EMRP2.15

How to cite: Bailey, R. L., Leonhardt, R., Möstl, C., Beggan, C., Reiss, M., Bhaskar, A., and Weiss, A.: Building a GIC forecasting tool based on geomagnetic and solar wind data: challenges and future avenues, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4570, https://doi.org/10.5194/egusphere-egu22-4570, 2022.

The development of mitigation capabilities to counteract the detrimental impacts of space weather on critical ground infrastructure, such as power lines, pipelines and cables, depends on the availability of the observations of their causes as well as monitoring of the subsequent results.
Although direct monitoring of critical infrastructure response to GeoMagnetic Disturbances (GMD) has become more advanced in recent years, observations of geomagnetic variations continue to play the most important role in all aspects of development of safe and robust operational procedures and technology, from the forecast of geomagnetically induced currents (GIC) to their climatological studies.
This presentation shows how different types of geomagnetic data are utilised, from 3-hour and 1-hour geomagnetic indices to 1 sec. geomagnetic data, and from real-time to multi-year climatology in order to provide forecasts of GIC, identify the effects of different geomagnetic patterns on infrastructure response or provide “climatology” for network design considerations.  

How to cite: Trichtchenko, L.: Utilisation of geomagnetic data and indices for GIC applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5792, https://doi.org/10.5194/egusphere-egu22-5792, 2022.

The space environment near Earth is constantly subjected to changes in the solar wind flow generated at the Sun. Examples of this variability are the occurrence of powerful solar disturbances, such as coronal mass ejections (CMEs). The impact of CMEs on the Earth's magnetosphere perturbs the geomagnetic field causing the occurrence of geomagnetic storms. Such extremely variable geomagnetic fields trigger geomagnetic effects measurable not only in the geospace but also in the ionosphere, upper atmosphere, and on the ground. For example, during extreme events, rapidly changing geomagnetic fields generate intense geomagnetically induced currents (GICs). In recent years, GIC impact on the power networks at middle and low latitudes has attracted attention due to the expansion of large-scale power networks into these regions. This work presents the analysis of the geoelectric field determined by the use of the MA.I.GIC. (Magnetosphere - Ionosphere - Ground Induced Current) model, on May 12, 2021 Geomagnetic Storm over the northern hemisphere. In addition, we discriminate between the ionospheric and magnetospheric origin contribution on the geoelectric field in Europe and on the Northern America in order to evaluate their relative contribution to the GIC amplitude.

How to cite: Piersanti, M., D'Angelo, G., and Recchiuti, D.: On the possible magnetospheric and ionospheric sources of the geoelectric field variations during the May 2021 Geomagnetic storm., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1249, https://doi.org/10.5194/egusphere-egu22-1249, 2022.

The paper shows that during very large magnetic storms (VLMS), the energy systems of Kazakhstan are exposed to geomagnetically induced currents for quite a long time (from tens of minutes to several hours). The minute values of the magnetic field vector B or its components Bx, By, Bz during four very large geomagnetic storms with a local geomagnetic activity K-index≥7 were used to calculate the values of geomagnetically induced currents:

- September 26-28, 2011, VLMS, Sc, duration 54 h 00 min, K-index =7;

- June 22-25, 2015, VLMS, Sc, duration 78 h 30 min, K-index =8;

- October 24-28, 2016, VLMS, duration 93 h 00 min, K-index =7;

- May 12-17, 2021, VLMS, Sc, duration 17 h 25 min, K-index =7.

Sc – a sudden commencement of strong storms.

The data of four magnetic observatories of the INTERMAGNET network, whose geomagnetic latitudes are close to the geomagnetic latitudes of the southern and northern borders of Kazakhstan were considered: Alma-Ata Observatory, Kazakhstan (code AAA, 43.25°N, 76.92°E); Novosibirsk Observatory, Russia (code NVS, 54.85N, 83.23E); Irkutsk Observatory, Russia (code IRT, 52.17°N, 104.45°E) and the Beijing Ming Tombs Observatory, Beijing, China (code BMT, 40.3°N, 116.2°E).

