Radial diffusion, generated by ultra-low frequency (ULF) waves, is an important process for the transport of electrons in the outer radiation belt. Ground magnetometers give us constant observations of such ULF waves but their usefulness is limited by the models used to transform ground measurements into their progenitor fields in space. A typical assumption is that of a dipole field, and so measurements can only be reliably performed during the day. We have chosen a series of magnetometer stations at different sets of geomagnetic latitudes, with stations in each set separated by several hours in longitude, and compared their Pc5 ULF power measurements, and the resulting calculated radial diffusion coefficients, from 11 years of their data. By comparing the mismatch of their results when they were in different time sectors, and for different values of Kp, we were able to assess the median deviation of measurements conducted before dawn or after dusk with those on the day side. This will be useful for projects requiring a longitudinally limited array of magnetometers or for parts of the world where such coverage is limited, such as FARBES (Forecast of Actionable Radiation Belt Scenarios).

How to cite: Dimitrakoudis, S., Balasis, G., Boutsi, A. Z., Daglis, I. A., Papadimitriou, C., Katsavrias, C., Georgiou, M., and Lichtenberger, J.: Empirical parameterisations of ULF power and drift orbit averaged radial diffusion coefficients from ground magnetometers separated in MLT, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19294, https://doi.org/10.5194/egusphere-egu25-19294, 2025.

Using auroral boundaries determined from far ultraviolet images of the aurora (Chisham et al., 2022), we have examined auroral occurrence with respect to magnetic latitude, local time and the Kp index. Our results show that auroral occurrence is highly correlated (R²>90%) with Kp between values of 0o and 5o. We use linear fits between occurrence and Kp to build a probabilistic Auroral Location Forecast (ALF) which gives the likelihood of the aurora occuring at a given magnetic latitude and local time for any level of Kp. The model includes both correlated relationships between Kp and occurrence at low latitudes and anti-correlated relationships between Kp and occurrence at high latitudes, enabling the model to replicate behaviour expected within the expanding-contracting polar cap paradigm. The model also shows higher variability in the location of the auroral boundary close to the interface between the upward Region 2 currents and downward Region 1 currents. Validation of the model returns high Brier Skill Scores for both the range of Kp used in the model creation (Kp=0 – 5, Brier Skill Score = 0.569) and the range unseen by the model (Kp=6 – 9, Brier Skill Score = 0.532) indicating that the model is skillful in predicting the location of the aurora. The results of our analysis and outputs of ALF may be of interest to space weather professionals and ‘aurora chasers’ in determining the likelihood of aurora being present, particularly when coupled with forecasts of the Kp index.

How to cite: Forsyth, C., Mooney, M., Chisham, G., Smith, A., Lao, C., and Paxton, L.: ALF - A Probabilistic Auroral Location Forecast derived from Far-Ultraviolet Auroral Boundaries and Geomagnetic Activity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10990, https://doi.org/10.5194/egusphere-egu25-10990, 2025.

Reliable forecasts with sufficient advance warning of Solar Energetic Particle (SEP) events (with energies ranging from tens of keV to a few GeV and lasting for a few hours to several days), are vital for swift mitigation of threats to modern technology, spacecraft, avionics and under extreme circumstances commercial aircraft. Moreover, such forecasts are imperative for minimizing radiation hazards to astronauts especially on future Lunar or Mars missions. To this end, the HESPERIA Relativistic Electron Alert System for Exploration (REleASE) forecasting tools provide real-time predictions of the proton flux (30-50 MeV) at L1 based on relativistic and near-relativistic electron measurements by the SOHO/EPHIN and ACE/EPAM experiments using relevant proton forecasting matrices created from historical electron and proton data. Likewise, the recently developed STEREO REleASE forecasting scheme provides real-time predictions of proton flux (21-40 MeV) at the current location of STEREO-A, relying on electron measurements by the Solar Electron Proton Telescope (SEPT) and the High Energy Telescope (HET) onboard the spacecraft  and relevant forecasting matrices that were derived from an analysis of 15-years of historical SEPT/HET electron and HET proton data. We, hereby, report on two novel implementations, namely HESPERIA REleASE+ and STEREO REleASE+, that combine for the first time real-time Type III solar radio burst observations by the STEREO S/WAVES instrument, as clear evidence of particle escape from the Sun, within the HESPERIA and STEREO REleASE systems respectively, aiming to substantially improve their accuracy and reduce false alarms. The identification of Type III radio bursts and their qualification as a precondition for intense SEP events occurring either at Earth or STEREO location is provided by a robust automated algorithm that recently resulted from an international collaboration between partners with complementary expertise on particles and radio data. These real-time and highly accurate forecasting schemes, which are currently operational and accessible through the Space Weather Operational Unit of the National Observatory of Athens (http://www.hesperia.astro.noa.gr), have attracted attention from various space organizations (e.g., NASA/CCMC, SRAG) and some of them are now integrated and provided through the ESA Space Weather (SWE) Service Network (https://swe.ssa.esa.int/noahesperia-federated) under the Space Radiation Expert Service Center (R-ESC).

