Coronal Mass Ejections Arrival Time Research Articles

CME Arrival Time Prediction via Fusion of Physical Parameters and Image Features

Coronal mass ejections (CMEs) are among the most intense phenomena in the Sun–Earth system, often resulting in space environment effects and consequential geomagnetic disturbances. Consequently, quickly and accurately predicting CME arrival time is crucial to minimize the harm caused to the near-Earth space environment. To forecast the arrival time of CMEs, researchers have developed diverse methods over the years. While existing approaches have yielded positive results, they do not fully use the available data, as they solely accept either CME physical parameters or CME images as inputs. To solve this issue, we propose a method that extracts features from both CME physical parameters and CME images and uses the attention mechanism to fuse the two types of data. First, we design a parameter feature extraction module that extracts features from CME physical parameters. After that, we adopt an effective convolutional neural network model as our image feature extraction module for extracting features from CME images. Finally, utilizing the attention mechanism, we present a feature fusion module designed to fuse the features extracted from both parameters and images of CMEs. Therefore, our model can fully utilize and combine physical parameters and image features, which allows it to capture significant and comprehensive information about CMEs.

CME Arrival Modeling with Machine Learning

Space weather phenomena have long captured the attention of the scientific community, and along with recent technological developments, the awareness that such phenomena can interfere with human activities on Earth has grown considerably. Coronal mass ejections (CMEs) are among the main drivers of space weather. Therefore, developing tools to provide information on their arrival at Earth's nearby space has become increasingly important. Liu et al. developed a tool, called CME Arrival Time Prediction Using Machine Learning Algorithms (CAT-PUMA), to obtain fast and accurate predictions of CME transit time. This present work aims at the expansion of the CAT-PUMA concept, employing supervised learning to obtain vital information about the arrival of CMEs at Earth. In this study, we report the results of our work following the implementation of supervised regression and classification models in the CAT-PUMA framework. We conducted a comparison of various machine learning models in the context of predicting the transit time of CMEs and classifying CMEs as either Earth impacting or non-impacting. In this way, we are able to provide information on the possibility of a CME reaching Earth relying on CME features and solar wind parameters measured at take-off. This application thus provides quantitative indications about the geoeffectiveness of these space weather events. While machine-learning models can demonstrate fairly strong performance in regression and classification tasks, it is not always straightforward to extrapolate their practical potential and real-world applicability. To address this challenge, we employed model interpretation techniques, specifically Shap values, to gain quantitative insights into the limitations that affect these models.

On the relationship between expansión angle of earth-directed CMES and soft X-ray emission from their related flare

In space weather, to study the impact of Earth-directed coronal mass ejections (CME) in our terrestrial environment, one of the most important parameters is the propagation speed of these disturbances. We present an improvement of the 3D CME Geometrical Propagation-Expansion Description (3D-CGPED) model developed in previous work to increase the simple that we can use in CME arrival time predictions. This 3D model estimates the arrival time of Earth-directed CMEs at Earth by including a 3D geometry for the CME propagation and expansion in interplanetary space. Since the 3D-CGPED model computes the expansion of the CME based on the radial distance of the CME front, only travel times for CMEs with welldefined shapes seen by coronographs can be estimated. In the present work, we found an empirical relationship between the expansion angle of CMEs with well-defined shapes and the start-to-peak SXR fluence of their associated flares. We applied this relationship in the 3D-CGPED model to obtain the expansion angle for 8 CMEs with an irregular shape. We found similar window errors in arrival time predictions compared to the previous work. This result allows us to complement the 3D-CGPED model to include not only regular shapes but also irregular ones for CMEs observed by coronographs in future works.

