In this study, we use measurements from over 4,735 globally distributed Global Navigation Satellite System receivers to track the progression of traveling ionospheric disturbances (TIDs) associated with the 15 January 2022 Hunga Tonga‐Hunga Ha'apai submarine volcanic eruption. We identify two distinct Large Scale traveling ionospheric disturbances (LSTIDs) and several subsequent Medium Scale traveling ionospheric disturbances (MSTIDs) that propagate radially outward from the eruption site. Within 3,000 km of epicenter, LSTIDs of >1,600 km wavelengths are initially observed propagating at speeds of ∼950 and ∼555 ms−1, before substantial slowing to ∼600 and ∼390 ms−1, respectively. MSTIDs with speeds of 200–400 ms−1 are observed for 6 hrs following eruption, the first of which comprises the dominant global ionospheric response and coincides with the atmospheric surface pressure disturbance associated with the eruption. These are the first results demonstrating the global impact of the Tonga eruption on the ionospheric state.

By their very nature, extreme space weather events occur rarely, and therefore, statistical methods are required to determine the probability of their occurrence. Space weather events can be characterized by a number of natural phenomena such as X‐ray (solar) flares, solar energetic particle fluxes, coronal mass ejections, and various geophysical indices (such as Dst, Kp, and F10.7). In this paper extreme value theory (EVT) is used to investigate the probability of extreme solar flares. Previous work has assumed that the distribution of solar flares follows a power law. However, such an approach can lead to a poor estimation of the return times of flares due to uncertainties in the tails of the probability distribution function. Using EVT and Geostationary Operational Environmental Satellites X‐ray flux data, it is shown that the expected 150 year return level is approximately an X60 flare while a Carrington‐like flare is a one in a 100 year event. In the worst case the 150 year return level is an X90 flare while a Carrington flare is a one in 30 year event. It is also shown that the EVT results are consistent with flare data from the Kepler space telescope mission.

Abstract. This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. The MME, with its reduced uncertainties, can then be used as the initial conditions in a physics-based thermosphere model for forecasting. This should increase the forecast skill since a reduction in the errors of the initial conditions of a model generally increases model skill. In this paper the Thermosphere-Ionosphere Electrodynamic General Circulation Model (TIE-GCM), the US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00), and Global IonosphereThermosphere Model (GITM) have been used to construct the MME. As well as comparisons between the MMEs and the "standard" runs of the model, the MME densities have been propagated forward in time using the TIE-GCM. It is shown that thermospheric forecasts of up to 6 h, using the MME, have a reduction in the root mean square error of greater than 60 %. The paper also highlights differences in model performance between times of solar minimum and maximum.

Abstract. The standard approach to remove the effects of the ionosphere from neutral atmosphere GPS radio occultation measurements is to estimate a corrected bending angle from a combination of the L1 and L2 bending angles. This approach is known to result in systematic errors and an extension has been proposed to the standard ionospheric correction that is dependent on the squared L1 ∕ L2 bending angle difference and a scaling term (κ). The variation of κ with height, time, season, location and solar activity (i.e. the F10.7 flux) has been investigated by applying a 1-D bending angle operator to electron density profiles provided by a monthly median ionospheric climatology model. As expected, the residual bending angle is well correlated (negatively) with the vertical total electron content (TEC). κ is more strongly dependent on the solar zenith angle, indicating that the TEC-dependent component of the residual error is effectively modelled by the squared L1 ∕ L2 bending angle difference term in the correction. The residual error from the ionospheric correction is likely to be a major contributor to the overall error budget of neutral atmosphere retrievals between 40 and 80 km. Over this height range κ is approximately linear with height. A simple κ model has also been developed. It is independent of ionospheric measurements, but incorporates geophysical dependencies (i.e. solar zenith angle, solar flux, altitude). The global mean error (i.e. bias) and the standard deviation of the residual errors are reduced from -1.3×10-8 and 2.2×10-8 for the uncorrected case to -2.2×10-10 rad and 2.0×10-9 rad, respectively, for the corrections using the κ model. Although a fixed scalar κ also reduces bias for the global average, the selected value of κ (14 rad−1) is only appropriate for a small band of locations around the solar terminator. In the daytime, the scalar κ is consistently too high and this results in an overcorrection of the bending angles and a positive bending angle bias. Similarly, in the nighttime, the scalar κ is too low. However, in this case, the bending angles are already small and the impact of the choice of κ is less pronounced.

Severe space weather was identified as a risk to the UK in 2010 as part of a wider review of natural hazards triggered by the societal disruption caused by the eruption of the Eyjafjallajökull volcano in April of that year. To support further risk assessment by government officials, and at their request, we developed a set of reasonable worst‐case scenarios and first published them as a technical report in 2012 (current version published in 2020). Each scenario focused on a space weather environment that could disrupt a particular national infrastructure such as electric power or satellites, thus, enabling officials to explore the resilience of that infrastructure against severe space weather through discussions with relevant experts from other parts of government and with the operators of that infrastructure. This approach also encouraged us to focus on the environmental features that are key to generating adverse impacts. In this paper, we outline the scientific evidence that we have used to develop these scenarios, and the refinements made to them as new evidence emerged. We show how these scenarios are also considered as an ensemble so that government officials can prepare for a severe space weather event, during which many or all of the different scenarios will materialize. Finally, we note that this ensemble also needs to include insights into how public behavior will play out during a severe space weather event and hence the importance of providing robust, evidence‐based information on space weather and its adverse impacts.