The Near Earth Space Environment is a complex and interconnected system of systems, and the home of a multitude of physical processes all contributing to space weather and space climate effects, and hence collaboration across traditional boundaries is essential in order to progress in our understanding of and our capability to predict Space Weather.  

The ESA Swarm Earth Explorer mission, launched 22. September 2013 has completed almost a solar cycle in orbit and is in its nature a true system science mission in its endeavor to unravel our planets invisible shield on a magnetic journey from the Earth’s core to the magnetosphere and nearly everything in-between.

In this presentation we will highlight both the direct contributions the Swarm mission has had and continues to have for the space weather community through constantly evolving products and services, but also how it has acted as a catalyst to help utilize other data sources in order to enhance our understanding of space weather processes.

How to cite: Stromme, A.: How the ESA Swarm mission can contribute to Space Weather and Space Climate , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18152, https://doi.org/10.5194/egusphere-egu24-18152, 2024.

On 22^nd November 2023 Swarm ESA’s Earth Explorer mission celebrated 10 years in Space, characterizing Earth’s geomagnetic, ionospheric and electric fields, for a better understanding of our planet’s interior and its environment. After a decade in orbit, the mission is still in excellent shape and continues to contribute to a wide range of scientific studies, from the core of our planet, via the mantle and the lithosphere, to the ionosphere and interactions with Solar wind, opening the door for many innovating applications largely beyond its original scope.

Moreover, the processing algorithms have been continuously improved since the beginning of the mission, to cope with the evolving needs of the scientific community, to keep providing excellent quality data and to maintain good instruments performances.

In April 2023 a “Fast” processing chain has been transferred to operations, providing Swarm L1B products with a minimum delay respect to the acquisition. This Fast data production adds significant value to Swarm mission’s scientific purposes and applications, making it eligible for monitoring Space Weather phenomena, modelling and nowcasting the evolution of several geomagnetic and ionospheric events.

This work provides an overview of the Swarm enhanced data processing chain, instruments performances, Fast chain applications and upcoming evolutions, together with other innovative Swarm-based data products and services.

How to cite: Forte, R., Qamili, E., Comparetti, N., Tøffner-Clausen, L., Buchert, S., Burchill, J., Siemes, C., Maltese, A., Mizerska, A., Brazal Aragón, M. J., Trenchi, L., Iorfida, E., Cerro Herrero, I., Hoyos Ortega, B., Albini, G., De la Fuente, A., and Stromme, A.: ESA Swarm mission after 10 years in Space: new opportunities through enhanced processors and data quality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17944, https://doi.org/10.5194/egusphere-egu24-17944, 2024.

The coupled Whole Atmosphere Model - Ionosphere Plasmasphere Model (WAM-IPE) has been transitioned into operations at the NOAA Space Weather Prediction Center (SWPC) in 2021. WAM is an extension of the NOAA National Weather Service (NWS) operational model and calculates Earth’s global three-dimensional, time-dependent, neutral atmosphere from the surface up to the thermosphere at 10^-7 hPa (400-600 km). WAM is coupled to the global ionosphere-plasmasphere electrodynamics (IPE) model which extends to several Earth radii. The model is providing a forecast of the neutral and plasma environment that impacts the GNSS positioning, global communications, and collision avoidance for space traffic management.

In this presentation, we describe the different Concept of Operations (CONOPS) which provide nowcast and forecast with WAM-IPE and the validation efforts. We discuss several developments based on the operational version of WAM, which includes the data-assimilation system for WAM and the high-resolution WAM-IPE. A recent testbed exercise targeted satellite operator and solicited feedback from operators and service providers which informs future developments.  We will conclude with the future plans to update WAM to the Finite-Volume Cubed-Sphere Dynamical Core (FV3) version.

How to cite: Maute, A., Fang, T.-W., Fuller-Rowell, T., Kubaryk, A., Li, Z., Millward, G., and Curtis, B.: Operation and developments of the Whole-Atmosphere-Model at NOAA Space Weather Prediction Center, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6683, https://doi.org/10.5194/egusphere-egu24-6683, 2024.