SIR‐HUXt—A Particle Filter Data Assimilation Scheme for CME Time‐Elongation Profiles

AbstractWe present SIR‐HUXt, the integration of a sequential importance resampling data assimilation scheme with the HUXt solar wind model. SIR‐HUXt assimilates the time‐elongation profiles of Coronal Mass Ejection (CME) fronts in the low heliosphere, like those extracted from heliospheric imager (HI) data. Observing System Simulation Experiments are used to explore SIR‐HUXt's performance for a simple synthetic CME scenario of an Earth directed CME in a uniform solar wind, where the CME is initialized with the average CME speed and width. These experiments are performed for a range of observer locations, from 20° to 90° behind Earth, spanning the L5 point where ESA's Vigil mission will return HI data for operational space weather forecasting. For this idealized scenario, SIR‐HUXt performs well at constraining the CME speed, and has some success at constraining the CME longitude while the CME width is largely unconstrained by SIR‐HUXt. Rank‐histograms suggest the SIR‐HUXt ensembles are well calibrated, with no indications of bias or under/over dispersion. Improved constraints on the initial CME speed lead to improvements in the CME transit time and arrival speed. For an L5 observer, SIR‐HUXt reduced the transit time and arrival speed uncertainties by 69% and 63%. Therefore, SIR‐HUXt could improve the real‐world representivity of HUXt simulations and reduce the uncertainty of CME arrival time forecasts. The idealized scenario studied here likely enhances SIR‐HUXt's performance relative to the challenge of simulating real‐world CMEs and solar wind conditions. Future work should validate SIR‐HUXt with case studies of real CMEs in structured solar wind.

Forecasting the transit times of earth-directed halo CMEs using artificial neural network: A case study application with GCS forward-modeling technique

Mitigating the lethal threats caused by coronal mass ejections (CMEs) on human and space operations can be accomplished with a fast and accurate forecast of Earth-directed CME transit times. The current paper presents a robust Cascade Forward Neural Network (CFNN) framework to predict the transit times of Earth-directed halo CMEs using a total of 290 CME/Interplanetary coronal mass ejections (ICME) pairs of datasets for the past two and half decades (solar cycle; SC 23, 24, 25). It is the first time incorporating deprojected speeds into a machine learning framework to mitigate uncertainties due to projection effects during CME transit time prediction. The CFNN model forecasted the transit times of 87 Earth-directed CMEs and recorded a mean absolute error (MAE) of 7.3 h. In addition, 5 selected fast-moving (energetic) halo CME episodes during the deep phases (solar minima) of SC 25 were reconstructed using the Graduated Cylindrical Shell (GCS) forward-modeling technique. The events were predicted using the CFNN model (MAE = 4.5 h) in comparison with the Drag-Based model (DBM; MAE = 6.2 h) and Empirical Shock Arrival model (ESA; MAE = 13.5 h) to evaluate the robustness and the flexibility of the CFNN framework as well as the reliability of the CME 3D speed as a proxy for the space speed. The CFNN framework demonstrated satisfactory performance in contrast to previously used models by minimizing CME arrival time prediction errors due to projection effects. Hence, the study validated the efficiency of the GCS model for studying the 3D kinematics of CMEs and emphasized the essence of utilizing deprojected speeds in machine learning frameworks as better alternatives for fast, reliable, and accurate CME arrival (transit) time predictions.

Can One Predict Coronal Mass Ejection Arrival Times With Thirty‐Minute Accuracy?

AbstractJ. Schmidt and Cairns (2019, https://doi.org/10.48550/arXiv.1905.08961) have recently claimed that they can predict Coronal Mass Ejection (CME) arrival times with an accuracy of 0.9 ± 1.9 hr for four separate events. They also stated that the accuracy gets better with increased grid resolution. Here, we show that combining their results with the Richardson extrapolation (Richardson & Gaunt, 1927, https://doi.org/10.1098/rsta.1927.0008), which is a standard technique in computational fluid dynamics, could predict the CME arrival time with 0.2 ± 0.26 hr accuracy. The CME arrival time errors of this model would lie in a 95% confidence interval [−0.21, 0.61] hr. We also show that the probability of getting these accurate arrival time predictions with a model with a standard deviation exceeding 2 hr is less than 0.1%, indicating that these results cannot be due to random chance. This unprecedented accuracy is about 20 times better than the current state‐of‐the‐art prediction of CME arrival times with an average error of about ±10 hr. Based on our analysis there are only two possibilities: the results shown by J. Schmidt and Cairns (2019, https://doi.org/10.48550/arXiv.1905.08961) were not obtained from reproducible numerical simulations, or their method combined by the Richardson extrapolation is in fact providing CME arrival times with half an hour accuracy. We believe that this latter interpretation is very unlikely to hold true. We also discuss how the peer‐review process apparently failed to even question the validity of the results presented by J. Schmidt and Cairns (2019, https://doi.org/10.48550/arXiv.1905.08961).

