, railways and gas pipelines through geomagnetically induced currents (GICs). Modeling GICs requires knowledge about the source magnetic field and the electrical conductivity structure of the Earth to calculate ground electric fields during enhanced geomagnetic activity. The electric field, in combination with detailed information about the power grid topology, enable the modeling of GICs in high-voltage (HV) power lines. Directly monitoring GICs in substations is possible with a Hall probe, but scarcely realized in the UK. Therefore we deployed the differential magnetometer method (DMM) to measure GICs at 12 sites in the UK power grid. The DMM includes the installation of two fluxgate magnetometers, one directly under a power line affected by GICs, and one as a remote site. The difference in recordings of the magnetic field at each instrument yields an estimate of the GICs in the respective power line segment via the Biot-Savart law. We collected data across the UK in 2018–2022, monitoring HV line segments where previous research indicated high GIC risk. We recorded magnetometer data during several smaller storms that allow detailed analysis of our GIC model. For the ground electric field computations we used recent magnetotelluric (MT) measurements recorded close to the DMM sites. Our results show that there is strong agreement in both amplitude and signal shape between measured and modeled line and substation GICs when using our HV model and the realistic electric field estimates derived from MT data