Appropriate metrics to track the progress over time of thermosphere models, both first principle and semi-empirical, were developed. Secondly, the high quality and high-spatial resolution neutral density data sets of TU Delft covering long intervals of time (i.e. the complete CHAMP, GRACE, GRACE-FO and GOCE mission datasets) were selected. The neutral density observations can then be used to verify model accuracy with respect to latitude-longitude-local time variations, and solar and geomagnetic activity levels and seasonal variations, respectively. The density data and metrics together allow benchmarking of the models, and improvement from one release to the next can be quantified. An assessment tool was implemented at CCMC specifically for storm-time assessment, while global assessment capacity is currently also under development.

In this study, we present the results of comparisons with storms from 2001-2022 for the DTM2020, NRLMSISE-00 and JB2008 models. Secondly, the CCMC CAMEL model assessment tool will be shown and the model score cards that it can generate. These score cards allow easy and objective comparison of the performance of thermosphere models.

How to cite: Bruinsma, S., Laurens, S., Wang, J., Yue, J., and Kuznetsova, M.: Thermosphere model assessment and implementation at NASA/CCMC, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7575, https://doi.org/10.5194/egusphere-egu24-7575, 2024.

Ensemble Data assimilation is a state-of-the-art method that can combine the observation information into a numerical model and further improve the performance of numerical weather prediction of the thermosphere and ionosphere.  The thermospheric and ionospheric weather are highly sensitive to the variation of forcings from above, including the solar irradiance and the magnetospheric forcings, and forcings from below, including waves and tides from the lower atmosphere. However, these forcings are hard to quantify. Building up the connection between the uncertainty of the forcings and the variability of a numerical model of the thermosphere and ionosphere that is used in a data assimilation system is a critical issue in thermospheric and ionospheric weather prediction.

This study aims to advance our understanding of how solar irradiance variability and tide and wave variability drive the variability of Earth's thermosphere and ionosphere and improve our capability to represent this driver-response relationship in physics-based models using ensemble data assimilation. This study focuses on the National Center for Atmospheric Research’s (NCAR’s) Whole Atmosphere Community Climate Model – eXtended (WACCM-X). In the WACCM-X, the solar irradiance is determined by an empirical model, such as EUVAC, or by a real data set, and the waves and tides are generated self-consistently from the lower atmosphere in the model.

We first try to quantify the solar irradiance variability in different wavelengths based on real data, including data from the Extreme Ultraviolet Variability Experiment (EVE) on Solar Dynamics Observatory (SDO) and the X-ray Photometer System (XPS)and the Solar Stellar Irradiance Comparison Experiment (SOLSTICE) on Solar Radiation and Climate Experiment (SORCE). Then, we quantify and qualify the response of the thermosphere and ionosphere in the WACCM-X to both the variation of solar irradiance and waves and tides by launching a set of ensemble simulation experiences. This will help prove the predictability of the thermospheric and ionospheric weather. 

How to cite: Hsu, C. T. and Pedatella, N.: Effects of Forcing Uncertainties on the Thermospheric and Ionospheric Ensemble-based data assimilation., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4913, https://doi.org/10.5194/egusphere-egu24-4913, 2024.

Safe and efficient management of the constantly growing air traffic is an important task. For that reason, the international civil aviation organization (ICAO) approved Automatic Dependent Surveillance (ADS) system is used to share information (e.g. position, altitude and speed) between aircraft and air traffic control units. This improves the situational awareness and visibility of aircraft but also the environmental impact and air space capacity. Both applications of ADS, Broadcast (B) and Contract (C), rely on satellite-based communication and navigation services, which can be significantly disturbed by space weather impacts (e.g. loss of the signal and position errors). Therefore, related impacts are also observed for ADS records especially during extreme space weather events. We will present such impacts for both, ADS-B and ADS-C, with first results from an analysis covering periods with solar flares and geomagnetic storms. We will also discuss the challenge of differentiating space weather impacts from other influences. Finally, we will give an outlook how monitoring these impacts could contribute to space weather services.