Improving the Arrival Time Estimates of Coronal Mass Ejections by Using Magnetohydrodynamic Ensemble Modeling, Heliospheric Imager Data, and Machine Learning

The arrival time prediction of coronal mass ejections (CMEs) is an area of active research. Many methods with varying levels of complexity have been developed to predict CME arrival. However, the mean absolute error (MAE) of predictions remains above 12 hr, even with the increasing complexity of methods. In this work we develop a new method for CME arrival time prediction that uses magnetohydrodynamic simulations involving data-constrained flux-rope-based CMEs, which are introduced in a data-driven solar wind background. We found that for six CMEs studied in this work the MAE in arrival time was ∼8 hr. We further improved our arrival time predictions by using ensemble modeling and comparing the ensemble solutions with STEREO-A and STEREO-B heliospheric imager data. This was done by using our simulations to create synthetic J-maps. A machine-learning (ML) method called the lasso regression was used for this comparison. Using this approach, we could reduce the MAE to ∼4 hr. Another ML method based on the neural networks (NNs) made it possible to reduce the MAE to ∼5 hr for the cases when HI data from both STEREO-A and STEREO-B were available. NNs are capable of providing similar MAE when only the STEREO-A data are used. Our methods also resulted in very encouraging values of standard deviation (precision) of arrival time. The methods discussed in this paper demonstrate significant improvements in the CME arrival time predictions. Our work highlights the importance of using ML techniques in combination with data-constrained magnetohydrodynamic modeling to improve space weather predictions.

Sensitivity of Model Estimates of CME Propagation and Arrival Time to Inner Boundary Conditions

AbstractAccurately 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 modeling 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 12 December 2008 Earth‐directed CME event, we compute ensembles using the HUXt solar wind model to analyze 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 modeled 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 0.73° and 0.64° with an ATE of 1 hour and 3 hours respectively.

Propagation of coronal mass ejections from the Sun to the Earth

Coronal Mass Ejections (CMEs), as they can inject a large amounts of mass and magnetic flux into the interplanetary space, are the primary source of space weather phenomena on the Earth. The present review first briefly introduces the solar surface signatures of the origins of CMEs and then focuses on the attempts to understand the kinematic evolution of CMEs from the Sun to the Earth. CMEs have been observed in the solar corona in white-light from a series of space missions over the last five decades. In particular, LASCO/SOHO has provided almost continuous coverage of CMEs for more than two solar cycles until today. However, the observations from LASCO suffered from projection effects and limited field of view (within 30 Rs from the Sun). The launch in 2006 of the twin STEREO spacecraft made possible multiple viewpoints imaging observations, which enabled us to assess the projection effects on CMEs. Moreover, heliospheric imagers (HIs) onboard STEREO continuously observed the large and unexplored distance gap between the Sun and Earth. Finally, the Earth-directed CMEs that before have been routinely identified only near the Earth at 1 AU in in situ observations from ACE and WIND, could also be identified at longitudes away from the Sun-Earth line using the in situ instruments onboard STEREO. Our review presents the frequently used methods for estimation of the kinematics of CMEs and their arrival time at 1 AU using primarily SOHO and STEREO observations. We emphasize the need of deriving the three-dimensional (3D) properties of Earth-directed CMEs from the locations away from the Sun-Earth line. The results improving the CME arrival time prediction at Earth and the open issues holding back progress are also discussed. Finally, we summarize the importance of heliospheric imaging and discuss the path forward to achieve improved space weather forecasting.