Variations of the Bx component of the geomagnetic field during the four considered very large magnetic storms according to the observatories AAA, NVS, IRT, BMT showed variability from 50 nT to 150 nT for several hours.

Also, based on measurements of geomagnetic observatories AAA, NVS, IRT, BMT, the analysis of variations of the horizontal component H of the magnetic field vector and its time derivative (dH/dt) was carried out. Histograms of the distribution dH/dt and histograms of the distribution of the directions H and dH/dt are constructed.

It is shown that the energy systems of Kazakhstan are exposed to geomagnetically induced currents when dH/dt/ varies from 17 nT/min and more. The geomagnetic-induced current is estimated based on the calculation that the electromotive force of self-induction is proportional to the rate of change in the magnetic field strength. According to preliminary calculations, the values of geomagnetic-induced currents are fractions of mA. For more accurate calculations, it is necessary to take into account the topology of the electrical system, the composition of the underlying surface and other factors that determine the degree of susceptibility of individual elements of the power system.

This research has been/was/is funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP09259554).

How to cite: Mukasheva, S., Andreyev, A., Kapytin, V., and Sokolova, O.: Geomagnetically Induced Currents over Kazakhstan during Large Geomagnetic Storms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3338, https://doi.org/10.5194/egusphere-egu22-3338, 2022.

The irregular variation of geomagnetic activity caused by the solar wind interaction with the magnetosphere/ionosphere (space weather) occurs in wide temporal and amplitude ranges. Major geomagnetic storms can induce geoelectric fields in the Earth conducting layers (through the lithosphere and down to the mantle), which may, in turn, be responsible for generating geomagnetically induced currents (GICs). The vulnerability of grounded conducting infrastructures, particularly electrical power transmission systems, to GICs, makes it important to understand the relation between the varying geomagnetic field components and the generated GICs, as well as the role of the local conductivity, i.e., geology, on the inducing process. Looking for proxies that better translate this relation is an open matter of debate.

In this work, we present a comprehensive study of several possible candidates for GIC proxies. We use geomagnetic time series from the Portuguese mid-latitude Coimbra observatory (COI) to calculate geomagnetic indices considering different periods (whole-storm duration, 3-h, 1-h and 1-min), with different focuses on the field components or their derivatives, and discuss their advantages and limitations. We compare the computed indices with both GIC simulations of the Portuguese mainland high voltage power network (150, 220 and 400 kV) (Alves Ribeiro et al., 2021), and observations from a Hall effect sensor based system installed at a power transformer located in the vicinity of Coimbra. 
We then propose a better GIC proxy, an index obtained from geomagnetic field components filtered by convolution with a uniform conductivity Earth model filter (EGIC index), based on previous work by Marshall et al (2010,2011). We search for empirical parameters that may contain information on local conductivity effects and power network geometry.

This study is funded by national funds through FCT (Portuguese Foundation for Science and Technology, I.P.), under the project MAG-GIC (PTDC/CTA-GEO/31744/2017). FCT is also acknowledged for support through projects UIDB/50019/2020-IDL, PTDC/CTA-GEF/1666/2020 (MN) and PTDC/CTA-GEO/031885/2017 (MN). CITEUC is funded by FCT (UIDB/00611/2020 and UIDP/00611/2020). We acknowledge the collaboration with REN (Redes Energéticas Nacionais).

References:
Alves Ribeiro J., F.J. Pinheiro, M.A. Pais, 2021. First Estimations of Geomagnetically Induced Currents in the South of Portugal. Space Weather, 19(1)
Marshall R. A., C. L. Waters, M. D. Sciffer (2010). Spectral analysis of pipe‐to soil potentials with variations of the Earth’s magnetic field in the Australian region. Space Weather 8.5
Marshall, R. A., et al (2011). "A preliminary risk assessment of the Australian region power network to space weather." Space Weather 9.10

How to cite: Gutiérrez Pinheiro, F. J., Neres, M., Pais, M. A., Alves Ribeiro, J., Santos, R., and Cardoso, J.: Towards an improved proxy for geomagnetically induced currents (GICs), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8015, https://doi.org/10.5194/egusphere-egu22-8015, 2022.

