EUropean Heliospheric FORecasting Information Asset (EUHFORIA) is a physics-based data-driven solar wind and CME propagation model designed for space weather forecasting and event analysis investigations. Although EUHFORIA can predict the solar wind plasma and magnetic field properties at Earth, it is not equipped to quantify the geoeffectiveness of the solar transients in terms of the geomagnetic indices like the disturbance storm time (Dst) index and the eauroral indices that quantify the impact of the magnetized plasma encounters on Earth's magnetosphere. Therefore, we couple EUHFORIA with the Open Geospace General Circulation Model (OpenGGCM), a magnetohydrodynamic model of the response of Earth's magnetosphere, ionosphere, and thermosphere, to transient solar wind characteristics. In this coupling, OpenGGCM is driven by the solar wind and interplanetary magnetic field obtained from EUHFORIA simulations to produce the magnetospheric and ionospheric response to the CMEs. This coupling is validated with two observed geoeffective CME events driven with the spheromak flux-rope CME model. We compare these simulation results with the indices obtained from OpenGGCM simulations driven by the measured solar wind data from spacecraft. We further employ the dynamic time warping (DTW) technique to assess the model performance in predicting Dst. The main highlight of this study is to use EUHFORIA simulated time series to predict the Dst and auroral indices 1 to 2 days in advance, as compared to using the observed solar wind data at L1, which only provides predictions 1 to 2 hours before the actual impact.

The use of supervised methods in space science have demonstrated powerful capability in classification tasks, but unsupervised methods have been less utilized for the clustering of spacecraft observations. We use a combination of unsupervised methods, being principal component analysis, self-organizing maps, and hierarchical agglomerative clustering, to make predictions on if THEMIS and MMS observations occurred in the magnetosphere, magnetosheath, or the solar wind. The resulting predictions are validated visually by analyzing the distribution of predictions and studying individual time series. Particular nodes in the self organizing map are studied to see what data they represent. The capability of deeper hierarchical analysis using this model is briefly explored. Finally, the changes in region prediction can be used to infer magnetopause and bow shock crossings, which can act as an additional method of validation, and are saved for their utility in solar wind validation, understanding magnetopause processes, and the potential to develop a bow shock model.

AbstractWe introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC‐derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML‐AIM inputs are 60‐min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML‐AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML‐AIM produces physically accurate ionospheric potential patterns such as the two‐cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML‐AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data‐assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML‐AIM than others. ML‐AIM is unique and innovative because it predicts ionospheric responses to the time‐varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi‐static ionospheric condition under quasi‐steady solar wind/IMF conditions. Plans are underway to improve ML‐AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.

The ion foreshock is highly dynamic, disturbing the bow shock and the magnetosphere-ionosphere system. To forecast foreshock-driven space weather effects, it is necessary to model foreshock ions as a function of upstream shock parameters. Case studies in the accompanying paper show that magnetosheath ions sometimes exhibit strong field-aligned asymmetry toward the upstream direction, which may be responsible for enhancing magnetosheath leakage and therefore foreshock ion density. To understand the conditions leading to such asymmetry and the potential for enhanced leakage, we perform case studies and a statistical study of magnetosheath and foreshock region data surrounding similar to 500 Time History of Events and Macroscale Interactions during Substorms mission bow shock crossings. We quantify the asymmetry using the heat flux along the field-aligned direction. We show that the strong field-aligned heat flux persists across the entire magnetosheath from the magnetopause to the bow shock. Ion distribution functions reveal that the strong heat flux is caused by a secondary thermal population. We find that stronger asymmetry events exhibit heat flux preferentially toward the upstream direction near the bow shock and occur under larger IMF strength and larger solar wind dynamic pressure and/or energy flux. Additionally, we show that near the bow shock, magnetosheath leakage is a significant contributor to foreshock ions, and through enhancing the leakage the magnetosheath ion asymmetry can modulate the foreshock ion velocity and density. Our results imply that likely due to field line draping and compression against the magnetopause that leads to a directional mirror force, modeling the foreshock ions necessitates a more global accounting of downstream conditions. Ion field-aligned asymmetry persists across the magnetosheath, caused by a secondary thermal population of magnetosheath ions Larger IMF strength and solar wind dynamic pressure and/or energy flux favor stronger field-aligned asymmetry Near the bow shock, the foreshock ion velocity and density are modulated by the magnetosheath ion field-aligned asymmetry

