Solar-wind forecasting is critical for predicting events which can affect Earth's technological systems.  Typically, forecasts combine coronal model outputs with heliospheric models to predict near-Earth conditions. Ensemble forecasting generates sets of outputs to create probabilistic forecasts which quantify forecast uncertainty, vital for reliable/actionable forecasts. We adapt meteorological methods to create a calibrated solar-wind ensemble and probabilistic forecast for ambient solar wind, a prerequisite for accurate coronal mass ejection (CME) forecasting. Calibration is achieved by adjusting ensemble inputs/outputs to align the ensemble spread with observed event frequencies. We produce hindcasts in near-Earth space using coronal-model output over Solar Cycle 24, as input to Heliospheric Upwind eXtrapolation with time dependence (HUXt) solar-wind model. Making spatial perturbations to the coronal model output at 0.1~AU, we produce ensembles of inner-boundary conditions for HUXt, evaluating how forecast accuracy was impacted by the scales of perturbations applied. We found optimal spatial perturbations described by Gaussian distributions with variances of 20 degrees latitude and 10 degrees longitude; these might represent spatial uncertainty within the coronal model. This produced probabilistic forecasts better matching observed frequencies.  Calibration improved forecast reliability, reducing the Brier score by 9% and forecast decisiveness increasing AUC ROC score by 2.5%. Improvements were subtle but systematic. Additionally, we explored statistical post-processing to correct over-confidence bias, improving forecast actionability. However, this method, applied post-run, does not affect the solar-wind state used to propagate CMEs. This work represents the first formal calibration of solar-wind ensembles, laying groundwork for comprehensive forecasting systems like a calibrated multi-model ensemble.

A document by Michael Lockwood. Click on the document to view its contents.

Accurate forecasting of the arrival time and arrival speed of coronal mass ejections (CMEs) is a unsolved problem in space weather research. In this study, a comparison of the predicted arrival times and speeds for each CME based, independently, on the inputs from the two STEREO vantage points is carried out. We perform hindcasts using ELlipse Evolution model based on Heliospheric Imager observations (ELEvoHI) ensemble modelling. An estimate of the ambient solar wind conditions is obtained by the Wang-Sheeley-Arge/Heliospheric Upwind eXtrapolation (WSA/HUX) model combination that serves as input to ELEvoHI. We carefully select 12 CMEs between February 2010 and July 2012 that show clear signatures in both STEREO-A and STEREO-B HI time-elongation maps, that propagate close to the ecliptic plane, and that have corresponding in situ signatures at Earth. We find a mean arrival time difference of 6.5 hrs between predictions from the two different viewpoints, which can reach up to 9.5 hrs for individual CMEs, while the mean arrival speed difference is 63 km s$^{-1}$. An ambient solar wind with a large speed variance leads to larger differences in the STEREO-A and STEREO-B CME arrival time predictions ($cc = 0.92$). Additionally, we compare the predicted arrivals, from both spacecraft, to the actual in situ arrivals at Earth and find a mean absolute error of 7.5 $\pm$ 9.5 hrs for the arrival time and 87 $\pm$ 111 km s$^{-1}$ for the arrival speed. There is no tendency for one spacecraft to provide more accurate arrival predictions than the other.

The Heliospheric Imagers on board NASAs twin STEREO spacecraft show that coronal mass ejections (CMEs) can be visually complex structures. To explore this complexity, we created a web-based citizen science project in collaboration with the UK Science Museum, in which participants were shown pairs of differenced CME images, taken by the inner cameras of the Heliospheric Imagers (HI-1) on board the twin NASA STEREO spacecraft between 2008 and 2016. Participants were asked to decide which image in each pair appeared the most complicated. 4,028 volunteers conducted 246,692 comparisons of 20,190 image pairs, with each pair being classified by 12 independent users. A Bradley-Terry model was then applied to these data to rank the CMEs by their visual complexity. This complexity ranking revealed that the annual average visual complexity values follow the solar activity cycle, with a higher level of complexity being observed at the peak of the cycle, and the average complexity of CMEs observed by HI1-A was significantly higher than the complexity of CMEs observed by HI1-B. Visual complexity was found to be associated with CME size and brightness, but the differences between HI1-A and HI1-B images suggest that complexity may be influenced by the scale-sizes of structure in the CMEs. Whilst it might not be surprising that the complexity observed in these CME images follows the trend observed in sunspots and the solar cycle; these results demonstrate that there is a quantifiable change in the structure of CMEs seen in the inner heliosphere.

