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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. In combination with low‐latency images from STEREO‐A, we successfully predicted the arrival time of two CME events before they reached Earth. Measurements at Solar Orbiter were used to constrain an ensemble of simulation runs from the ELEvoHI model, reducing the uncertainty in arrival time from 10.4 to 2.5 hr in the first case study. There was also an excellent agreement in the Bz profile between Solar Orbiter and Wind spacecraft for the second case study, despite being separated by 0.5 au and 10° longitude. 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. Plain Language Summary: Coronal mass ejections (CMEs) are large eruptions of plasma from the Sun that can significantly disrupt human technology when directed toward Earth. Much like weather on Earth, the consequences of these "space weather" events can be lessened with warning of their arrival, for example, putting satellites into safe mode. This is usually done by identifying a CME in telescope images, and then predicting if and when it will arrive at Earth. However, the current forecasting models have large uncertainties in arrival time, and struggle to predict the in situ properties of the CME, which can significantly alter the severity of the event. In this paper, by taking advantage of a period in March 2022, we were able to use real time measurements from halfway between the Sun and the Earth taken by the Solar Orbiter spacecraft. This allowed us to predict the arrival time of a CME more than a day before it arrived at Earth, a significant improvement on the current capabilities. We also show that the Solar Orbiter measurements can be used to constrain a CME propagation model, significantly improving the accuracy and precision of the forecasted arrival time. Key Points: Real time data from Solar Orbiter and STEREO‐A were used to predict the arrival time of two coronal mass ejections before arrival at EarthFor one event, in situ measurements at 0.5 au were used to reduce the error in arrival time from 10.4 to 2.5 hr with the ELEvoHI modelThe in situ Bz profile was comparable to the geomagnetic response at Earth, despite being separated by 0.5 au and 10° longitude [ABSTRACT FROM AUTHOR]

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. For the automatic detection of ICMEs we propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. The method has been tested on in situ data from the Wind spacecraft between 1997 and 2015 with a True Skill Statistic of 0.64. Out of the 640 ICMEs, 466 were detected correctly by our algorithm, producing a total of 254 false positives. Additionally, it produced reasonable results on datasets with fewer features and smaller training sets from Wind, STEREO‐A, and STEREO‐B with TSSs of 0.56, 0.57, and 0.53, respectively. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hr and 56 min, and the end time with a MAE of 3 hr and 20 min. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions. Plain Language Summary: Interplanetary coronal mass ejections (ICMEs) are part of space weather and can have severe effects on human technology. Since the detection of these events is often difficult, experts are usually needed to detect them in time series from different spacecraft. Even though, there have been attempts to solve this problem using machine learning, it is far from being solved. We propose a machine learning model, that manages to find the start and end times with improved accuracy and takes less time to train. Even though our results are quite promising, it is important to notice that there are many different catalogs documenting ICMEs, that do not always agree. Therefore, there is no unique solution to this problem, which makes it difficult for a machine learning method to learn the features of an ICME. Nevertheless, our model can be expected to make a substantial contribution to the area of space weather forecast in the future. Key Points: We automatically detect interplanetary coronal mass ejections in solar wind in situ dataWe achieve a True Skill Statistic of 0.64, Recall of 0.67 and Precision of 0.70 on data from WindWe propose a pipeline generally applicable to time series event detection problems [ABSTRACT FROM AUTHOR]

Geometric modeling of Coronal Mass Ejections (CMEs) is a widely used tool for assessing their kinematic evolution. Furthermore, techniques based on geometric modeling, 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 modeling 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 modeling 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 modeling errors are minimised for an observer in the L5 region. Furthermore, geometric modeling 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 ± 1.2 h, 8.3 ± 1.0 h, and 5.8 ± 0.9 h for the average, fast, and extreme CME scenarios. Plain Language Summary: Coronal Mass Ejections (CMEs) are the largest space weather hazard to society. To help manage this hazard, we need to understand how CMEs flow through space and to develop methods to forecast when they will arrive at Earth. To help understand how CMEs flow, a range of geometric models have been developed and are widely used. Geometric models approximate a CME as a simple geometric shape, such as a circle or ellipse, and are used to help interpret CME remote sensing observations from heliospheric imagers. So far, it has been difficult to work out how good the assumptions of geometric models are and how uncertain their predictions are. In this study, we use numerical simulations of the solar wind and CMEs to try and estimate how good the geometrical modeling assumptions are, and the size of the uncertainties on their predictions. We find that because the geometric models don't account for time‐dependent solar wind structure, that they are biased and typically predict that a CME is further out into the solar wind than it actually is. Because of this, geometric models tend to predict early arrival times at Earth. Key Points: We test the performance of geometric models for estimating coronal mass ejection kinematics with a suite of solar wind numerical model runsFor Earth‐directed coronal mass ejection scenarios, geometric modeling errors are minimised for observers in the L5 regionGeometric modeling generally overestimates a coronal mass ejections speed and predicts earlier arrivals at Earth by, on average, 8 hours [ABSTRACT FROM AUTHOR]