Kelvin-Helmholtz Instability is ubiquitous at Earth's magnetopause and plays an important role in plasma entry into the magnetosphere during northward interplanetary magnetic fields. Here, using one solar cycle of data from NASA THEMIS (Time History of Events and Macro scale Interactions during Substorms) and MMS (Magnetospheric Multiscale) missions, we found that KHI occurrence rates show seasonal and diurnal variations with the rate being high near the equinoxes and low near the solstices. The instability depends directly on the Earth's dipole tilt angle. The tilt toward or away from the Sun explains most of the seasonal and diurnal variations, while the tilt in the plane perpendicular to the Earth-Sun line explains the difference between the equinoxes. The results reveal the critical role of dipole tilt in modulating KHI across the magnetopause as a function of time, highlighting the importance of Sun-Earth geometry for solar wind-magnetosphere interaction and for space weather.

It is well known that the polar cap, delineated by the open–closed field line boundary (OCB), responds to changes in the interplanetary magnetic field (IMF). In general, the boundary moves equatorward when the IMF turns southward and contracts poleward when the IMF turns northward. However, observations of the OCB are spotty and limited in local time, making more detailed studies of its IMF dependence difficult. Here, we simulate five solar storm periods with the coupled model consisting of the Open Geospace General Circulation Model (OpenGGCM) coupled with the Coupled Thermosphere Ionosphere Model (CTIM) and the Rice Convection Model (RCM), i.e., the OpenGGCM-CTIM-RCM, to estimate the location and dynamics of the OCB. For these events, polar cap boundary location observations are also obtained from Defense Meteorological Satellite Program (DMSP) precipitation spectrograms and compared with the model output. There is a large scatter in the DMSP observations and in the model output. Although the model does not predict the OCB with high fidelity for every observation, it does reproduce the general trend as a function of IMF clock angle. On average, the model overestimates the latitude of the open–closed field line boundary by 1.61∘. Additional analysis of the simulated polar cap boundary dynamics across all local times shows that the MLT of the largest polar cap expansion closely correlates with the IMF clock angle, that the strongest correlation occurs when the IMF is southward, that during strong southward IMF the polar cap shifts sunward, and that the polar cap rapidly contracts at all local times when the IMF turns northward.

<p>We survey one solar cycle of in situ data from the NASA THEMIS (Time History of Events and Macro scale Interactions during Substorms) and MMS (Magnetospheric Multiscale) missions to identify Kelvin–Helmholtz Instability (KHI) along Earth’s magnetopause flank. We found that KHI occurrence rates exhibit semiannual and diurnal variations; the rate maximizes at the equinoxes and minimizes at the solstices. The rate varies for different IMF By polarities; it is maximum around fall equinox for negative IMF By, while is maximum around spring equinox for positive IMF By. <span>The</span> rate is directly related to the dipole tilt angle. Therefore, the equinoctial hypothesis explains most part of the seasonal and diurnal variation of KHI, while the angle of the Earth's dipole in the plane perpendicular to the Earth‐Sun line explains the difference between KHI occurrence rates with positive/negative IMF By. These results reveal the key role of Sun-Earth geometry on modulating the KHI and thus the importance of Earth’s dipole tilt and Sun solar declination angle as a function of time for plasma transport across the magnetopause.</p>