The evolution and propagation of coronal mass ejections (CMEs) in interplanetary space is still not well understood. As a consequence, accurate arrival time and arrival speed forecasts are an unsolved problem in space weather research. In this study, we present the ELlipse Evolution model based on HI observations (ELEvoHI) and introduce a deformable front to this model. ELEvoHI relies on heliospheric imagers (HI) observations to obtain the kinematics of a CME. With the newly developed deformable front, the model is able to react to the ambient solar wind conditions during the entire propagation and along the whole front of the CME. To get an estimate of the ambient solar wind conditions, we make use of three different models: Heliospheric Upwind eXtrapolation model (HUX), Heliospheric Upwind eXtrapolation with time dependence model (HUXt), and EUropean Heliospheric FORecasting Information Asset (EUHFORIA). We test the deformable front on a CME first observed in STEREO-A/HI on February 3, 2010 14:49 UT. For this case study, the deformable front provides better estimates of the arrival time and arrival speed than the original version of ELEvoHI using an elliptical front. The new implementation enables us to study the parameters influencing the propagation of the CME not only for the apex, but for the entire front. The evolution of the CME front, especially at the flanks, is highly dependent on the ambient solar wind model used. An additional advantage of the new implementation is given by the possibility to provide estimates of the CME mass.

Accurately forecasting the arrival of coronal mass ejections (CMEs) at Earth is important to enabling mitigation of the associated space weather risks to society. This is only possible with accurate modelling of the event. To do so, we must understand the propagation of a CME through the heliosphere and quantify the performance of models through comparison with spacecraft observations. For the December 12th 2008 Earth-directed CME event, we compute ensembles using the HUXt solar wind model to analyse CME distortion with a structured solar wind and explore hindcast arrival time error (ATE). By highlighting the impact CME shape has on Root-Mean-Square-Error (RMSE) values, we show that time-elongation profiles of fronts captured by the Heliospheric Imager (HI) instruments onboard NASA’s STEREO mission match those of the modelled CME nose and flank and can therefore be used to infer details of the longitudinal extent of the CME. We then show that accounting for CME distortion is important to enable accurate estimates of the CME arrival at Earth. This can be achieved by either using observations of multiple features in HI data to infer CME evolution or mapping the solar wind back to a lower inner boundary to allow CMEs to be distorted close to the Sun. For the event studied we show that these approaches resulted in reduced RMSEs of 0.726˚and 0.638˚ with an ATE of one hour and three hours respectively.

Geometric modelling of Coronal Mass Ejections (CMEs) is a widely used tool for assessing their kinematic evolution. Furthermore, techniques based on geometric modelling, such as ELEvoHI, are being developed into forecast tools for space weather prediction. These models assume that solar wind structure does not affect the evolution of the CME, which is an unquantified source of uncertainty. We use a large number of Cone CME simulations with the HUXt solar wind model to quantify the scale of uncertainty introduced into geometric modelling and the ELEvoHI CME arrival times by solar wind structure. We produce a database of simulations, representing an average, a fast, and an extreme CME scenario, each independently propagating through 100 different ambient solar wind environments. Synthetic heliospheric imager observations of these simulations are then used with a range of geometric models to estimate the CME kinematics. The errors of geometric modelling depend on the location of the observer, but do not seem to depend on the CME scenario. In general, geometric models are biased towards predicting CME apex distances that are larger than the true value. For these CME scenarios, geometric modelling errors are minimised for an observer in the L5 region. Furthermore, geometric modelling errors increase with the level of solar wind structure in the path of the CME. The ELEvoHI arrival time errors are minimised for an observer in the L5 region, with mean absolute arrival time errors of 8.2±1.2h, 8.3±1.0h, and 5.8±0.9h for the average, fast, and extreme CME scenarios

We introduce the inductive effects of polar cap motions towards and way from the Sun into magnetospheric electrodynamics and show how this explains observed Universal Time variations in hemispheric geomagnetic indices. The large (and growing) hemispheric asymmetry in the offsets of the geomagnetic (dip or eccentric dipole) poles from Earth’s rotational axis means that the effect is not cancelled out in global indices. By adding this effect to that of the Russell-McPherron effect on solar wind-magnetosphere coupling, that of ionospheric conductivities, and that of the solar wind dynamic pressure and dipole tilt on the near-Earth tail lobe field and cross-tail current sheet, we are able to model the persistent “equinoctial” time-of-day/time-of-year pattern (with additional net Universal time variations) observed in the an, as and am geomagnetic indices since 1959. We discuss the implications for the longitudinal dependence of the effects of extreme space weather events