How to cite: Malandraki, O., Tziotziou, K., Karavolos, M., Droege, H., Heber, B., Kuehl, P., Barzilla, J., Semones, E., Whitman, K., Mays, M. L., Didigu, C., J. Stubenrauch, C., Laurenza, M., Maksimovic, M., Krupar, V., and Milas, N.: Using Type III radio bursts as evidence of particle escape from the Sun for enhancing solar proton forecasting capabilities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18393, https://doi.org/10.5194/egusphere-egu25-18393, 2025.

Solar flare forecasting is an essential component in space environment forecasting. Most of the deep learning flare forecasting models constructed are based on the magnetograms of active regions. Affected by the projection effect, these models can only forecast the active region in the center of the Sun. It is difficult to meet the need of operational flare forecasting of the solar full disk. Based on the traditional solar activity parameters, in this study, the relationships between the magnetic type of the active region, area of the active region, the history of the flare outburst, the 10 cm radio flux and flares from January 1996 to December 2022 were statistically analyzed. By using the fully connected neural network, an operational flare forecasting model for solar full disk active regions was constructed. This model can forecast the eruption of the M-class or above flares of the full solar disk active regions in the next 48 h. The F1 score of the model is 0.4304, the TSS is 0.3689, and the HSS is 0.3906. The model is compared with the deep learning flare forecasting model constructed in the previous work, and the results show that the operational forecasting model constructed in this paper has a better forecasting performance. Meanwhile, in order to explore the influence of the projection effect, the solar full disk active regions flare forecasting model constructed was tested for test data within 30 degrees from the center of the solar disk, within the interval from 30 degrees to 60 degrees, and over 60 degrees, respectively. The results show that the projection effect has little influence on the flare forecast model constructed in this study. The model can be used to forecast flares in the active region of the full solar disk, and provide an effective tool for operational solar flare forecasting.

How to cite: Li, M.: Machine Learning Solar Full Disk Flare Operational Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8513, https://doi.org/10.5194/egusphere-egu25-8513, 2025.

The non-linear dynamics of the solar wind cover multiple decades of scales. These scales are not independent and are linked by turbulent processes. For instance, the energy will cascade from the largest scales determined by the dynamic and structure of the corona, to the smallest where kinetic dissipation and heating of the plasma occur. Mesoscale structures of size superior to the minute can be induced or affected by the turbulent cascade of energy or can generate a cascade. They have the right size to interact quasi-stationarily with the magnetosphere. However, are they well reproduced in space weather forecasts? We question the scale-by-scale accuracy of solar wind forecasts using turbulent-state diagnostics. These forecasts are obtained from the ensemble-analogue methodology applied to L1 measurements. We will discuss the implications of our results for space weather forecasting.

How to cite: A. Simon, P., H. K. Chen, C., and J. Owens, M.: Turbulence diagnostics of scale-by-scale accuracy of solar wind forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11986, https://doi.org/10.5194/egusphere-egu25-11986, 2025.

Coronal mass ejections (CMEs) are the main drivers of severe space weather at Earth which can cause significant disruption to both satellite and ground systems, necessitating accurate predictions for timely mitigation. The complicated nature of the processes affecting CMEs as they propagate makes understanding and predicting their physical properties and global structure a challenging task, from both a fundamental and practical space weather perspective. Current challenges lie in forecasting CME arrival time and magnetic structure prior to Earth arrival, closely related to our ability to directly measure their magnetic field configuration between the Sun and 1 AU, which is critical for assessing their geo-effectiveness.