How to cite: Schmölter, E. and Berdermann, J.: Significance of space weather impacts on Automatic Dependent Surveillance (ADS) data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1512, https://doi.org/10.5194/egusphere-egu24-1512, 2024.

The International Civil Aviation Organization (ICAO) has set up a space weather service for monitoring and raising alerts in case of moderate or severe potential impact on aviation, and covering three domains: GNSS, Radiation and HF Communications. This service is operational since November 2019 and is working in a bi-weekly rotation of four global centers: SWPC (US Space Weather Prediction Center), PECASUS (consortium of 9 European States), CRC (China-Russia Consortium) and ACFJ (Australia-Canada-France-Japan). CLS (Collecte Localisation Satellites), a member of ACFJ, is a subsidiary of the French Space Agency and responsible for delivering near real-time ionospheric scintillation maps. The input data is based on a worldwide network of GNSS receivers, composed of various regional and global networks. The work presented here summarizes the adopted algorithms and pre-processing tasks leading to generate the nowcast maps. The results of a validation work are shown (comparison of indices from geodetic receivers and scintillation monitors) as well as a focus on severe events and their effect on aviation. Finally, taking advantage of a 4-years long data base, the status of a forecasting scintillation model is presented, taking care of separating the EPBs (Equatorial Plasma Bubbles) and geomagnetic storms origins.

How to cite: Yaya, P., Souissi, R., Cherrier, M., and Naouri, A.: Ionospheric Scintillation Nowcasting and Forecasting for Civil Aviation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17954, https://doi.org/10.5194/egusphere-egu24-17954, 2024.

Dedicated scientific measurements of the strength and direction of the Earth's magnetic field began at Greenwich and Kew observatories in London, UK, in the middle of the 19th century. Using advanced techniques for the time, collimated light was focussed onto mirrors mounted on free-swinging magnetized needles which reflected onto photographic paper, allowing continuous analogue magnetograms to be recorded. By good fortune, both observatories were in full operation during the so-called Carrington storm in early September 1859 and its precursor storm in late August 1859. Based on digital images of the magnetograms and information from the observatory yearbooks and scientific papers, it is possible to scale the measurements to SI units and extract quasi-minute cadence spot values. However, due to the magnitude of the storms, the periods of the greatest magnetic field variation were lost as the traces moved off-page. We present the most complete digitized magnetic records to date of the ten-day period from 25th August to 5th September 1859 encompassing the Carrington storm and its lesser recognised precursor on the 28th August. We demonstrate the good correlation between observatories and estimate the instantaneous rate of change of the magnetic field.

How to cite: Beggan, C., Clarke, E., Lawrence, E., Eaton, E., Williamson, J., Matsumoto, K., and Hayakawa, H.: Digitized continuous magnetic recordings for the August/September 1859 storms from London, UK, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11055, https://doi.org/10.5194/egusphere-egu24-11055, 2024.

Due to their socioeconomic impact, the forecasting of coronal mass ejections (CMEs) is of paramount importance. This has led to decades of model development based on the knowledge we have gained from observations and our understanding of the physical processes that take place and could explain these observations. Currently we have empirical and magnetohydrodynamic (MHD) models that show promising results in CME forecasting. However, these are observationally driven and strongly dependent on the quality and quantity of observations we have at our disposal. The vast majority of our CME observations are made on the ecliptic plane. And in situ observations are collected by a few spacecraft scattered in the same plane. In the case of MHD models, we are also limited by numerical implementation issues and our understanding of the CME plasma and magnetic structure, as well as the physical processes CMEs undergo during their journey in the heliosphere.

EUHFORIA (EUropean Heliospheric FORecasting Information Asset), is a state-of-the-art 3-dimensional MHD model that can simulate CMEs in the inner heliosphere, either as hydrodynamic pulses (cone model) or magnetised flux ropes (spheromak, FRiED-3D, torus). Throughout this presentation we will explore the observable parameters the CME implementations in EUHHFORIA depend on and how they are impacted by observational limitations. We will also discuss the performance of the spheromak CME and how we can track it with a novel tool. Last but not least, we will discuss the physical processes manifested in spheromak CME simulations and how they can shed light on the evolution of 3D flux ropes in the interplanetary space depending on the ambient medium (solar wind and interplanetary magnetic field) they are embedded in.

