Arrival Of Coronal Mass Ejections Research Articles

An Assessment of Solar Cycle 25 progress through observation of SRBs and associated Geomagnetic Storms

Geomagnetic storms are severe aspects of Space Weather. They originate due to solar transient emissions such as coronal mass ejections (CMEs), whose energetic materials propagate in the Interplanetary medium and are coupled with the magnetosphere system. CME driven Geomagnetic storms are often associated with solar radio bursts (SRBs), particularly type II and type IV bursts. In this study, we present the preliminary results of solar radio observations and their associated geomagnetic activity during solar cycle 25 (SC 25) from January 2020 to June 2023, focusing on the cycle’s first four intense geomagnetic storms. The study used various radio telescopes, mainly the compound astronomical low-frequency low-cost instrument for spectroscopy and transportable observatory (CALLISTO), as well as OMNI data and the World Data Center for Geomagnetism. During the study period, it was found that 23 solar radio bursts diagnosed the geomagnetic storms with Dst <-50nT from 35 reported, including three severe storms of the SC 25. The time delay between the solar radio bursts and the arrival of CMEs and/or HSS near the Earth’s magnetosphere is estimated with an average value of 79 h within the [48–120 h] range for 23 geomagnetic storms associated with solar radio bursts. Among 35 geomagnetic storms recorded, five are recurring geomagnetic storms associated with coronal high-speed streams (HSS), while CMEs cause the rest with average speeds of 750 km/s. The current SC 25 recognizes four major storms within the scope of the study. On 21 April 2023, a type II radio burst followed by a type IV burst diagnosed the first severe geomagnetic storm on 24 April 2023. The second severe storm was unusual and detected in the absence of the precursor as a solar radio burst. The SRBs of type II burst and type IV burst extending in IP medium on 1 November 2021 tracked the third major storm of the cycle while the group of type III radio bursts, type II and type IV bursts on 24 February 2023, predicted the major storm on 27 February 2023. These major geomagnetic storms are linked to CMEs that show expanding flux ropes, which are signatures of type II and moving type IV radio bursts identified. Furthermore, the detected SRBs and related major geomagnetic storms are proof of high solar and magnetic activity of the ascending phase of SC 25. The SC 25 has been characterized overall, and its current progress is being tracked using observations of SRBs and magnetic activity during its rising phase.

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.

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.

Analysis of Two Selected Solar Events in 2011 and 2015 With Mars Express Radio Occultation Data

AbstractThe temporal behavior of the Martian ionosphere is highly variable due to various dynamic processes including space weather events. Here, we study the effect of solar flares and coronal mass ejections (CMEs) on the Martian ionosphere for two selected solar events in 2011 and 2015, using the publicly available Mars EXpress (MEX) radio occultation (RO) data (MaRS). We developed a data processing software that converts the calibrated Radio Occultation (RO) Doppler data to scientifically valuable atmospheric profiles. Using this software and previously unexplored MaRS observations, the variations in ionospheric parameters (electron density profiles and total electron content (TEC)) are calculated in order to evaluate the ionospheric changes due to solar flares and CMEs. The RO measurements mostly available 1–4 days apart from the peak events, showed no evident change in the TEC nor in the shape of electron density profiles except for a possible gradual decrease in altitude of M2 (main layer) peak density following the arrival of CMEs. To better quantify the effect of solar events on electron density profiles, RO observations near the time of arrival of solar flares and CMEs are crucial. This can be achieved by frequent RO measurements by various Mars orbiters including spacecraft‐to‐spacecraft measurements assisted by multi‐instrument monitoring of the ionosphere.

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.

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.

Forecasting Heliospheric CME Solar-Wind Parameters Using the UCSD Time-Dependent Tomography and ISEE Interplanetary Scintillation Data: The 10 March 2022 CME

Remotely sensed interplanetary scintillation (IPS) data from the Institute for Space-Earth Environmental Research (ISEE), Japan, allows a determination of solar-wind parameters throughout the inner heliosphere. We show the 3D analysis technique developed for these data sets that forecast plasma velocity, density, and component magnetic fields at Earth, as well at the other inner heliospheric planets and spacecraft. One excellent coronal mass ejection (CME) example that occurred on the 10 March 2022 was viewed not only in the ISEE IPS analyses, but also by the spacecraft near Earth that measured the CME arrival at one AU. Solar Orbiter, that was nearly aligned along the Earth radial at 0.45 AU, also measured the CME in plasma density, velocity, and magnetic field. BepiColombo at 0.42 AU was also aligned with the STEREO A spacecraft, and viewed this CME. The instruments used here from BepiColombo include: 1) the European-Space-Agency Mercury-Planetary-Orbiter magnetic field measurements; 2) the Japan Aerospace Exploration Agency Mio spacecraft Solar Particle Monitor that viewed the CME Forbush decrease, and the Mercury Plasma Experiment/Mercury Electron Analyzer instruments that measured particles and solar-wind density from below the spacecraft protective sunshield covering. This article summarizes the analysis using ISEE, Japan real-time data for these forecasts: it provides a synopsis of the results and confirmation of the CME event morphology after its arrival, and discusses how future IPS analyses can augment these results.