It is well known that the polar cap, delineated by the Open Closed field line Bound ary (OCB), responds to changes in the Interplanetary Magnetic Field (IMF). In general, the boundary moves equatorward when the IMF turns southward and contracts poleward when the IMF turns northward. However, observations of the OCB are spotty and limited in local time, making more detailed studies of its IMF dependence difficult. Here, we simulate five solar storm periods with the coupled OpenGGCM-RCM-CTIM model to estimate the location and dynamics of the OCB. For these events, polar cap boundary location observations are also obtained from Defense-Meteorological Satellite Pro- gram (DMSP) precipitation spectrograms and compared with the model output. There is a large scatter in the DMSP observations and in the model output. However, we generally find good agreement between the model and the observations. On average, the model overestimates the latitude of the open-closed field line boundary by 1.61◦. Additional analysis of the simulated polar cap boundary dynamics across all local times shows that the MLT of the largest polar cap expansion closely correlates with the IMF clock angle; that the strongest correlation occurs when the IMF is southward; that during strong southward IMF the polar cap shifts sunward; and that the polar cap rapidly contracts at all local times when the IMF turns northward.

In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest. Our method is based on Self Organizing Maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with Principal Component Analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with K-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

The EUropean Heliospheric FORecasting Information Asset (EUHFORIA, Pomoell and Poedts, 2018) is a physics-based heliospheric and CME propagation model designed for space weather forecasting. Although EUHFORIA can predict the solar wind plasma and magnetic field parameters at Earth, it is not designed to evaluate indices like Disturbance-storm-time (Dst) or Auroral Electrojet (AE) that quantify the impact of the magnetized plasma encounters on Earth’s magnetosphere. To overcome this limitation, we coupled EUHFORIA with Open Geospace General Circulation Model (OpenGGCM, Raeder et al, 1996) which is a magnetohydrodynamic model of Earth’s magnetosphere. In this coupling, OpenGGCM takes the solar wind and interplanetary magnetic field obtained from EUHFORIA simulation as input to produce the magnetospheric and ionospheric parameters of Earth. We perform test runs to validate the coupling with real CME events modelled using flux rope models like Spheromak and FRi3D. We compare these simulation results with the indices obtained from OpenGGCM simulations driven by the measured solar wind data from spacecrafts like WIND. We further discuss how the choice of CME model and observationally constrained parameters influences the input parameters, and hence the geomagnetic disturbance indices estimated by OpenGGCM. We highlight limitations of the coupling and suggest improvements for future work.

In-situ measurements in the magnetotail are sparse and limited to single points. On the other hand, there is a broad range of observations, including magnetometers, aurora imagers, and radars, in the ionosphere. Since the nightside ionosphere resembles the magnetotail plasma sheet dynamics projection, it can be used to monitor the tail's dynamics. A proper interpretation of the ionosphere and ground observations necessitates understanding the coupling processes between the ionosphere and the magnetosphere. Here, we use the global magnetohydrodynamic simulation model, OpenGGCM-CTIM-RCM, to investigate these coupling processes during periods when the magnetic flux is transported through the tail via narrow fast flow channels, typically called bursty bulk flows (BBFs). We consider three relevant states of the magnetotail: immediately before the substorm's onset, during the expansion of the substorm, and during a steady magnetic convection (SMC) event. The number of flow channels increases, and they penetrate closer to the Earth during SMC. The IMF By direction influences fast flows' location but not their orientation in the plasma sheet during this event. However, the dimension of fast flows and streamers do not depend on the IMF conditions and state of the magnetosphere.

Over the past few decades global simulations have emerged as a valuable new tool to study the magnetosphere-ionosphere-thermosphere system, to the point where some of them are considered community models available for runs on demand by the community at the Community Coordinated Modeling Center (CCMC). Most leading global models are still based on the conventional and well-established MHD approximation; they are remarkably successful reproducing the major features and processes in geospace. Here, we briefly review the purpose and utility of such models, their history, their basic structure, the need for submodels of the ionosphere-thermosphere system and the inner magnetosphere, as well as inherent model limitations. The major computational and numerical constraints are discussed and recent simulations addressing plasma injections and sawtooth oscillations are presented.