Space weather, like solar eruptions, can be hazardous to Earth’s electric grids via geomagnetically induced currents (GIC). In worst cases they can even cause city-wide power outages. GIC is a complicated phenomenon, closely related to the time derivative of the geomagnetic field. However, behavior the time derivative is chaotic and has proven to be challenging to predict. In this study we look at the geomagnetic field orientations at different magnetometer stations in the Fennoscandian region during active space weather conditions.  We aim to characterize the magnetic field behavior, to better understand the drivers behind strong GIC events. One of our main findings is that the direction of time derivative of the geomagnetic field has a very short “reset time“, only a few minutes. We conclude that this result gives insight on the time scale of the ionospheric current systems, which are the primary driver behind the time derivative’s behavior.

How to cite: Kellinsalmi, M., Viljanen, A., Juusola, L., and Käki, S.: Time derivative of the geomagnetic field has a short reset time, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13154, https://doi.org/10.5194/egusphere-egu22-13154, 2022.

Solar flares are known to enhance the ionospheric electron density and thus influence the D- and E-region electric currents in the sunlit hemisphere.  The resultant geomagnetic disturbances (called "crochet") are found at both low latitudes and high latitudes with a minimum in between.  The subsolar response, with short-lived and symmetric changes around the subsolar region, is understood as a temporal re-distribution of the electron density.  However, no systematic study has been made of the high-latitude responses, covering the auroral oval, the cusp, and the dayside sub-auroral region.  Even global patterns are not well described or understood.  

Using data from GOES satellites and SuperMAG, we made a statistical study of the high-latitude geomagnetic responses to X-class solar flares in the northern polar region.  First, we needed to create a reliable X-flare database that we could use to get precise timings of when the flares start and when they stop. We merged XRS databases from different GOES satellites to create a X-class solar flare database between 1984 and 2017, gathering 331 X-flares over 34 years.  

For all these X-flares, we plotted the geomagnetic disturbance (∆B) on a polar map during the periods when the X-ray flux exceeds 1e-4 W/m2 (>X1 flare).  Plots were made also for merged data, i.e., different events on the same map organized by geographic coordinates and local time to obtain the average disturbance pattern caused by the flares.  Large events (∆B >300 nT) were excluded to minimize the contamination from substorm events.  

In these "merged" plots, we classify the data by season (summer - 4 months, equinox ±2 months, winter 4 months), flare intensity (X1-X2 flares and >X2 flares), and maximum ∆B among all stations > 65° GGlat (< 100 nT and 100-300 nT). 

Except for winter, we found a large poleward ∆B which peaks at 13-16 LT, particularly for > X2 flares, but no enhancements in the pre-noon sector.  This asymmetry, surprisingly, remains even after we consider IMF By polarity.  We do not have any plausible explanation for this result, and we will discuss it during the presentation.

[Acknowledgement: This work is resulted from a 2021 summer internship study at the Swedish Institute of Space Physics, Kiruna.   The GOES X-ray data is provided by NOAA (USA). The geomagnetic data at high latitudes are obtained from SuperMAG and are originally provided by DTU (Denmark), TGO (Norway), FMI (Finland), SGO (Finland), SGU (Sweden), GSC (Canada), USGS (USA), AARI (Russia), PGI (Russia), IZMIRAN (Russia), BAS (UK), BGS (UK), IPGP (France), PAS (Poland), ZAMF (Austria), and ASCR (Czech)]

How to cite: Cavarero, P., Yamauchi, M., Johnsen, M. G., Ohtani, S.-I., and Machol, J.: Dawn-dusk asymmetry of solar flare-driven ionospheric current at high latitudes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6269, https://doi.org/10.5194/egusphere-egu22-6269, 2022.