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.

Magnetohydrodynamic simulation of coronal mass ejections using interplanetary scintillation data observed from radio sites ISEE and LOFAR

Interplanetary scintillation (IPS) is a useful tool for detecting coronal mass ejections (CMEs) throughout interplanetary space. Global magnetohydrodynamic (MHD) simulations of the heliosphere, which are usually used to predict the arrival and geo-effectiveness of CMEs, can be improved using IPS data. In this study, we demonstrate an MHD simulation that includes IPS data from multiple stations to improve CME modelling. The CMEs, which occurred on 09–10 September 2017, were observed over the period 10–12 September 2017 using the Low-Frequency Array (LOFAR) and IPS array of the Institute for Space-Earth Environmental Research (ISEE), Nagoya University, as they tracked through the inner heliosphere. We simulated CME propagation using a global MHD simulation, SUSANOO-CME, in which CMEs were modeled as spheromaks, and the IPS data were synthesised from the simulation results. The MHD simulation suggests that the CMEs merged in interplanetary space, forming complicated IPS g-level distributions in the sky map. We found that the MHD simulation that best fits both LOFAR and ISEE data provided a better reconstruction of the CMEs and a better forecast of their arrival at Earth than from measurements when these simulations were fit from the ISEE site alone. More IPS data observed from multiple stations at different local times in this study can help reconstruct the global structure of the CME, thus improving and evaluating the CME modelling.

Unveiling the Space Weather During the Starlink Satellites Destruction Event on 4 February 2022

AbstractOn 4 February 2022, 38 Starlink satellites were destroyed by the geomagnetic storm, which brought significant financial, aerospace and public influences. In this letter, we reveal the space weather process during 3–4 February 2022 geomagnetic disturbances, from the Sun all the way to the satellite orbiting atmosphere. Initiated by an M1.0 class flare and the following coronal mass ejection (CME), a moderate geomagnetic storm was stimulated on 3rd February by the CME arrival at Earth. Subsequently, another moderate storm was triggered on 4th February by the passage of another CME. Model simulations driven by solar wind show that the first geomagnetic storm induced around 20% atmospheric density perturbations at 210 km altitude on 3rd February. The unexpected subsequent storm on 4th February led to a density enhancement of around 20%–30% at around 210 km. The resulting atmospheric drag can be even larger, since the regional density enhancement was over 60% and the satellite orbits were continuously decaying. This event brings forth the urgent requirements of better understanding and accurate prediction of the space weather as well as collaborations between industry and space weather community.

Predicting CMEs Using ELEvoHI With STEREO‐HI Beacon Data

AbstractBeing able to accurately predict the arrival of coronal mass ejections (CMEs) at Earth has been a long‐standing problem in space weather research and operations. In this study, we use the ELlipse Evolution model based on Heliospheric Imager (ELEvoHI) to predict the arrival time and speed of 10 CME events that were observed by HI on the STEREO‐A spacecraft between 2010 and 2020. Additionally, we introduce a Python tool for downloading and preparing STEREO‐HI data, as well as tracking CMEs. In contrast to most previous studies, we use not only science data, which have a relatively high spatial and temporal resolution, but also lower‐quality beacon data, which are—in contrast to science data—provided in real‐time by the STEREO‐A spacecraft. We do not use data from the STEREO‐B spacecraft. We get a mean absolute error of 8.81 ± 3.18 hr/59 ± 31 km s−1 for arrival time/speed predictions using science data and 11.36 ± 8.69 hr/106 ± 61 km s−1 for beacon data. We find that using science data generally leads to more accurate predictions, but using beacon data with the ELEvoHI model is certainly a viable choice in the absence of higher resolution real‐time data. We propose that these differences could be minimized if not eliminated altogether if higher quality real‐time data were available, either by enhancing the quality of the already available data or coming from a new mission carrying a HI instrument on‐board.

Erratum to: Use of the DBM Model to the Predict of Arrival of Coronal Mass Ejections to the Earth

Erratum to: Use of the DBM Model to the Predict of Arrival of Coronal Mass Ejections to the Earth

First Simultaneous In Situ Measurements of a Coronal Mass Ejection by Parker Solar Probe and STEREO-A

Abstract We present the first Parker Solar Probe mission (PSP)-observed coronal mass ejection (CME) that hits a second spacecraft before the end of the PSP encounter, providing an excellent opportunity to study short-term CME evolution. The CME was launched from the Sun on 2019 October 10 and was measured in situ at PSP on 2019 October 13 and at STEREO-A on 2019 October 14. The small, but not insignificant, radial (∼0.15 au) and longitudinal (∼8°) separation between PSP and STEREO-A at this time allows for both observations of short-term radial evolution as well as investigation of the global CME structure in longitude. Although initially a slow CME, magnetic field and plasma observations indicate that the CME drove a shock at STEREO-A and also exhibited an increasing speed profile through the CME (i.e., evidence for compression). We find that the presence of the shock and other compression signatures at 1 au are due to the CME having been overtaken and accelerated by a high speed solar wind stream (HSS). We estimate the minimum interaction time between the CME and the HSS to be ∼2.5 days, indicating the interaction started well before the CME arrival at PSP and STEREO-A. Despite alterations of the CME by the HSS, we find that the CME magnetic field structure is similar between the vantage points, with overall the same flux rope classification and the same field distortions present. These observations are consistent with the fact that coherence in the magnetic structure is needed for steady and continued acceleration of the CME.