Recent opportunities provided by Solar Orbiter crossing the Sun-Earth line have allowed us to monitor upstream solar wind conditions in real-time. On 23 March 2024, Solar Orbiter observed a fast CME whilst located upstream of Earth at 0.39 AU. It provided observations of the CME magnetic field vector in real time, with a lead time of over one day before Earth impact. We present the analysis performed that led to the first real-time prediction of the geomagnetic magnitude of a severe geomagnetic storm (minimum Dst -130 nT) with sufficient accuracy and lead time. Our results demonstrate the necessity of future real-time upstream solar wind monitors towards providing accurate and timely predictions of space weather effects.

How to cite: Davies, E., Möstl, C., Weiler, E., Rüdisser, H., Amerstorfer, U., Horbury, T., O’Brien, H., Morris, J., Crabtree, A., and Fauchon-Jones, E.: Prediction of coronal mass ejection geo-effectiveness using Solar Orbiter as a far upstream monitor in real-time, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16359, https://doi.org/10.5194/egusphere-egu25-16359, 2025.

How to cite: Rüdisser, H. T., Nguyen, G., Le Louëdec, J., and Möstl, C.: ARCANE: An Operational Framework for Automatic Realtime ICME Detection in Solar Wind In Situ Data  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3560, https://doi.org/10.5194/egusphere-egu25-3560, 2025.

We present the new Solar Wind Scoreboard, which is hosted by NASA’s Community Coordinated Modeling Center (CCMC) and developed with the community as part of the COSPAR ISWAT initiative. The Solar Wind Scoreboard will serve the space weather and science community as a hub for real-time solar wind predictions at Earth, for viewing the ensemble of community-contributed models, and for comparing the performance of these models during extreme space weather events. Our overarching objective is to identify models that show potential to improve operational services. In this presentation, we will share our progress from the COSPAR ISWAT Workshop in Cape Canaveral, FL, USA, focusing on the open information architecture, including metadata standards, automated prediction submissions, and front-end development. Additionally, we will discuss how the Solar Wind Scoreboard integrates with existing CCMC Scoreboards and feeds into the new Geospace Scoreboard. We will share lessons learned from running models like AWSoM (University of Michigan) and ICARUS (KU Leuven) in real-time, and how we integrate their results into the scoreboard. Finally, we will outline future plans and how we envision broader community engagement in line with open science principles.

How to cite: Reiss, M., Mays, L., Kuznetsova, M., Huang, Z., Baratashvili, T., Petrenko, M., Kubaryk, A., and Henley, E.: The Solar Wind Scoreboard hosted by NASA’s CCMC, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20309, https://doi.org/10.5194/egusphere-egu25-20309, 2025.

The enhanced variation of the magnetic field during severe to extreme geomagnetic storms induces a large geoelectric field in the subsurface. Grounded infrastructure can be susceptible to geomagnetically induced currents (GICs) during these events. Modelling the effect in real time and forecasting the magnitude of GICs are important for allowing operators of critical infrastructure to make informed decisions on potential impacts. As part of the UK-funded SWIMMR programme, we implemented nine research-level models into operational codes capable of running consistently and robustly to produce estimates of GICs in the Great Britain high voltage power transmission network, the high pressure pipeline network and the railway network. To improve magnetic coverage and geoelectric field modelling accuracy, three new variometer sites were installed in the UK and a three year campaign of magnetotelluric measurements at 53 sites was undertaken. The models rely on real time ground observatory data and solar wind data from satellites at the L1 Lagrange point. A mixture of empirical machine learning and numerical magnetohydrodynamic models are used for forecasting. In addition to nowcast capabilities, contextual information on the likelihood of substorms, sudden commencements and large rates of magnetic field change were developed.  The final nowcast and forecast codes were implemented in a cloud-based environment using modern software tools and practices. We describe the process to move from research to operations (R2O) and give examples from the largest storms in 2024.

How to cite: Beggan, C.: Research to Operations: Implementing cloud-based real-time operational magnetic, geoelectric and GIC models for the UK, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2804, https://doi.org/10.5194/egusphere-egu25-2804, 2025.