Predicting the Bz magnetic field embedded within interplanetary coronal mass ejections (ICMEs), also known as the Bz problem, is a key challenge in space weather forecasting. We study the hypothesis that upstream in situ measurements of the sheath region and the first few hours of the magnetic obstacle provide sufficient information for predicting the downstream Bz component. To do so, we develop a predictive tool based on machine learning that is trained and tested on 348 ICMEs from Wind, STEREO‐A, and STEREO‐B measurements. We train the machine learning models to predict the minimum value of the Bz component and the maximum value of the total magnetic field Bt in the magnetic obstacle. To validate the tool, we let the ICMEs sweep over the spacecraft and assess how continually feeding in situ measurements into the tool improves the Bz prediction. We specifically find that the predictive tool can predict the minimum value of the Bz component in the magnetic obstacle with a mean absolute error of 3.12 nT and a Pearson correlation coefficient of 0.71 when the sheath region and the first 4 hr of the magnetic obstacle are observed. While the underlying hypothesis is unlikely to solve the Bz problem, the tool shows promise for ICMEs that have a recognizable magnetic flux rope signature. Transitioning the tool to operations could lead to improved space weather forecasting. Plain Language Summary: At any time, our solar system is populated with interplanetary coronal mass ejections (ICMEs). Solar scientists and space weather forecasters track ICMEs when they are ejected from the Sun and follow their path into the vast reaches of interplanetary space. They do so because if an ICME hits Earth, it could damage our infrastructure such as power‐grids and GPS satellites, which are a mainstay of our modern civilization. The possible damage is primarily determined by the magnetic field embedded within the ICME. The North‐South magnetic field component, Bz, plays a decisive role, especially if it is pointing opposite to Earth's magnetic field. Currently we cannot predict Bz with sufficient accuracy. Scientists often refer to our limited predictive abilities as the Bz problem. Here we shine a new light on the Bz problem by developing a predictive tool based on machine learning that is trained and tested on 348 ICMEs. By feeding measurements of the ICME into machine learning algorithms, we find that our predictive tool can forecast the Bz component reasonably well. While our tool does not solve the Bz problem, it shows promise in forecasting and potentially mitigating the effects of ICMEs on our planet Earth and its inhabitants. Key Points: We hypothesize that upstream in situ measurements are sufficient to predict Bz in solar coronal mass ejectionsWe present a predictive tool that forecasts the minimum of Bz in an interplanetary coronal mass ejections with a MAE of 3.12 nT and a pearson correlation coefficient of 0.71 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 Bz in comparison to Bx and By reduce the accuracy. Predictions of Dst from L5 have a root‐mean‐square error 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 4 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. Key Points: In situ L5 data can be used for Dst forecasts at Earth and perform better than 27‐day persistenceLow consistency ofBz over multiple days limits the accuracy of Dst predicted from L5This method performs best when forecasting the minimum Dst during SIRs [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]

Abstract: Forecasting the geomagnetic effects of solar storms, known as coronal mass ejections (CMEs), is currently severely limited by our inability to predict the magnetic field configuration in the CME magnetic core and by observational effects of a single spacecraft trajectory through its 3‐D structure. CME magnetic flux ropes can lead to continuous forcing of the energy input to the Earth's magnetosphere by strong and steady southward‐pointing magnetic fields. Here we demonstrate in a proof‐of‐concept way a new approach to predict the southward field Bz in a CME flux rope. It combines a novel semiempirical model of CME flux rope magnetic fields (Three‐Dimensional Coronal ROpe Ejection) with solar observations and in situ magnetic field data from along the Sun‐Earth line. These are provided here by the MESSENGER spacecraft for a CME event on 9–13 July 2013. Three‐Dimensional Coronal ROpe Ejection is the first such model that contains the interplanetary propagation and evolution of a 3‐D flux rope magnetic field, the observation by a synthetic spacecraft, and the prediction of an index of geomagnetic activity. A counterclockwise rotation of the left‐handed erupting CME flux rope in the corona of 30° and a deflection angle of 20° is evident from comparison of solar and coronal observations. The calculated Dst matches reasonably the observed Dst minimum and its time evolution, but the results are highly sensitive to the CME axis orientation. We discuss assumptions and limitations of the method prototype and its potential for real time space weather forecasting and heliospheric data interpretation. [ABSTRACT FROM AUTHOR]