Use of the DBM Model to the Predict of Arrival of Coronal Mass Ejections to the Earth

This paper analyzes the results of modeling coronal mass ejection (CME) propagation in 2010–2011 obtained using input data from different sources: CME catalogs SEEDS and CACTus, and predictions of the velocity of quasi-stationary solar wind fluxes, as an environment, through which CMEs propagate. As the model of quasi-stationary solar wind fluxes, the model for predicting the velocity of the solar wind of the Space Weather Forecast Center of the Skobeltsyn Institute of Nuclear Physics of Moscow State University, operating online, is used. The CME prediction is carried out using the Simple Drag-Based Model. A comparison was performed between the ICME arrival time and their velocities obtained when modeling with data from the open ICME catalogs: the Richardson and Cane ICME catalog and the GMU CME List. Based on the comparison, it was concluded that a more accurate prediction for the growth phase of the 24th solar activity cycle was obtained using data on CME parameters from the CACTus database. The obtained errors in predicting the ICME parameters are comparable with the errors of other existing models.

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.

Predicting the Time of Arrival of Coronal Mass Ejections at Earth From Heliospheric Imaging Observations

AbstractThe time of arrival (ToA) of coronal mass ejections (CMEs) at Earth is a key parameter due to the space weather phenomena associated with the CME arrival, such as intense geomagnetic storms. Despite the incremental use of new instrumentation and the development of novel methodologies, ToA estimated errors remain above 10 h on average. Here, we investigate the prediction of the ToA of CMEs using observations from heliospheric imagers, i.e., from heliocentric distances higher than those covered by the existent coronagraphs. In order to perform this work, we analyze 14 CMEs observed by the heliospheric imagers HI‐1 onboard the twin STEREO spacecraft to determine their front location and speed. The kinematic parameters are derived with a new technique based on the Elliptical Conversion (ElCon) method, which uses simultaneous observations from the two viewpoints from STEREO. Outside the field of view of the instruments, we assume that the dynamics of the CME evolution is controlled by aerodynamic drag, i.e., a force resulting from the interaction with particles from the background solar wind. To model the drag force, we use a physical model that allows us to derive its parameters without the need to rely on drag coefficients derived empirically. We found a CME ToA mean error of 1.6 ± 8.0 h ToA and a mean absolute error of 6.9 ± 3.9 h for a set of 14 events. The results suggest that observations from HI‐1 lead to estimates with similar errors to observations from coronagraphs.

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.

EUV Wave Detection and Characterization Using Deep Learning

Coronal Mass Ejections (CMEs) are the most violent solar bursts. They cause severe disturbances in the solar–terrestrial space and affect human activities in many aspects, especially causing damage to high-tech infrastructure. It usually takes few hours for a CME to arrive at the Earth after eruption. Therefore, many efforts have been devoted to CME arrival time prediction, so that we have enough time to take action before a CME arrives at the Earth. For predicting CME arrival time, it is vital to detect the CME origin, arrival and departure speed in a coronagraph. It has been widely accepted that Extreme Ultraviolet (EUV) waves are associated with CMEs, so EUV waves are the signatures of CMEs as CMEs originate and traverse the solar disk, specifically for front-side CMEs. In this paper, two deep neural networks are developed to first detect EUV waves and then outline their wavefronts, giving early signatures of CMEs. Usually, CMEs are recorded by coronagraphs as they transit the corona, so our proposed method can obtain a certain time ahead compared with conventional CME forecasting. In addition, the parameters for describing EUV waves can be more easily deduced, benefiting the subsequent statistical analysis of CMEs. The experimental results demonstrate the effectiveness of the proposed model for detecting EUV waves and generating their outlines.

Development of a coronal mass ejection arrival time forecasting system using interplanetary scintillation observations

Coronal mass ejections (CMEs) cause disturbances in the environment of the Earth when they arrive at the Earth. However, the prediction of the arrival of CMEs still remains a challenge. We have developed an interplanetary scintillation (IPS) estimation system based on a global magnetohydrodynamic (MHD) simulation of the inner heliosphere to predict the arrival time of CMEs. In this system, the initial speed of a CME is roughly derived from white-light coronagraph observations. Then, the propagation of the CME is calculated by a global MHD simulation. The IPS response is estimated by the three-dimensional density distribution of the inner heliosphere derived from the MHD simulation. The simulated IPS response is compared with the actual IPS observations made by the Institute for Space-Earth Environmental Research, Nagoya University, and shows good agreement with that observed. We demonstrated how the simulation system works using a halo CME event generated by a X9.3 flare observed on September 5, 2017. We find that the CME simulation that best estimates the IPS observation can more accurately predict the time of arrival of the CME at the Earth. These results suggest that the accuracy of the CME arrival time can be improved if our current MHD simulations include IPS data.