The development of satellite measurements over the past four decades has allowed us to understand the magnetic field in the Earth environment at higher temporal and spatial resolution than before. This is most evident for satellite ensembles such as ESA’s Swarm constellation which allows simultaneous global coverage with three independent satellites.

Thanks to Swarm’s particular configuration, we can take advantage of the Local Time sampling difference between Swarm A/C and Swarm B in order to estimate the low degree variation of the external magnetic field in latitude and longitude. We separate the external and induced fields measured at satellite altitude, and obtain the spherical harmonic decomposition of each source to degree and order 3 twice per day. However, there is a trade-off between spatial and temporal resolution and clear disadvantages occur when the measured field varies rapidly during a geomagnetic storm, since the method used will result in coefficients of the averaged field over the chosen time interval rather than the peaks.

We compare our results with previous models of the external field during the St Patrick storm 2015, which used up to four different local time simultaneous coverage, as well as during quiet times and lesser storms using our own solutions. We find good agreement in each case.

In this talk we will describe the algorithm and methodology used and show results over the lifetime of the Swarm mission to date (2013-). A new daily product for the Swarm mission (MMA_SHA_2E) is being developed.

How to cite: Gomez Perez, N. and Beggan, C.: Swarm Fast Track spherical harmonic model of the external magnetic field to degree and order 3, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4733, https://doi.org/10.5194/egusphere-egu22-4733, 2022.

Geomagnetic indices can have a central role in the mitigation of ground effects due to space weather events, for instance when their reliable forecasting will be achieved. To this purpose, machine learning techniques represent a powerful tool. Here, we use two conceptually different neural networks to forecast the SYM-H index: the long short-term memory (LSTM) and the convolutional neural network (CNN). We build two models and train both of them using two different sets of input parameters including interplanetary magnetic field components and magnitude and differing for the presence or not of previous SYM-H values. Both models are trained, validated, and tested on a total of 42 geomagnetic storms among the most intense that occurred between 1998 and 2018. Results show that both models are able to well forecast SYM-H index 1 hour in advance. The main difference between the two stands in the better performance of the one based on LSTM when SYM-H index is included in the input parameters and, contrarily, in the better performance of the one based on CNN for predictions based only on interplanetary magnetic field data.

How to cite: Siciliano, F., Consolini, G., and Giannattasio, F.: Comparing long short-term memory and convolutional neural networks in SYM-H index forecasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11344, https://doi.org/10.5194/egusphere-egu22-11344, 2022.

Geomagnetic storms have the potential for significant impact on a wide range of technologies, including aviation, communications and power transmission grids. The likelihood of occurrence of geomagnetic storms varies with the solar cycle of level of activity, and each solar cycle has a unique amplitude and duration. The space weather response at earth then varies within and between successive solar cycles and can be characterized by the statistics of bursts, that is, time-series excursions above a threshold, in geomagnetic indices derived from ground based magnetometer observations. We consider non-overlapping 1 year samples of the minute-resolution auroral electrojet index (AE) and the minute-resolution SuperMAG-based ring current index (SMR), across the last four solar cycles. These indices respectively characterize high latitude and equatorial geomagnetic disturbances. We propose that average burst duration T and burst return period R (that is, the time between successive upcrossings of the threshold) form an activity parameter, T/R [1] which characterizes the fraction of time the magnetosphere spends, on average, in an active state for a given burst threshold. If the burst threshold takes a fixed value, T/R for SMR tracks sunspot number, while T/R for AE peaks in the solar cycle declining phase. Level crossing theory directly relates T/R to the observed index value cumulative distribution function (cdf). For burst thresholds at fixed quantiles, we find that the probability density functions of T and R each collapse onto single empirical curves for AE at solar cycle minimum, maximum, and declining phase and for -SMR at solar maximum. Moreover, underlying empirical cdf tails of observed index values collapse onto common functional forms specific to each index and cycle phase when normalized to their first two moments. Together, these results offer operational support to quantifying space weather risk which requires understanding how return periods of events of a given size vary with solar cycle strength.

