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Coronal mass ejections (CMEs) can create significant disruption to human activities and systems on Earth, much of which can be mitigated with prior warning of the upstream solar wind conditions. However, it is currently extremely challenging to accurately predict the arrival time and internal structure of a CME from coronagraph images alone. In this study, we take advantage of a rare opportunity to use Solar Orbiter, at 0.5\,AU upstream of Earth, as an upstream solar wind monitor. We were able to use real time science quality magnetic field measurements, taken only 12 minutes earlier, to predict the arrival time of a CME prior to reaching Earth. We used measurements at Solar Orbiter to constrain an ensemble of simulation runs from the ELEvoHI model, reducing the uncertainty in arrival time from 10.4\,hours to 2.5\,hours. There was also an excellent agreement in the $B_z$ profile between Solar Orbiter and Wind spacecraft, despite being separated by 0.5\,AU and 10$^{\circ}$ longitude. Therefore, we show that it is possible to predict not only the arrival time of a CME, but the sub-structure of the magnetic field within it, over a day in advance. The opportunity to use Solar Orbiter as an upstream solar wind monitor will repeat once a year, which should further help assess the efficacy upstream in-situ measurements in real time space weather forecasting., Submitted for publication in Space Weather

Studying the ambient solar wind, a continuous pressure-driven plasma flow emanating from our Sun, is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and especially during solar minimum are themselves a driver of activity in the Earth's magnetic field. Accurately forecasting the ambient solar wind flow is therefore imperative to space weather awareness. Here we present a machine learning approach in which solutions from magnetic models of the solar corona are used to output the solar wind conditions near the Earth. The results are compared to observations and existing models in a comprehensive validation analysis, and the new model outperforms existing models in almost all measures. In addition, this approach offers a new perspective to discuss the role of different input data to ambient solar wind modeling, and what this tells us about the underlying physical processes. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth., 15 pages, 6 figures, 3 tables

Geomagnetic storms resulting from high-speed streams can have significant negative impacts on modern infrastructure due to complex interactions between the solar wind and geomagnetic field. One measure of the extent of this effect is the Kyoto $Dst$ index. We present a method to predict $Dst$ from data measured at the Lagrange 5 (L5) point, which allows for forecasts of solar wind development 4.5 days in advance of the stream reaching the Earth. Using the STEREO-B satellite as a proxy, we map data measured near L5 to the near-Earth environment and make a prediction of the $Dst$ from this point using the Temerin-Li $Dst$ model enhanced from the original using a machine learning approach. We evaluate the method accuracy with both traditional point-to-point error measures and an event-based validation approach. The results show that predictions using L5 data outperform a 27-day solar wind persistence model in all validation measures but do not achieve a level similar to an L1 monitor. Offsets in timing and the rapidly-changing development of $B_z$ in comparison to $B_x$ and $B_y$ reduce the accuracy. Predictions of $Dst$ from L5 have an RMSE of $9$ nT, which is double the error of $4$ nT using measurements conducted near the Earth. The most useful application of L5 measurements is shown to be in predicting the minimum $Dst$ for the next four days. This method is being implemented in a real-time forecast setting using STEREO-A as an L5 proxy, and has implications for the usefulness of future L5 missions., 20 pages, 6 figures, in press at AGU Space Weather

The Solar TErrestrial RElations Observatory (STEREO) and its heliospheric imagers (HIs) have provided us the possibility to enhance our understanding of the interplanetary propagation of coronal mass ejections (CMEs). HI‐based methods are able to forecast arrival times and speeds at any target and use the advantage of tracing a CME's path of propagation up to 1 AU and beyond. In our study, we use the ELEvoHI model for CME arrival prediction together with an ensemble approach to derive uncertainties in the modeled arrival time and impact speed. The CME from 3 November 2010 is analyzed by performing 339 model runs that are compared to in situ measurements from lined‐up spacecraft MErcury Surface, Space ENvironment, GEochemistry, and Ranging and STEREO‐B. Remote data from STEREO‐B showed the CME as halo event, which is comparable to an HI observer situated at L1 and observing an Earth‐directed CME. A promising and easy approach is found by using the frequency distributions of four ELEvoHI output parameters, drag parameter, background solar wind speed, initial distance, and speed. In this case study, the most frequent values of these outputs lead to the predictions with the smallest errors. Restricting the ensemble to those runs, we are able to reduce the mean absolute arrival time error from 3.5 ± 2.6 to 1.6 ± 1.1 hr at 1 AU. Our study suggests that L1 may provide a sufficient vantage point for an Earth‐directed CME, when observed by HI, and that ensemble modeling could be a feasible approach to use ELEvoHI operationally., Key Points Heliospheric imaging at the Sun‐Earth L1 point may be supplementary to an operational space weather mission at L5Information on the ecliptic extent of a CME improves HI‐based predictionsELEvoHI ensemble prediction can be restricted by considering frequency distributions of internal fitting parameters

We present an advance toward accurately predicting the arrivals of coronal mass ejections (CMEs) at the terrestrial planets, including Earth. For the first time, we are able to assess a CME prediction model using data over two thirds of a solar cycle of observations with the Heliophysics System Observatory. We validate modeling results of 1337 CMEs observed with the Solar Terrestrial Relations Observatory (STEREO) heliospheric imagers (HI) (science data) from 8 years of observations by five in situ observing spacecraft. We use the self‐similar expansion model for CME fronts assuming 60° longitudinal width, constant speed, and constant propagation direction. With these assumptions we find that 23%–35% of all CMEs that were predicted to hit a certain spacecraft lead to clear in situ signatures, so that for one correct prediction, two to three false alarms would have been issued. In addition, we find that the prediction accuracy does not degrade with the HI longitudinal separation from Earth. Predicted arrival times are on average within 2.6 ± 16.6 h difference of the in situ arrival time, similar to analytical and numerical modeling, and a true skill statistic of 0.21. We also discuss various factors that may improve the accuracy of space weather forecasting using wide‐angle heliospheric imager observations. These results form a first‐order approximated baseline of the prediction accuracy that is possible with HI and other methods used for data by an operational space weather mission at the Sun‐Earth L5 point., Key Points Hindsight predictions of CMEs by modeling observations from the STEREO heliospheric imagers science data are analyzed for 2007‐2014About one out of four predicted CME arrivals is observed in situ as a magnetic obstacle by MESSENGER, Venus Express, Wind, and STEREO A/BAlthough using strong assumptions on CME physics, prediction accuracies for CME arrival times are similar to numerical or analytical models