How to cite: Asvestari, E.: Lessons learned from modelling flux ropes with EUHFORIA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7852, https://doi.org/10.5194/egusphere-egu24-7852, 2024.

Predicting the impacts of coronal mass ejections (CMEs) is a major focus of current space weather forecasting efforts. Typically, CME properties are reconstructed from stereoscopic coronal images and then used to forward model a CME's interplanetary evolution. Knowing the uncertainty in the coronal reconstructions is then a critical factor in determining the uncertainty of any predictions. A growing number of catalogs of coronal CME reconstructions exist, but no extensive comparison between these catalogs has yet been performed. Here we develop a Living List of Attributes Measured in Any Coronal Reconstruction (LLAMACoRe), an online collection of individual catalogs, which we intend to continually update. In this first version, we use results from 24 different catalogs with 3D reconstructions using STEREO observations between 2007-2014. We have collated the individual catalogs, determining which reconstructions correspond to the same events. LLAMACoRe contains 2954 reconstructions for 1862 CMEs. Of these, 511 CMEs contain multiple reconstructions from different catalogs. Using the best-constrained values for each CME, we find that the combined catalog reproduces the generally known solar cycle trends. We determine the typical difference we would expect between two independent reconstructions of the same event and find values of 4.0 deg in the latitude, 8.0 deg in the longitude, 24.0 deg in the tilt, 9.3 deg in the angular width, 0.1 in the shape parameter kappa, 115 km/s in the velocity, and 2.5e15 g in the mass. These remain the most probable values over the solar cycle, though we find more extreme outliers in the deviation toward solar maximum.

How to cite: Kay, C. and Palmerio, E.: Collection, Collation, and Comparison of 3D Coronal CME Reconstructions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4076, https://doi.org/10.5194/egusphere-egu24-4076, 2024.

The study of space weather impacts of coronal mass ejections (CMEs) requires the formulation, implementation and validation of predictive approaches to address issues such as the arrival of CMEs to Earth and, if so, its arrival time and speed. The problem of predicting the CMEs’ travel times has been addressed by means of empirical models, physics-based models, and artificial intelligence techniques. In this talk, we propose a physics-driven artificial intelligence (AI) method, in which we encode physical information into the process of neural network training. Specifically we include the drag-based model in the definition of the loss functions to minimize during the training process of a cascade of two neural networks fed with both remote sensing and in situ data. We show that including physical information in the AI architecture improves its predictive capabilities and the proposed physics-driven AI method leads to more accurate and robust results for the CMEs’ travel time prediction with respect to the purely-data driven AI approach.

How to cite: Guastavino, S., Piana, M., Massone, A. M., Marchetti, F., Benvenuto, F., Bemporad, A., Susino, R., and Telloni, D.: Encoding the drag-based model in the artificial intelligence training process to predict CMEs’ travel times, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8210, https://doi.org/10.5194/egusphere-egu24-8210, 2024.

In-situ measurements from the Sun-Earth Lagrangian L1 point typically provide a 20-minute to 1-hour advanced warning of incoming interplanetary (IP) shocks, magnetic clouds before impact at the nose of Earth's magnetopause. Sub-L1 monitors may provide measurements sunward of the L1 point to improve the lead times for such transients to several hours, and various mission architecture have been proposed for more than 25 years. Because CMEs and shocks do not propagate exactly radially, the location of such a monitor with respect to the Sun-Earth line is a key parameter to take into account when designing such missions. Here, we highlight some recent results and measurements of CMEs that show that small angular separations may result in drastic differences in the CME properties measured by two spacecraft, and examples showing that CME evolution over a few hours may differ significantly from the average evolution as obtained from statistical studies over several decades. We highlight how a pathfinder mission is required to better understand the variation of properties within CMEs on moderate scales and the evolution of CMEs over a few hours. Such an improved knowledge will then allow for a dedicated fleet of operational monitors that will improve the lead time of space weather forecasting without a loss of accuracy

How to cite: Lugaz, N., Regnault, F., Banu, S., Al-Haddad, N., Zhuang, B., Lee, C., Farrugia, C. J., Möstl, C., Winslow, R. M., Davies, E., Scolini, C., Yu, W., and Galvin, T.: The Space Weather Research on Coronal Mass Ejections Required Before Operational Sub-L1 Monitors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13486, https://doi.org/10.5194/egusphere-egu24-13486, 2024.