Universal Time Variations in the Magnetosphere and the Effect of CME Arrival Time: Analysis of the February 2022 Event that Led to the Loss of Starlink Satellites

AbstractWe study Universal Time (UT) variations in the magnetospheric response to Coronal Mass Ejection (CME) impacts, using the example of the two CMEs that led to the destruction of 38 out of 49 Starlink satellites in early February 2022. We employ the Expanding‐Contracting Polar Cap model to analyze the variation in the size of the ionospheric polar caps and an eccentric dipole model of the geomagnetic field and thereby quantify the UT variations caused by the inductive effect of the diurnal motions of the geomagnetic poles in a “geocentric‐solar” frame of reference (i.e., a frame with an X axis that points from the center of the Earth to the center of the Sun). The results show that use of a quasi‐steady convection model predicts a similar global power deposition into the thermosphere as that inferred here, but does not give the same division of that power between the northern and southern hemispheres. We demonstrate that, through the combined effects of the Russell‐McPherron dipole‐tilt mechanism on solar‐wind magnetosphere coupling and of the diurnal polar cap motions in a geocentric‐solar frame, the power deposited varies significantly with the arrival UT of the CMEs at Earth. We also show that in the events of early February 2022, both CMEs arrived at almost the optimum UT to cause maximum thermospheric heating.

Predicting CME arrival time through data integration and ensemble learning

The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.

Ensemble Simulations of the 2012 July 12 Coronal Mass Ejection with the Constant-turn Flux Rope Model

Flux-rope-based magnetohydrodynamic modeling of coronal mass ejections (CMEs) is a promising tool for prediction of the CME arrival time and magnetic field at Earth. In this work, we introduce a constant-turn flux rope model and use it to simulate the 2012 July 12 16:48 CME in the inner heliosphere. We constrain the initial parameters of this CME using the graduated cylindrical shell (GCS) model and the reconnected flux in post-eruption arcades. We correctly reproduce all the magnetic field components of the CME at Earth, with an arrival time error of approximately 1 hr. We further estimate the average subjective uncertainties in the GCS fittings by comparing the GCS parameters of 56 CMEs reported in multiple studies and catalogs. We determined that the GCS estimates of the CME latitude, longitude, tilt, and speed have average uncertainties of 5.°74, 11.°23, 24.°71, and 11.4%, respectively. Using these, we have created 77 ensemble members for the 2012 July 12 CME. We found that 55% of our ensemble members correctly reproduce the sign of the magnetic field components at Earth. We also determined that the uncertainties in GCS fitting can widen the CME arrival time prediction window to about 12 hr for the 2012 July 12 CME. On investigating the forecast accuracy introduced by the uncertainties in individual GCS parameters, we conclude that the half-angle and aspect ratio have little impact on the predicted magnetic field of the 2012 July 12 CME, whereas the uncertainties in longitude and tilt can introduce relatively large spread in the magnetic field predicted at Earth.

Quantifying the Uncertainty in CME Kinematics Derived From Geometric Modeling of Heliospheric Imager Data

AbstractGeometric 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.

Probabilistic Drag-Based Ensemble Model (DBEM) Evaluation for Heliospheric Propagation of CMEs

The Drag-based Model (DBM) is a 2D analytical model for heliospheric propagation of Coronal Mass Ejections (CMEs) in ecliptic plane predicting the CME arrival time and speed at Earth or any other given target in the solar system. It is based on the equation of motion and depends on initial CME parameters, background solar wind speed, \(w\) and the drag parameter \(\gamma \). A very short computational time of DBM (< 0.01 s) allowed us to develop the Drag-Based Ensemble Model (DBEM) that takes into account the variability of model input parameters by making an ensemble of n different input parameters to calculate the distribution and significance of the DBM results. Thus the DBEM is able to calculate the most likely CME arrival times and speeds, quantify the prediction uncertainties and determine the confidence intervals. A new DBEMv3 version is described in detail and evaluated for the first time determining the DBEMv3 performance and errors by using various CME–ICME lists and it is compared with previous DBEM versions, ICME being a short-hand for interplanetary CME. The analysis to find the optimal drag parameter \(\gamma \) and ambient solar wind speed \(w\) showed that somewhat higher values (\(\gamma \approx 0.3 \times 10^{-7}\) km−1, \(w \approx \) 425 km s−1) for both of these DBEM input parameters should be used for the evaluation than the previously employed ones. Based on the evaluation performed for 146 CME–ICME pairs, the DBEMv3 performance with mean error (ME) of −11.3 h, mean absolute error (MAE) of 17.3 h was obtained. There is a clear bias towards the negative prediction errors where the fast CMEs are predicted to arrive too early, probably due to the model physical limitations and input errors (e.g. CME launch speed). This can be partially reduced by using larger values for \(\gamma \) resulting in smaller prediction errors (\(\mathrm{ME} =-3.9\) h, MAE = 14.5 h) but at the cost of larger prediction errors for single fast CMEs as well as larger CME arrival speed prediction errors. DBEMv3 showed also slight improvement in the performance for all calculated output parameters compared to the previous DBEM versions.

