Space Weather

The prediction of post-sunset equatorial plasma depletions (EPDs), often called ionospheric plasma bubbles, has remained a challenge for decades. In this study, we introduce the Ionospheric Bubble Probability (IBP) model, an empirical model to predict the occurrence probability of EPDs derived from 9 years of CHAMP and 9 years of Swarm magnetic field measurements. The model predicts the occurrence probability of EPDs for a given longitude, day of year, local time and solar activity, for the altitude range of about 350–510 km, and low geographic latitudes of ±45°. IBP has been found to successfully reconstruct the distribution of EPDs as reported in previous studies from independent data. IBP has been further evaluated using 1-year of untrained data of the Ionospheric Bubble Index (IBI). IBI is a Level 2 product of the Swarm satellite mission used for EPD identification. The relative operating characteristics (ROC) curve shows positive excursion above the no-skill line with Hanssen and Kuiper's Discriminant (H&KSS) score of 0.52, 0.51, and 0.55 at threshold model output of 0.16 for Swarm A, B, and C satellites. Additionally, the reliability plots show proximity to the diagonal line with a decent Brier Skill Score (BSS) of 0.249, 0.210, and 0.267 for Swarm A, B, and C respectively at 15% climatological occurrence rate. These tests indicate that the model performs significantly better than a no-skill forecast. The IBP model offers compelling glimpses into the future of EPD forecasting, thus demonstrating its potential to reliably predict EPD occurrences. The IBP model is publicly available.

Solar energetic particle (SEP) events, originating from solar flares and Coronal Mass Ejections, present significant hazards to space exploration and technology on Earth. Accurate prediction of these high-energy events is essential for safeguarding astronauts, spacecraft, and electronic systems. In this study, we conduct an in-depth investigation into the application of multimodal data fusion techniques for the prediction of high-energy SEP events, particularly ∼100 MeV events. Our research utilizes six machine learning (ML) models, each finely tuned for time series analysis, including Univariate Time Series (UTS), Image-based model (Image), Univariate Feature Concatenation (UFC), Univariate Deep Concatenation (UDC), Univariate Deep Merge (UDM), and Univariate Score Concatenation (USC). By combining time series proton flux data with solar X-ray images, we exploit complementary insights into the underlying solar phenomena responsible for SEP events. Rigorous evaluation metrics, including accuracy, F1-score, and other established measures, are applied, along with K-fold cross-validation, to ensure the robustness and generalization of our models. Additionally, we explore the influence of observation window sizes on classification accuracy.

Dr. Jennifer Gannon passed away suddenly on 2024 May 2 in Greenbelt, MD. Dr. Gannon had served as an editor for the Space Weather journal since April 2019, and she was the longest-serving editor on the current board, having started under the previous editor-in-chief, Dr. Delores Knipp.

An accurate solar wind (SW) speed model is important for space weather predictions, catastrophic event warnings, and other issues concerning SW—magnetosphere interaction. In this work, we construct a model based on convolutional neural network (CNN) and Potential Field Source Surface (PFSS) magnetic field maps, considering a SW source surface of R SS = 2.5R⊙, aiming to predict the SW speed at the Lagrange-1 (L1) point of the Sun-Earth system. The input of our model consists of four PFSS magnetic field maps at R SS, which are three, four, five, and six days before the target epoch. Reduced maps are used to promote the model's efficiency. We use the Global Oscillation Network Group (GONG) photospheric magnetograms and the potential field extrapolation model to generate PFSS magnetic field maps at the source surface. The model provides predictions of the quasi-continuous test data set, which is generated by randomly assigning 120 data segments that are individually continuous in time, with an averaged correlation coefficient (CC) of 0.53 ± 0.07 and a root mean square error (RMSE) of 80.8 ± 4.8 km/s in an eight-fold validation training scheme with the time resolution of the data as small as one hour. The model also has the potential to forecast high speed streams of the SW, which can be quantified with a general threat score of 0.39.

A coronal mass ejection erupted from the Sun on 21 April 2023 and created a G4 geomagnetic storm on 23 April. NASA's global-scale observations of the limb and disk (GOLD) imager observed bright equatorial ionization anomaly (EIA) crests at ∼25° Mlat, ∼11° poleward from their average locations, computed by averaging the EIA crests during the previous geomagnetic quiet days (18–22 April) between ∼15°W and 5°W Glon. Reversed C-shape equatorial plasma bubbles (EPBs) were observed reaching ∼±36° Mlat (∼40°N and ∼30°S Glat) with apex altitudes ∼4,000 km and large westward tilts of ∼52°. Using GOLD's observations EPBs zonal motions are derived. It is observed that the EPBs zonal velocities are eastward near the equator and westward at mid-latitudes. Model-predicted prompt penetration electric fields indicate that they may have affected the postsunset pre-reversal enhancement at equatorial latitudes. Zonal ion drifts from a defense meteorological satellite program satellite suggest that westward neutral winds and perturbed westward ion drifts over mid-latitudes contributed to the observed latitudinal shear in zonal drifts.