Space weather events can affect Earth. In order to mitigate damage, space weather modelling tools have been implemented. In this study, the full MHD chain is presented, starting from the Sun, with a 3D MHD data-driven coronal model COCONUT up to 0.1 AU, where the code is coupled to Icarus, an ideal 3D MHD heliospheric modelling tool.

COCONUT (Perri, Leitner et al. 2022, COolfluid COrona Unstructured) is a data-driven coronal model that was recently developed at the Centre for Mathematical Plasma Astrophysics, KU Leuven. It is a global 3-D MHD model based on the COOLFluiD code (Yalim et al. 2011, Lani et al. 2014). The advantage of the COCONUT model lies in its efficient, optimised implementation. It uses a time-implicit backward Euler scheme and unstructured computational grid, which avoids singularities near the poles and enables using high CFL numbers to rapidly converge to steady state for realistic simulations on modern HPC systems. In order to obtain realistic solar wind conditions at 0.1AU, the source terms have been implemented in the MHD equations, namely, the approximated coronal heating function, radiative losses and the thermal conduction. The output of the COCONUT coronal model is used as input boundary conditions for plasma variables in the heliospheric model Icarus.

Icarus (Verbeke et al. 2022, Baratashvili et al. 2022) is a new heliospheric wind and CME evolution model that is implemented within the framework of MPI-AMRVAC (Xia et al., 2018) and introduces new capabilities for better and faster space weather forecasts. Advanced numerical techniques, such as solution adaptive mesh refinement (AMR) and radial grid stretching, are implemented. These techniques result in optimised computer memory usage and a significant execution speed-up, which is crucial for forecasting purposes.

The modelled 3D data in the solar corona and heliosphere are presented for assessing the model capabilities. The density profiles near the Sun are compared to tomography data. The time-series profiles of different variables at Earth are compared to observational data. As a result, the COCONUT+Icarus model chain represents the full MHD model covering the domain from Sun to Earth, which allows more in depth studies and understanding of different physics phenomena, e.g. shock formation, erosion, and deformation, compared to empirical or semi-empirical models. 

How to cite: Baratashvili, T., Brchnelova, M., Guo, J. H., Lani, A., and Poedts, S.: A novel full MHD forecasting model chain from Sun to Earth: COCONUT+ Icarus, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5109, https://doi.org/10.5194/egusphere-egu24-5109, 2024.

The European Union's Horizon 2020 Prediction of Adverse effects of Geomagnetic storms and Energetic Radiation (PAGER) project was successfully concluded in 2023. This project provides real-time space weather forecast initiated from the solar observations as well as predictions of radiation in space and its effects on satellite infrastructure. Real-time predictions of particle radiation and cold plasma density allow for evaluation of surface charging and deep dielectric charging. The project provides a 1-2-day probabilistic and data assimilative forecast of ring current and radiation belt environments, which allows satellite operators to respond to predictions that present a significant threat. As a backbone of the project, we use the state-of-the-art codes that currently exist and adapt existing codes to perform ensemble simulations and uncertainty quantifications. Within PAGER, a number of innovative tools was obtained, including data assimilation and uncertainty quantification, new models of near-Earth electromagnetic wave environment, ensemble predictions of solar wind parameters at L1, and data-driven forecast of the geomangetic Kp index and plasma density. In this presentation, we show the overview of the outcomes and the products obtained within the project. The developed codes may be used in the future for realistic modelling of extreme space weather events.

How to cite: Shprits, Y., Bianco, S., Wang, D., Haas, B., Khawaja, M. A., Wilgan, K., Arber, T., Bennett, K., Santolik, O., Kolmasova, I., Taubenschuss, U., Liemohn, M., van der Holst, B., Forest, J., Trouche, A., and Tezenas du Montcel, B.: Prediction of Adverse effects of Geomagnetic storms and Energetic Radiation (PAGER) – project conclusion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10110, https://doi.org/10.5194/egusphere-egu24-10110, 2024.