Drag-Based Model (DBM) Tools for Forecast of Coronal Mass Ejection Arrival Time and Speed

Forecasting the arrival time of coronal mass ejections (CMEs) and their associated shocks is one of the key aspects of space weather research. One of the commonly used models is the analytical drag-based model (DBM) for heliospheric propagation of CMEs due to its simplicity and calculation speed. The DBM relies on the observational fact that slow CMEs accelerate whereas fast CMEs decelerate and is based on the concept of magnetohydrodynamic (MHD) drag, which acts to adjust the CME speed to the ambient solar wind. Although physically DBM is applicable only to the CME magnetic structure, it is often used as a proxy for shock arrival. In recent years, the DBM equation has been used in many studies to describe the propagation of CMEs and shocks with different geometries and assumptions. In this study, we provide an overview of the five DBM versions currently available and their respective tools, developed at Hvar Observatory and frequently used by researchers and forecasters (1) basic 1D DBM, a 1D model describing the propagation of a single point (i.e., the apex of the CME) or a concentric arc (where all points propagate identically); (2) advanced 2D self-similar cone DBM, a 2D model which combines basic DBM and cone geometry describing the propagation of the CME leading edge which evolves in a self-similar manner; (3) 2D flattening cone DBM, a 2D model which combines basic DBM and cone geometry describing the propagation of the CME leading edge which does not evolve in a self-similar manner; (4) DBEM, an ensemble version of the 2D flattening cone DBM which uses CME ensembles as an input; and (5) DBEMv3, an ensemble version of the 2D flattening cone DBM which creates CME ensembles based on the input uncertainties. All five versions have been tested and published in recent years and are available online or upon request. We provide an overview of these five tools, as well as of their similarities and differences, and discuss and demonstrate their application.

Why are ELEvoHI CME Arrival Predictions Different if Based on STEREO‐A or STEREO‐B Heliospheric Imager Observations?

AbstractAccurate forecasting of the arrival time and arrival speed of coronal mass ejections (CMEs) is an 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 modeling. 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 h between predictions from the two different viewpoints, which can reach up to 9.5 h 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 ± 9.5 h for the arrival time and 87 ± 111 km s−1 for the arrival speed. There is no tendency for one spacecraft to provide more accurate arrival predictions than the other.

Evaluation of CME Arrival Prediction Using Ensemble Modeling Based on Heliospheric Imaging Observations.

In this study, we evaluate a coronal mass ejection (CME) arrival prediction tool that utilizes the wide‐angle observations made by STEREO's heliospheric imagers (HI). The unsurpassable advantage of these imagers is the possibility to observe the evolution and propagation of a CME from close to the Sun out to 1 AU and beyond. We believe that by exploiting this capability, instead of relying on coronagraph observations only, it is possible to improve today's CME arrival time predictions. The ELlipse Evolution model based on HI observations (ELEvoHI) assumes that the CME frontal shape within the ecliptic plane is an ellipse and allows the CME to adjust to the ambient solar wind speed; that is, it is drag based. ELEvoHI is used to perform ensemble simulations by varying the CME frontal shape within given boundary conditions that are consistent with the observations made by HI. In this work, we evaluate different setups of the model by performing hindcasts for 15 well‐defined isolated CMEs that occurred when STEREO was near L4/5, between the end of 2008 and the beginning of 2011. In this way, we find a mean absolute error of between and hr depending on the model setup used. ELEvoHI is specified for using data from future space weather missions carrying HIs located at L5 or L1. It can also be used with near‐real‐time STEREO‐A HI beacon data to provide CME arrival predictions during the next 7 years when STEREO‐A is observing the Sun‐Earth space.