A key space weather hazard is the generation of Geomagnetically Induced Currents (GICs) in grounded, conducting infrastructure (e.g., power networks).  These GICs are driven by the changing magnetic field at the surface of the Earth and in extreme cases can cause disruption or even damage to power systems.  Due to a sparsity of GIC measurements around the globe, the rate of change of the magnetic field (e.g., H’) is often used as a proxy, under the assumption that larger rates of change of the geomagnetic field will be related to larger GICs.  While a range of magnetospheric processes can result in large GICs, in this work we focus on one: Sudden Commencements (SCs).  SCs are rapid, coherent changes in the geomagnetic field caused by the impact of a large increase in solar wind dynamic pressure (e.g., an interplanetary shock).  Globally, in one-minute cadence ground magnetic field data SCs appear relatively homogenous, lasting only a few data points.  However, it has previously been found that in New Zealand SCs on the dayside have been linked to 30% larger measured GICs for a given H’.  We investigate a deceptively simple question: why?

In this work we examine the sub-minute structure of SCs in New Zealand and their impact on the resulting GICs.  We introduce an analytical model that describes the key features of the magnetic field signature, allowing us to fully describe the key features of an SC.  The use of parameters (e.g., maximum H’) from the fitted analytical model strengthens the correlation between maximum H’ and GIC during SCs, but leaves remnant dependencies which are yet to be explained.  We conduct synthetic experiments with our analytical SC model and a high resolution magnetotelluric-derived map of the southern part of New Zealand to examine which properties of an SC make it more-likely to cause disproportionately large GIC.

How to cite: Smith, A. W., Rodger, C., Pratscher, K., MacManus, D., Rae, J., Ratliff, D., Clilverd, M., Lawrence, E., Beggan, C., Richardson, G., Fogg, A., Oliveira, D., Petersen, T., and Dalzell, M.:  Why do Some Sudden Commencements Generate “Disproportionate” Geomagnetically Induced Currents?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9258, https://doi.org/10.5194/egusphere-egu25-9258, 2025.

Severe geomagnetic storms can induce geomagnetically induced currents (GICs) in infrastructures like power grids, leading to transformer overheating, voltage instability, and power outages. With the rapid expansion and large-scale deployment of these systems worldwide, space weather effects have become an increasing concern. To address the urgent need to assess the impacts of space weather, a systematic modelling framework is essential. However, the absence of complete grid data has hindered the development of a GICs model during geomagnetic storms. This study proposes a systematic GICs modeling methodology that involves reconstructing power grid topology using open-source geographic data, calculating induced electric fields from magnetic disturbances, and analyzing GICs at substation nodes and transmission lines. Firstly, we accurately reconstruct the UK power grid. The high-resolution reconstructed grid model establishes a solid foundation for calculating the electromagnetic properties of key nodes and transmission lines. Next, we use geomagnetic field data from observatories during geomagnetic storms to perform geomagnetic field interpolation and induced electric field modeling, followed by the computation of GICs at substations within the grid. The model is further applied to simulate the geoelectric fields in Japan, demonstrating high accuracy. Additionally, GIC analysis during magnetic storms is conducted for a specific region in China, revealing that even low-latitude areas of China's power grid can be significantly affected by strong magnetic storms. This study establishes a systematic model that takes geomagnetic field data as input and outputs GICs at substation nodes, providing a new tool for GICs modelling induced by geomagnetic storms. It holds significant implications for assessing and managing the operational security of power grids during geomagnetic storms.

How to cite: Chen, W., Yuan, D., Zhu, Y., and Yin, T.: National-scale power grid modelling and space weather application with open-source data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3034, https://doi.org/10.5194/egusphere-egu25-3034, 2025.

This talk applies artificial intelligence (AI) to predict the whole chain of events associated with the May 2024 superstorm, including solar flares from NOAA active region (AR) 13644, Earth-directed CMEs, and a violent geomagnetic storm. Specifically, we will show that, using magnetogram cut-outs, a Vision Transformer was able to classify the evolution of the AR morphology and a video-based deep learning could have predicted the occurrence of solar flares; further, using remote sensing and in-situ observations, we will show that physics-driven models were able to improve the accuracy of CME travel time prediction and provide timely alert of its geomagnetic impact.  The results showed unprecedented accuracy in predicting both solar flares and geomagnetic disturbance occurrences, as well as CME arrival, with uncertainty as low as one minute.

