Abstract Shortwave fadeout (SWF) is a typical dayside high‐frequency radio disturbance caused by solar flare‐induced ionospheric ionization enhancement. Using newly built Chinese Dual Auroral Radar Network (CN‐DARN) observations, we investigate two X‐flare‐driven SWFs during the May 2024 geomagnetic superstorm (SYM‐H = −518 nT), on 10 May (pre‐storm) and 11 May (storm main phase). Comparative analysis reveals strong ionospheric negative storm modulations on SWF characteristics: a prominent Doppler flash (line‐of‐sight velocity peak 137 m/s, exceeding prior records) preceded the pre‐storm SWF, but none was detected for the event during the main phase of the storm. The latter showed layered radar backscatters (E‐vs. F‐region reflections) with divergent interruption durations (∼20 vs. ∼80 min); F‐region backscatters had abnormally prolonged recovery (>3 hr to quiet‐time levels), while E‐region signals and the pre‐storm SWF recovered in ∼1 hr. We also find radar transmission frequency and local time impact SWF observations. These results highlight ionospheric negative storm effects on flare‐induced SWFs, clarify their characteristics in complex ionospheric conditions, and support their accurate forecasting.
Abstract Accurate forecasting of global ionospheric total electron content (TEC) is important for Global Navigation Satellite Systems (GNSS) performance and satellite communications. This study proposes a driver framework based on the spectral whitening method (SWM). We combine a new high‐latitude geomagnetic disturbance index with previously developed SWM‐based solar extreme ultraviolet and geomagnetic‐response indices and use them to drive a deep learning model for global TEC forecasting. Unlike existing driving indices (e.g., AE, SYM‐H, F10.7) derived with different procedures, the SWM‐based drivers are generated with a consistent signal‐extraction method designed to isolate non‐stationary disturbances. Throughout the year, the Pearson correlation coefficients between the SWM index group and the existing driving indices remain greater than 0.7. Seasonally, the SWM‐driven model yields Root Mean Square Error (RMSE) of 2.13–3.49 TECU, compared to 3.68–4.46 TECU for the IRI‐2020 model. For spring and winter specifically, the SWM‐driven RMSE are 2.63 and 2.76 TECU, compared to 2.64 and 2.79 TECU for the traditional‐index‐driven model. During recursive forecasts extending to 144 hr for the 2021 geomagnetic storm, the SWM‐driven model records RMSE of 4.57 TECU at 96 hr and 4.74 TECU at 144 hr, whereas the traditional‐index‐driven model yields 4.65 TECU and 4.94 TECU, respectively. These results suggest that SWM‐derived driver indices can improve forecast stability under disturbed conditions.
Abstract Chorus waves, typically excited by anisotropic electrons with energies from hundreds of eV to tens of keV, play a critical role in the dynamics of Earth's outer radiation belt. Previous chorus wave models are commonly driven by geomagnetic indices and solar wind parameters, which capture large‐scale activity but may not fully reproduce localized and rapid wave variations. This limitation may partly arise from the omission of microphysical drivers, such as electron flux associated with wave excitation and evolution. Here we incorporate 1–100 keV electron flux information into a machine learning framework to improve reconstruction of lower band chorus wave amplitudes. Compared with an index‐only baseline model, the new approach increases the overall Pearson correlation coefficient from 0.38 to 0.53 and reduces the root‐mean‐square error from 20.3 to 18.6 pT. The inclusion of in situ electron flux information also helps capture intense localized chorus activity during geomagnetically quiet periods, which is often underestimated by models driven only by macro‐scale inputs. These results show that local electron flux provides substantial additional predictive information for chorus reconstruction. The framework provides a practical pathway toward generating proxy chorus wave data from particle measurements, thereby enhancing the space weather utility of missions that do not carry dedicated wave instruments.
Abstract This study analyzes the 10–12 May 2024 geomagnetic storm using data from the Ebre (Spain) and Livingston Island (Antarctica) geomagnetic observatories, together with modeling approaches to assess its impact on the Spanish power transmission grid. Geomagnetic field variations reveal stronger disturbances at higher geomagnetic latitudes, with peak rates of change of the horizontal magnetic field reaching 230.1 nT/min at Livingston Island and 74.4 nT/min at Ebre. A statistical analysis based on a 46‐year data set (1980–2025) indicates that the storm corresponds to a return period of approximately 7.7 years at Ebre, ranking ninth in magnitude. In terms of geoelectric field intensity, the event is less extreme, with a return period of 3.3 years, indicating that it was significant but moderate compared with historical extremes. To evaluate geomagnetically induced currents (GICs), recorded geomagnetic field was combined with a 3D lithospheric resistivity model to estimate geoelectric fields and resulting currents across the Spanish power grid. Results show an uneven GIC distribution, with maximum modeled values reaching 28.7 A, largely due to the alignment of transmission lines with the dominant electric field direction. Model performance was assessed using indirect GIC measurements, revealing a systematic overestimation of observed values. After applying the derived scaling factor to the most vulnerable substation, the expected GIC was estimated at 14.5 ± 3.5 A. The findings confirm that the storm generated measurable GICs in Spain, although they did not pose a risk to the Spanish grid, unlike those recorded in grids at higher latitudes.
Abstract On 11 November 2025, at approximately 10:20 UTC, the 77th Ground Level Enhancement (GLE) occurred. This paper presents a comprehensive data set encompassing observations from ground‐level neutron monitors across the United Kingdom (UK), radiation monitors on radiosonde flights from the UK and the Netherlands, and radiation monitors onboard transatlantic flights from London‐Heathrow to New York. The ground level observations reveal a strong anisotropy in the event intensity and time profile across the UK, where a strong peak was observed in the north at the beginning of the event, and a second weaker peak was observed in the south approximately 1 hr later. Data from radiosondes launched from the northern extremity of the UK show that the threshold for an International Civil Aviation Organisation (ICAO) moderate radiation threshold was exceeded at business jet altitudes several hours after the event peak, and at supersonic altitudes the severe threshold was exceeded by a factor of two. Other radiosonde flights from the south of the UK and the Netherlands did not show a significant increase in radiation levels. One Heathrow‐New York (JFK) flight, departing at 11:45 UTC, experienced a 42.6 % increase in total ambient dose equivalent exposure, compared to typical background conditions, but did not observe any ICAO thresholds being exceeded. The second flight, (Heathrow‐Newark) departing at 17:45 UTC did not observe any significant increase in radiation exposure.