Solar activity, ranging from the background solar wind to energetic coronal mass ejections (CMEs), is the main driver of the conditions in the interplanetary space and in the terrestrial space environment, known as space weather. A better understanding of the Sun-Earth connection carries enormous potential to mitigate negative space weather effects with economic and social benefits. Effective space weather forecasting relies on data and models. In this paper, we discuss some of the most used space weather models, and propose suitable locations for data gathering with space weather purposes. We report on the application of Representer analysis (RA) and Domain of Influence (DOI) analysis to three models simulating different stages of the Sun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solar wind-magnetosphere interaction, and the PLUTO model, used to simulate CME propagation in interplanetary space. Our analysis is promising for space weather purposes for several reasons. First, we obtain quantitative information about the most useful locations of observation points, such as solar wind monitors. For example, we find that the absolute values of the DOI are extremely low in the magnetospheric plasma sheet. Since knowledge of that particular sub-system is crucial for space weather, enhanced monitoring of the region would be most beneficial. Second, we are able to better characterize the models. Although the current analysis focuses on spatial rather than temporal correlations, we find that time-independent models are less useful for Data Assimilation activities than time-dependent models. Third, we take the first steps toward the ambitious goal of identifying the most relevant heliospheric parameters for modeling CME propagation in the heliosphere, their arrival time, and their geoeffectiveness at Earth.

Extreme space weather events are extremely rare, but pose a significant threat to our infrastructure. The one known event of such kind was the Carrington storm of 1859, but it was not well documented; in particular the solar wind and IMF conditions that caused it remain guesses. On the other hand, the STEREO-A observations of July 23, 2012 showed solar wind and IMF parameters that are most likely comparable to those of the Carrington event, and remind us that such extreme events are very well possible even during times of a quiet sun. Here, we use OpenGGCM simulations of such events to assess the effects of such solar wind and IMF on the magnetosphere. Precious work has shown that during the much more benign Halloween storm the nose of the magnetopause was as close as 4.9 RE, with an accordingly large polar cap. We will present simulations of a sequence of scaled-up storms with increasingly larger driving and demonstrate the further expansion of the polar cap, intensity of plasma injections, and the eventual saturation. In addition, we will show how the ionosphere potential penetrates to lower latitudes and affects the ionosphere and thermosphere at mid latitudes when the solar wind drivers become extreme.

Aims: This paper presents a H2020 project aimed at developing an advanced space weather forecasting tool, combining the MagnetoHydroDynamic (MHD) solar wind and coronal mass ejection (CME) evolution modelling with solar energetic particle (SEP) transport and acceleration model(s). The EUHFORIA 2.0 project will address the geoeffectiveness of impacts and mitigation to avoid (part of the) damage, including that of extreme events, related to solar eruptions, solar wind streams, and SEPs, with particular emphasis on its application to forecast geomagnetically induced currents (GICs) and radiation on geospace.Methods: We will apply innovative methods and state-of-the-art numerical techniques to extend the recent heliospheric solar wind and CME propagation model EUHFORIA with two integrated key facilities that are crucial for improving its predictive power and reliability, namely (1) data-driven flux-rope CME models, and (2) physics-based, self-consistent SEP models for the acceleration and transport of particles along and across the magnetic field lines. This involves the novel coupling of advanced space weather models. In addition, after validating the upgraded EUHFORIA/SEP model, it will be coupled to existing models for GICs and atmospheric radiation transport models. This will result in a reliable prediction tool for radiation hazards from SEP events, affecting astronauts, passengers and crew in high-flying aircraft, and the impact of space weather events on power grid infrastructure, telecommunication, and navigation satellites. Finally, this innovative tool will be integrated into both the Virtual Space Weather Modeling Centre (VSWMC, ESA) and the space weather forecasting procedures at the ESA SSCC in Ukkel (Belgium), so that it will be available to the space weather community and effectively used for improved predictions and forecasts of the evolution of CME magnetic structures and their impact on Earth.Results: The results of the first six months of the EU H2020 project are presented here. These concern alternative coronal models, the application of adaptive mesh refinement techniques in the heliospheric part of EUHFORIA, alternative flux-rope CME models, evaluation of data-assimilation based on Karman filtering for the solar wind modelling, and a feasibility study of the integration of SEP models.