How to cite: Legnaro, E., Guastavino, S., Massone, A. M., and Piana, M.: AI could have predicted the whole chain of events associated with the may 2024 space weather superstorm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19525, https://doi.org/10.5194/egusphere-egu25-19525, 2025.

On 8 May 2024, the solar active region AR13664 started releasing a series of intense solar flares. Those of class X released between 9 and 11 May 2024 gave rise to a chain of fast Coronal Mass Ejections (CMEs) that proved to be geoeffective. The Storm Sudden Commencement (SSC) of the resulting geomagnetic storm was registered on 10 May 2024 and it is, to date, the strongest event since November 2003. The May 2024 storm, named hereafter Mother’s Day storm, peaked with a Dst of -412 nT and exhibited almost no substorm signatures in the recovery phase.

This study deals with the Space Weather effects that the Mother’s Day storm had on the Mediterranean sector, with a special focus on Italy. Istituto Nazionale di Geofisica e Vulcanologia (INGV) operational manages and monitors a dense network of GNSS receivers (including scintillation receivers), ionosondes and magnetometers in the Mediterranean area, which facilitated a detailed characterization of the storm effects.

Geomagnetic observatories located in Italy recorded a SSC with a rise time of only 3 minutes and a maximum variation of around 600 nT. The most notable ionospheric effect following the arrival of the disturbance was a significant decrease in plasma density on 11 May, resulting in a pronounced negative ionospheric storm registered on both foF2 and Total Electron Content (TEC). These negative ionospheric phases were ascribed to neutral composition changes and, specifically, to a decrease of the [O]/[N2] ratio. The IRI UP IONORING data-assimilation procedure, recently developed to nowcast the critical F2-layer frequency (foF2) over Italy, proved to be quite reliable during this extreme event. Relevant outcomes of the work relate to the Rate of TEC change Index (ROTI), which showed unusually high spatially distributed values on the nights of 10 and 11 May. The ROTI enhancements on 10 May might be linked to Stable Auroral Red (SAR) arcs and an equatorward displacement of the ionospheric trough. Differently, the ROTI enhancements on 11 May might be triggered by a joint action of low-latitude plasma pushed poleward by the pre-reversal enhancement (PRE) in the post-sunset hours and wave-like perturbations propagating from the north.

The storm attracted also the general public’s attention to Space Weather effects, including mid-latitude visible phenomena like SAR arcs. This presentation outlines also the monitoring report of the Space Weather Monitoring Group (SWMG) of the INGV Environment Department and its effort to timely disseminate information about this exceptional event.

How to cite: Pignalberi, A. and the Space Weather Monitoring Group (SWMG) of the INGV Environment Department: The geomagnetic and ionospheric effects of the May 2024 Mother’s Day superstorm over the Mediterranean sector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4340, https://doi.org/10.5194/egusphere-egu25-4340, 2025.

In this study, we present the results of a comprehensive assessment of thermosphere models under geomagnetic storm conditions, defined by a geomagnetic index ap ≥ 80. This work builds upon Bruinsma et al. (2024, DOI: 10.1051/swsc/2024027), which evaluated the performance of empirical and physics-based thermosphere models during storm periods. Utilizing models hosted at NASA's Community Coordinated Modeling Center (CCMC), we conduct an unbiased evaluation of their performance. Model simulations are analyzed across four storm phases—pre-storm, onset, recovery, and post-storm—relative to the time of peak ap. After applying a debiasing procedure based on the pre-storm phase, we compare the modeled neutral density data to high-fidelity observational datasets from TU Delft, derived from CHAMP, GOCE, GRACE, GRACE-FO, and SWARM-A satellites.

Key performance metrics, including mean density ratios, standard deviations, and correlation coefficients, are used to construct thermosphere model scorecards. These scorecards provide a valuable resource for users to identify the most suitable model for specific applications. The ultimate objective of this study is to establish a near-real-time scorecard for thermosphere model assessment at NASA/CCMC, employing consistent and standardized metrics.

How to cite: Wang, J., Yue, J., Bruinsma, S., Sypal, J., Kuznetsova, M., Mullinix, R., Wiegand, C., Siemes, C., Laurens, S., Dimarzio, P., Chou, M.-Y., and Petrenko, M.: Comprehensive Assessment of Thermospheric Models During Geomagnetic Storms at NASA/CCMC, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1673, https://doi.org/10.5194/egusphere-egu25-1673, 2025.