Providing reliable forecasts of Solar Energetic Particle (SEP) events is mandatory for human spaceflight beyond low-Earth orbit, especially outside the Earth's magnetosphere. High-energy SEPs are tracked because they penetrate deeper into the terrestrial atmosphere and contribute to the radiation dose aboard spacecraft specifically over Canada and the Southern Indian Ocean, due to the tilt of the Earth on its axis. Based on the Relativistic Electron Alert System for Exploration (REleASE) forecasting scheme^], the HESPERIA REleASE product was developed by the HESPERIA H2020 project (Project Coordinator: Dr. Olga Malandraki) and generating real-time predictions of the proton flux (30-50 MeV) at L1, making use of relativistic and near-relativistic electron measurements by the SOHO/EPHIN and ACE/EPAM experiments, respectively. The HESPERIA REleASE tools are operational through the Space Weather Operational Unit of the National Observatory of Athens, accessible through the dedicated website (http://www.hesperia.astro.noa.gr). HESPERIA REleASE has attracted attention from various space organizations (e.g., NASA/CCMC, SRAG), due to the real-time, highly accurate and timely performance offered. ESA selected the HESPERIA REleASE products that were integrated and provided through the ESA Space Weather (SWE) Service Network (https://swe.ssa.esa.int/noa-hesperia-federated) under the Space Radiation Expert Service Center (R-ESC). Solar cycle 25 solar radiation storms successfully predicted by HESPERIA REleASE are presented and discussed. Moreover, we present an innovative upgrade implemented, namely HESPERIA REleASE+, that is using the novel approach of combining for the first time real-time type III solar radio burst observations by the STEREO S/WAVES instrument, thus incorporating clear evidence of particle escape from the Sun, within the HESPERIA REleASE system. To this end, a robust automated algorithm has been developed for the real-time identification and classification of Type III radio burst characteristics, related to intense SEP events at Earth’s orbit. This new implementation leads to a substantial step forward in improving the accuracy and reduction of false alarms.

How to cite: Malandraki, O. E., Posner, A., Karavolos, M., Tziotziou, K., Smanis, F., Laurenza, M., Barzilla, J., Semones, E., Whitman, K., Mays, M. L., Didigu, C., Stubenrauch, C. J., Heber, B., Kuehl, P., Maksimovic, M., Krupar, V., and Milas, N.: Improving the REleASE solar proton forecasting capabilities with evidence of particle escape from the Sun: HESPERIA REleASE + , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10862, https://doi.org/10.5194/egusphere-egu24-10862, 2024.

In order to better safeguard society and infrastructure from space weather hazards, improved forecasting capabilities are required. To maximise the efficiency of mitigation strategies, forecasting products must not only be accurate, but also timely and tailored to end-user needs. For understanding and predicting the behaviour of the near-Earth space environment in changing solar wind conditions, physics-based modelling is extremely powerful, though often comes at considerable computational expense. The Bergen-Imperial Global Geospace (BIGG) project is an ongoing collaborative effort to provide new space weather forecasting capabilities to the ESA space weather service network via the use of two 3D magnetohydrodynamic (MHD) magnetosphere models, GorgonOps and the Space Weather Modelling Framework (SWMF). Solar wind observations as measured in situ at L1 will be continuously and automatically ingested as simulation inputs, with minimal human intervention. Both models have been optimised such that they are able to run in faster than real time, using only modest computational resources, delivering bespoke forecasting products to the end-user community via a web portal and API in a timely fashion. This multi-model approach will provide forecast diversity and redundancy to ensure continuous and reliable service provision to Europe and beyond.

How to cite: LaMoury, A., Heyns, M., Eastwood, J., Kwagala, N., and Hagen, J.-T.: Operational Space Weather Modelling in the Bergen-Imperial Global Geospace (BIGG) Project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13327, https://doi.org/10.5194/egusphere-egu24-13327, 2024.