Abstract To study the evolution and propagation of Coronal Mass Ejections (CMEs), the advanced magnetized flux rope CME model, Flux Rope in 3D (FRi3D), has been integrated into the European Heliospheric Forecasting Information Asset (EUHFORIA). Recent updates have been addressing the computational efficiency and numerical stability issues of this CME model. In this work, the latest improvements to the FRi3D model are represented, including optimizations of the integral calculations in the ai.fri3d Python package, an updated CME leg disconnection method, and the introduction of a new datacube injection methodology for time‐efficient FRi3D simulations. Within the datacube injection method, the input values of FRi3D at the inner boundary of EUHFORIA are precomputed, eliminating the need to calculate the FRi3D CME model during the solar wind simulation with superimposed CMEs. The datacube injection method is tested by modeling the CME event of 12 July 2012, and by conducting ensemble runs for this event. The updates to the FRi3D Python package reduced the computation time to , and the datacube methodology achieved a computational time reduction of up to compared to previous FRi3D versions. Numerical stability was significantly improved, and ensemble simulations demonstrated robust ensemble spreads for the thermodynamic and magnetic time profiles on Earth. These updates enhance the efficiency, stability, and accuracy of the FRi3D model, making it highly suitable for predicting both the CME arrival time, magnetic field strength, and velocity. As a result, the advancements contribute significantly to the future operational space weather forecasting capabilities of the FRi3D CME model.

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Abstract A compilation is presented of impacts (interference and damage) realized on long‐line telegraph and telephone systems across North America during the 13–16 May 1921 magnetic storm. Impacts occurred primarily during local nighttime, after the third of four sudden commencements, and during the storm's most‐prominent main phase. Impacts are attributed to rapid and high‐amplitude geomagnetic field variation generated by substorms. This induced potential differences and between the grounding points of communication networks that were sufficient to cause system interference and damage. In the United States, impacts were concentrated in the Midwest and in the East, regions characterized by high electromagnetic surface impedance. Given technological changes, modern telecommunication systems are less exposed to storms like that of May 1921, while power‐grid systems are now more exposed to them.

Abstract An assessment of the risk of extreme geomagnetic storms is critically important for modern society. However, current methods mainly focus on using stationary statistical models to analyze extreme geomagnetic events. These models ignore the non‐stationary nature of the data, caused by effects of the solar cycle and the seasons, and thus could provide unreliable estimates of return levels. We propose use of hidden Markov models and generalized additive models, both involving time‐varying parameters, in order to capture these features of the data. We use these models to analyze extreme values of the magnitude of the derivative in the horizontal component of geomagnetic observations from the Eyrewell geomagnetic observatory in New Zealand. We use residual diagnostics to check for lack‐of‐fit of the models, demonstrate that they can successfully model the effects of both the solar cycle and the seasons, and use the best‐fitting models to provide more reliable estimates of return levels. From our analysis, the 50‐year and 100‐year conditional return levels of the extreme magnitude of the derivative in the horizontal component at the Eyrewell magnetic observatory are likely to be within the ranges 500–2,600 and 700–4,500 nT/min respectively at solar maximum.

Abstract We expand the assessment study of modeling capabilities in the prediction of foF2 and hmF2 for the ionospheric climatology (Tsagouri et al., 2018, https://doi.org/10.1029/2018sw002035) by using updated empirical (IRI and MIT Empirical model) and physics‐based models (CTIPe, WACCM‐X, and TIE‐GCM) as well as the additional observations in the southern hemisphere. Monthly medians of foF2 and hmF2 are considered to evaluate the model performance for the entire year of 2012. For quantitative evaluation, we employ several metrics including the correlation coefficient (R), coefficient of determination (R2), root‐mean square error (RMSE), mean error (ME), and mean relative error (MRE). The linear regression analysis shows that the empirical models perform much better than physics‐based models for foF2 but to a lesser degree for hmF2. There are negligible hemispheric differences in the predictions from empirical models. All the physics‐based models show relatively good correlations with the observations for foF2 in the northern hemisphere compared to the southern hemisphere, but the hemispheric differences are small for hmF2. The results of the study indicate that recent versions of empirical models tend to perform better than old versions of the models, but this is not always true for physics‐based models.