The ring current is an important component of the Earth's near-space environment, as its variations are the direct driver of geomagnetic storms that can disrupt power grids, satellite communications, and navigation systems, thereby impacting a wide range of technological and human activities. Oxygen ions (O+) are one of the major components of the ring current and play a significant role in both the enhancement and depletion of the ring current during geomagnetic storms. Although a standard statistical study can provide average global distributions of ring current ions, it can't offer insight into the short-term dynamic variations of the global distribution. Therefore, we employed the Artificial Neural Network technique to construct a global ring current O+ ion model based on the Van Allen Probes observations. Through optimization of the combination of input geomagnetic indices and their respective time history lengths, the model can well reproduce the spatiotemporal variation of the oxygen ion flux distributions and demonstrates remarkable accuracy and minimal errors. Additionally, the model effectively reconstructs the temporal variation of ring current O+ ions for non-training set data. Furthermore, the model provides a comprehensive and dynamic representation of global ring current O+ ion distribution. It accurately captures the dynamics of O+ ions during a geomagnetic storm with the oxygen ion fluxes enhancement and decay, and reveals distinct characteristics for different energy levels, such as injection from the plasma sheet, outflow from the ionosphere, and magnetic local time asymmetry.

In this work, we address the computational challenge of large-scale physics-based simulation models for the ring current. Reduced computational cost allows for significantly faster than real-time forecasting, enhancing our ability to predict and respond to dynamic changes in the ring current, valuable for space weather monitoring and mitigation efforts. Additionally, it can also be used for a comprehensive investigation of the system. Thus, we aim to create an emulator for the Ring current-Atmosphere interactions Model with Self-Consistent magnetic field (RAM-SCB) particle flux that not only improves efficiency but also facilitates forecasting with reliable estimates of prediction uncertainties. The probabilistic emulator is built upon the methodology developed by Licata and Mehta (2023), https://doi.org/10.1029/2022sw003345. A novel discrete sampling is used to identify 30 simulation periods over 20 years of solar and geomagnetic activity. Focusing on a subset of particle flux, we use Principal Component Analysis for dimensionality reduction and Long Short-Term Memory (LSTM) neural networks to perform dynamic modeling. Hyperparameter space was explored extensively resulting in about 5% median symmetric accuracy across all data sets for one-step dynamic prediction. Using a hierarchical ensemble of LSTMs, we have developed a reduced-order probabilistic emulator (ROPE) tailored for time-series forecasting of particle flux in the ring current. This ROPE offers accurate predictions of omnidirectional flux at a single energy with no pitch angle information, providing robust predictions on the test set with an error score below 11% and calibration scores under 8% with bias under 2% providing a significant speed up as compared to the full RAM-SCB run.

In this study, the error of total electron content (TEC) derived from the global ionospheric map (GIM) (GIM-TEC) during equatorial plasma bubble (EPB) event is investigated for the first time. The frequently-used assessment parameter of ionospheric TEC model, namely difference of Slant TEC (difference of slant total electron content (dSTEC)) is checked and employed based on eight global navigation satellite system (GNSS) stations distributed around the geomagnetic equator during the high solar activity year of 2014. The international GNSS service final GIM products are exemplified. The results present several interesting findings: (a) The observed dSTEC series is biased when an EPB is observed at the highest satellite elevation, leading to a fake bias in GIM-TEC; (b) When an EPB occurred, the error of GIM-TEC can increases or decreases and its variation sign is unrelated to the magnitude of EPB; (c) The average of the EPB-induced GIM-TEC errors is mainly at −5 to 5 TECU with 76% (24%) of positive (negative) values, and the maximum (minimum) is close to 10 TECU (−10 TECU); (d) The structure of EPB is unable to be captured by the GIM-TEC series.

We study the statistical features of magnetosphere-ionosphere (M-I) coupling using a two-way M-I model, the GT configuration of the Multiscale Atmosphere Geospace Environment (MAGE) model. The M-I coupling characteristics, such as field-aligned current, polar cap potential, ionospheric Joule heating, and downward Alfvénic Poynting flux, are binned according to the interplanetary magnetic field clock angles over an entire Carrington Rotation event between 20 March and 16 April 2008. The MAGE model simulates similar distributions of field-aligned currents compared to empirical Weimer/AMPS models and Iridium observations and reproduces the Region 0 current system. The simulated convection potential agrees well with the Weimer empirical model and displays consistent two-cell patterns with SuperDARN observations, which benefit from more extensive data sets. The Joule heating structure in MAGE is generally consistent with both empirical Cosgrove and Weimer models. Moreover, our model reproduces Joule heating enhancements in the cusp region, as presented in the Cosgrove model and observations. The distribution of the simulated Alfvénic Poynting flux is consistent with that observed by the FAST satellite in the dispersive Alfvén wave regime. These M-I coupling characteristics are also binned by the Kp indices, indicating that the Kp dependence of these patterns in the M-I model is more effective than the empirical models within the Carrington Rotation. Furthermore, the MAGE simulation exhibits an improved M-I current-voltage relation that closely resembles the Weimer model, suggesting that the updated global model is significantly improved in terms of M-I coupling.