[1] A. Bergin, S. C. Chapman, N. Moloney, N. W. Watkins, Variation of geomagnetic index empirical distribution and burst statistics across successive solar cycles, J. Geophys. Res, in press (2022)

How to cite: Bergin, A., Chapman, S., Moloney, N., and Watkins, N.: A parameter for the solar cycle variation in geomagnetic activity as quantified by bursts in the AE and SMR indices, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1797, https://doi.org/10.5194/egusphere-egu22-1797, 2022.

When the interplanetary magnetic field is characterized by nearly-southward conditions, the near-Earth magnetospheric environment and, specifically, the plasma circulation and the magnetospheric-ionospheric current systems undergo to some dynamical changes to dissipate the excess of energy-momentum and mass transfer from interplanetary medium to the magnetosphere. Geomagnetic storms and magnetospheric substorms are the macroscopic manifestation of such a response and their relation is one of the critical issues of the magnetospheric dynamics. In this framework, a very old and widely debated topic is the storm-substorm relations, such as for instance the role of substorms in developing a storms. In recent years, some novel methods developed in the ambit of the information theory, such us the transfer entropy, have been applied to unveil the directionality of the information flow between storms and substorms (De Michelis et al., 2011, Stumpo et al, 2020). However, these results have been partially criticised suggesting that there is not a clear net transfer of information between substorms to storms. However, the use of information theory methods which relies on time averages could hide the dynamics of the information flow. Indeed, the absence of a net information exchange between storms and substorms may be due to the fact that it is enhanced only during activity periods, so that it may be canceled out if transfer entropy is computed by averaging together quiet and activity periods.Here, we attempt an instantaneous estimation of the magnetospheric internal transfer of information during the occurrence of geomagnetic storms using an ensemble-based transfer entropy analysis. In detail using some geomagnetic indices as proxies of magnetospheric-ionosphere dynamics during geomagnetic storms, we investigate the directionality of the information flow within the magnetosphere-ionosphere system during the occurrence of periods of magnetic storms and substorms.

This work received funding by Italian MIUR-PRIN grant 2017APKP7T on Circumterrestrial Environment: Impact of Sun-Earth Interaction.

How to cite: Stumpo, M., Consolini, G., Benella, S., and Alberti, T.: Information flow within the magnetosphere-ionosphere system: insights from ensemble-based transfer entropy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11323, https://doi.org/10.5194/egusphere-egu22-11323, 2022.

In order to monitor space weather events and their impacts, ground magnetic field data has proven to be a long-lasting and powerful source of information. For the determination of space weather effects, it is essential to extract and understand the evolution of the quiet-time magnetic field. However, the data shows a high degree of complexity since the Earth’s magnetic field is a superposition of sources that cover a broad amplitude and frequency spectrum. In sub-auroral regions, it is well understood that the solar quiet current system contributes to the quiet signal with smooth patterns that depend on season and local time, having distinct periods of 24 hours and beneath.

In this work, we apply signal filtering techniques on time-series magnetic data from ground observatories in sub-auroral regions. In order to extract the solar quiet current contributions, we use its specific periods of 24h and beneath and analyse the results with respect to season, local time, and day-to-day variability between 1991 and 2019. Careful investigations and interpretation of the contributing sources are given, confirming the main contribution to the filtered signal is the solar quiet current system. This implies that the filter approach is able to extract the quiet magnetic field variations and due to its simplicity may be used for real-time determination of the quiet magnetic field, including magnetic baselines. 

How to cite: Haberle, V., Marchaudon, A., Chambodut, A., and Blelly, P.-L.: Extraction of ground magnetic signatures from solar quiet current systems in sub-auroral regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1416, https://doi.org/10.5194/egusphere-egu22-1416, 2022.