Abstract The ionosphere has significant impacts on wireless communication, satellite navigation, and space weather. Based on hundreds of global GNSS observation stations, we calculate the rate of TEC index (ROTI). Through the analysis of ROTI, we establish a global ionospheric disturbed index (IDI) to directly characterize ionospheric disturbances. Daily IDI values from 1998 to 2023 are then calculated. Furthermore, we establish a multiple linear regression model to fit the IDI, using data from Ap, F10.7, the upper atmospheric wind field, Kp, SYM‐H, and Dst indices. We analyze the contribution of each index to the IDI and classify the disturbance levels of the IDI time series, calculated by the multiple linear regression model for the period from 1981 to 2023, into five levels, ranging from severe disturbance to quiet. Next, the Lomb‐Scargle periodogram method is applied to perform spectral analysis on the IDI time series, extracting the annual, monthly, and daily periodic components of the IDI. Respectively, the annual periods are 1, 5.7, 6.6, 8.2, and 11.5 years; the monthly period is 6 months; and the daily periods are 9 and 26.5 days. Regarding the spatiotemporal features of IDI, our analysis reveals that temporally, the variations in IDI align with those of ROTI. Spatially, IDI exhibits stronger representation effectiveness in high‐latitude regions compared to mid‐ and low‐latitude regions. Finally, an investigation is conducted into the differences in the accuracy of standard point positioning under various conditions, including high and low solar activity years, different severities of geomagnetic storms, and across a range of IDI levels.

Abstract Ground level enhancements (GLEs), which occur when high energy solar protons reach Earth, are a considerable space weather hazard for aviation activities. Neutron monitor (NM) observations of these events are the key input to operational models of ionizing radiation at aviation altitudes. Similarly, the NM data is key to techniques for deriving anisotropic solar proton spectra during GLEs. A higher density of observations is desirable for both purposes. In this paper, a simple way of improving the density of observations for large events is presented: the compact neutron monitor (CNM). This monitor uses the same unleaded detectors as soil moisture sensing networks. Three years of data from the CNM located in Guildford, UK, is presented. The solar cycle variation in cosmic rays is observed, alongside 4 Forbush decreases of varying magnitude. No GLEs were observed during this time, due to a lack of any events of sufficient magnitude to be observed. A future CNM station near Lerwick, UK is briefly described in addition to the Guildford station. The implications of the observations to date are discussed in the context of GLE detection. The CNM is complementary to existing and emerging NM designs, and may be suitable for use as a reference point for the soil moisture monitoring networks. The suitability of the CNM to GLE detection can be extrapolated to the soil moisture networks in the case of large GLEs; in the event of one occurring, the data may provide unprecedented spatial resolution.

Abstract In this paper, we developed an efficient regional three‐dimensional data assimilation model focused on the middle‐to‐high latitude ionosphere in the Northern Hemisphere. The model employed the Empirical Canadian High Arctic Ionospheric Model (E‐CHAIM) as the background, which was specifically optimized for high‐latitude applications and has demonstrated exceptional performance in the northern high‐latitude regions. Utilizing a three‐dimensional variational (3DVAR) method and incorporating extensive slant total electron content (TEC) observations, the model achieves near‐real‐time computation of the three‐dimensional electron density distribution. The spatial‐temporal resolution of the reanalyzed three‐dimensional electron density product is 2.5° in latitude, 5° in longitude, with altitude intervals of 20 km between 80 and 500 km, 100 km between 500 and 1,000 km, 500 km between 1,000 and 3,000 km, and a temporal resolution of 15 min. To assess the effectiveness and accuracy of the model, we performed extensive comparisons with observational data from various sources, including the GNSS vertical TEC, ionosonde foF2, electron density profile derived from ionosonde measurements and COSMIC radio occultation data. Independent verification confirmed that the data assimilation results align well with these observations. Leveraging on the data assimilation output, we reconstructed critical high‐latitude ionospheric structures and phenomena during geomagnetic storms, such as the storm‐enhanced density (SED), the tongue of ionization (TOI), boundary blobs, polar cap patches, and the electron density enhancement in the auroral particle precipitation region, and characterized their three‐dimensional spatial distributions well.

Abstract The geomagnetic disturbance index SYM‐H is primarily determined by the total kinetic energy of ring current particles. Therefore, the energy balance mechanism of the ring current can be used to construct an SYM‐H evolution equation for prediction purposes. This study extends a modeling concept developed by Ji et al. (2023), https://doi.org/10.1029/2022ea002560 to establish an algebraic equation for predicting the SYM‐H index based on equilibrium between energy injection and ring current loss. The loss term in the model is determined by a fully connected neural network. The fundamental form of the energy injection function is derived from existing solar wind–magnetosphere energy coupling functions, with its scale factor adjusted as a free‐fitting parameter to optimize the prediction of observations. After being trained on solar wind and SYM‐H observations from 20 magnetic storms, the new model predicts the SYM‐H index well 1 hr and 2 hr in advance, with root mean square errors of 6.7 and 8.9 nT, respectively. These accuracies represent a 7 (1‐hr model) and a 6 (2‐hr model) improvement over the previous model. Furthermore, the scale factors for the solar wind parameters in the energy coupling function determined by the new model can be explained by the previous observations in the magnetic tail current sheet, confirming that the ring current energy primarily originates from the current sheet. The lifetime of the ring current particles, as determined by the neural network, varies with the SYM‐H index. It is approximately 6 hr for the fast recovery phase and more than 10 hr for the slow recovery phase, consistent with the dominant ring current particles changing from oxygen ions to protons during intense storms.