Solar wind is an intermediary in energy transfer from the Sun into the Earth's magnetosphere, and is considered as a decisive driver of energetic electron dynamics at the geosynchronous orbit (GEO). Based on machine learning technology, several models driven by solar wind parameters have been established to predict GEO electron fluxes. However, the relative contributions of different solar wind parameters on GEO electron fluxes are still unclear. Recently, a feature attribution method, Deep SHapley Additive exPlanations (Deep SHAP) is proposed to open black boxes of machine learning models. In this study, we use the Deep SHAP method to quantify contributions of different solar wind parameters with an artificial neural network (ANN) model. Backpropagating the prediction results of this ANN model from 2011 to 2020, SHAP values for four solar wind parameters (interplanetary magnetic field (IMF) B Z, solar wind speed, solar wind dynamic pressure, and proton density) are calculated and comprehensively analyzed. The results suggest that solar wind speed with a lag of 1 day is the most important driver. We further investigate relative roles of different parameters in three specific electron fluxes variation events (corresponding to electron fluxes reaching a local maximum, a local minimum, and unchanged, respectively). The results suggest that high solar wind speed and southward IMF B Z (high dynamic pressures) facilitate increases (decreases) of electron fluxes. These findings help reveal the underlying physical mechanisms of GEO electron dynamics and help develop more accurate prediction models for GEO electron fluxes.

Ground-level enhancements (GLEs) are sporadic events that signal the arrival of high fluxes of solar energetic particles (SEPs) that have been produced by solar eruptions. Ground-level enhancement events are characterized by a significant increase in the count rate of ground-based neutron monitors (NMs). The arrival of high-energy SEPs in the atmosphere leads to an enhancement of the radiation environment, with the enhancement at aviation altitudes being particularly hazardous to human health as pilots, crew, and airline passengers can be subjected to dangerous levels of radiation during a GLE. Through the use of a currently expanding library of analyzed GLEs and the application of a newly developed atmospheric radiation model, both of which have been created in-house, we found a strong statistically significant relationship between real-time NM data during GLE events and the radiation doses at aviation altitudes. This result provides a strong scientific basis for the use of real-time NM data as a proxy for radiation dose estimates during GLE events and aids in the development of future nowcasting models to help mitigate the dangerous impacts of future GLEs.

A mini-neutron monitor (MNM) was installed at the German Antarctic Neumayer III station, measuring the variation of galactic cosmic rays and searching for Forbush Decreases (FDs) caused by solar activities. Running continuously from 2014 until the end of 2017, the long-term stability of the detector could be investigated. After correcting the air pressure and normalization to the 27 days running mean averages of the SANAE and TERA Neutron Monitors (NMs), the daily running mean count rates are compared with the SANAE and TERA NMs also installed in Antarctica. For most of the 14 FDs with magnitudes greater than 3, taken from the list compiled by the IZMIRAN group (http://spaceweather.izmiran.ru/eng/dbs.html), the three detectors show consistent particle flux variation, although the average rate of the MNM is more than a hundred times smaller. The light and low-cost MNM is an ideal alternative to heavy and old NMs, especially at high altitudes and remote environments.

Severe space weather has the potential to cause significant socio-economic impact and it is widely accepted that mitigating this risk requires more comprehensive observations of the Sun and heliosphere, enabling more accurate forecasting of significant events with longer lead-times. In this context, it is now recognized that observations from the L5 Sun-Earth Lagrange point (both remote and in situ) would offer considerable improvements in our ability to monitor and forecast space weather. Remote sensing from L5 allows for the observation of solar features earlier than at L1, providing early monitoring of active region development, as well as tracking of interplanetary coronal mass ejections through the inner heliosphere. In situ measurements at L5 characterize the solar wind's geoeffectiveness (particularly stream interaction regions), and can also be ingested into heliospheric models, improving their performance. The Vigil space weather mission is part of the ESA Space Safety Program and will provide a real-time data stream for space weather services from L5 following its anticipated launch in the early 2030s. The interplanetary magnetic field is a key observational parameter, and here we describe the development of the Vigil magnetometer instrument for operational space weather monitoring at the L5 point. We summarize the baseline instrument capabilities, demonstrating how heritage from science missions has been leveraged to develop a low-risk, high-heritage instrument concept.

No abstract is available for this article.