Space Weather Monitor at the L5 Point: A Case Study of a CME Observed with STEREO B

AbstractAn important location for future space weather monitoring is the Lagrange point 5 (L5) of the Sun‐Earth system. We test the performance of L5 for space weather monitoring using STEREO B observations of an Earth‐directed coronal mass ejection (CME), seen as a partial halo by SOHO at L1. STEREO B (located close to L5) continuously tracked the CME. By using these data in combination with methods to calculate the CME arrival time at the Earth (extrapolation, drag‐based model, and a magnetohydrodynamic model), we demonstrate that the estimation of the CME arrival time can be drastically improved by adding L5 data. Based on the L1 data alone, one could predict that the CME would arrive at the Earth. Using only the L5 data, one would not expect an arrival, as the estimations of the CME 3‐D configuration is uncertain. The combination of L1 and L5 data leads to an ambiguous prediction of the CME arrival due to low CME brightness in L1 data. To obtain an unambiguous prediction, one needs its 3‐D configuration, from observing the CME material close to the plane of the sky from at least two viewpoints (in this case L5 and, coincidentally, L4). This event demonstrates that L1 observations may be better to determine CME arrival, but L5 observations are superior for constraining arrival time. In this work, the advantages and caveats of using data from a space weather monitor at L5 for predicting interplanetary propagation of CMEs are discussed and demonstrated in a direct case study.

Ensemble CME Modeling Constrained by Heliospheric Imager Observations

Predicting the arrival of coronal mass ejections (CMEs) is one key objective of space weather forecasting. In operational space weather forecasting, solar wind numerical models are used for this task and ensemble techniques are being increasingly explored as a means to improve these forecasts. Currently, these forecasts are not constrained by the available in situ and remote sensing observations, such as those from the heliospheric imagers (HIs) on the National Aeronautics and Space Administration's (NASA's) STEREO spacecraft, which record white‐light images of solar wind and CMEs. We report case studies of four CMEs and show how HI observations can be used to improve the skill and reduce the uncertainty of ensemble hindcasts of these events. Using a computationally efficient solar wind model, we produce 200‐member ensemble hindcasts, perturbing the modeled CME parameters within uniform distributions about the best estimates. By comparing the trajectory of the modeled CME flanks with HI observations, we compute a weight for each ensemble member. Weighting the ensemble distribution of CME arrival times improves the skill and reduces the hindcast uncertainty of each event. For these four events, the weighted ensembles show a mean reduction in arrival time error of 20.1 ± 4.1%, and a mean reduction in arrival time uncertainty of 15.0 ± 7.2%, relative to the unweighted ensembles. This technique could be applied in operational space weather forecasting, if real‐time HI observations were available. Therefore, as NASA and the European Space Agency are currently planning the next space weather monitoring missions, our proof‐of‐concept study provides some evidence of the potential value of including HIs on these missions.

The Value of CME Arrival Time Forecasts for Space Weather Mitigation

AbstractSevere geomagnetic storms are driven by the coronal mass ejections (CMEs). Consequently, there has been a great deal of focus on predicting if and when a CME will arrive in near‐Earth space. However, it is useful to step back and ask, “How valuable is this information, in isolation, for making decisions to mitigate against the adverse effects of space weather?” While all severe geomagnetic storms are triggered by CMEs, not all CMEs trigger severe storms. Thus, even perfect knowledge of CME arrival time will provide “actionable” forecast information only in operational situations where false alarms can be tolerated. Of course, any CME transit model used to predict CME arrival time must also produce an estimate of CME speed at Earth. This can help discriminate between geoeffective and nongeoeffective CMEs, reducing false alarms and expanding the range of operational scenarios under which a forecast provides value. Thus, from an end‐user perspective, CME arrival speed should form part of the standard metric by which CME transit models are evaluated. Looking to the future, even coarse information about the CME magnetic properties would likely provide even greater forecast value. These points are illustrated by a simple analysis of solar wind data.