The Earth’s thermosphere plays a critical role in the Earth’s response to space weather and satellite drag in particular. The relationship between thermospheric variability and satellite drag is relatively straightforward: A hotter thermosphere results in higher densities at all altitudes, directly increasing satellite drag. Despite its crucial role in space weather, there are presently no operational direct measurements of the thermospheric state. Instead, today, the thermospheric state can only be estimated by driving numerical models with known space weather drivers, or by assimilating spatiotemporally averaged satellite drag data into such models.

The NOAA GOES satellites include a suite of measurements for space weather operations including magnetic field, energetic particle flux, solar soft x-ray and EUV irradiance and solar corona imagery, but historically have not provided measurements of the upper atmosphere. This may soon change. Recent NASA and NOAA funded projects have derived upper atmospheric densities from GOES solar measurements during solar occultations, which include measurements of exospheric hydrogen density from ~1000 km to 40,000 km using the GOES EXIS instrument and thermospheric O and N₂ density from ~180 km to 400 km using the GOES SUVI instrument.  In this presentation, we review the new datasets, discuss their capabilities and limitations, and provide examples of both longer-term (solar cycle) and transient (geomagnetic storm) variability. Additionally, we discuss what improvements could be made for future sensors intended for thermospheric measurements.

How to cite: Thiemann, E., Machol, J., Sewell, R., Bhattacharyya, D., and Bethge, C.: Exosphere and thermosphere density measurements from GOES for space weather operations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13196, https://doi.org/10.5194/egusphere-egu25-13196, 2025.

The mission of NOAA’s Space Weather Prediction Center (SWPC) is to provide space weather products and services to stakeholders by issuing alerts, watches, and warnings. For the satellite and GNSS users, different thermosphere and ionospheric products have been developed. These ionosphere-thermosphere products are based on first-principle and data-assimilative models. To operate stable 24/7, the products have gone through long cycles of development, verification, and validation. Once in operation, products need to be continuously evaluated to assess their performance in the operational environment and identify areas of improvement. The validation was named, among others, as a need in the recently released NOAA Space Weather Advisory Group (SWAG) report.  Results of validations can guide improvements which can be addressed with the help of the research community. In addition, SWPC continuously interacts with customers to identify their needs and ensure that developed products and tools are useful and meet the requirements. In this presentation, we will describe the ionospheric and thermospheric data products provided by SWPC and the validation efforts. We will discuss plans to improve the data products / tools and expand the evaluation effort.

How to cite: Maute, A., Fang, T.-W., Fuller-Rowell, T., Kubaryk, A., Li, Z., Fuller-Rowell, D., Durgonics, T., Fedrizzi, M., Karol, S., Millward, G., and Curtis, B.: NOAA SPWC’s ionospheric and thermospheric data products – status and plans, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7325, https://doi.org/10.5194/egusphere-egu25-7325, 2025.

For the past three decades, ionospheric drift velocity measurements from the Super Dual Auroral Radar Network (SuperDARN) have been combined at a nominal time resolution of two minutes to produce horizontal patterns of the high-latitude convective flow. Recently, SuperDARN radars operated by the University of Saskatchewan (codenamed Borealis), which overlook much of the northern hemisphere polar cap, have been upgraded to enable a form of scanning which can be carried out every 3.7 seconds without compromising on the large field-of-views of the radars. When data from all Borealis radars are combined, a 32-fold temporal resolution improvement over traditional SuperDARN convection maps is achieved. We call this new data product the Fast Borealis Ionosphere (FBI).

The SuperDARN FBI allows for the study of highly transient and quickly evolving ionospheric phenomena (or the order of seconds) that span several thousands of kilometres, such as transient flow bursts, polar cap patches, substorm-related enhancements, and more. In this presentation, we show FBI results for events highlighting its capabilities in capturing transient ionospheric dynamics, along with several conjunction studies with satellites and other ground-based instruments (such as all-sky cameras).  

How to cite: Billett, D., Rohel, R., Martin, C., McWilliams, K., and Laundal, K.: The Fast Borealis Ionosphere: New observations and insights from mapping the polar ionosphere every four seconds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1313, https://doi.org/10.5194/egusphere-egu25-1313, 2025.