Space Weather Prediction Research Articles

Development of risk assessment technology for small spacecraft based on a space weather heat map

This study focuses on the development and implementation of a specialized technology to address the challenges posed by space weather on the performance and operational reliability of small spacecraft, particularly CubeSats and nanosatellites in Low Earth Orbit (LEO). The research emphasizes the importance of data acquisition, processing, and visualization of energy particle fluxes in near-Earth space, with a web application demonstrating visualization of average electron values in a heat map format. The proposed technology, in conjunction with space weather prediction methods, allows for quick response to adverse conditions, contributing to increased longevity and operational efficiency of small spacecraft. In the absence of comprehensive protection systems, the research suggests simple organizational methods as an effective approach for risk mitigation and reliability improvement, applicable even to spacecraft already in orbit.

Geomagnetic storm forecasting from solar coronal holes

ABSTRACT Coronal holes (CHs) are the source of high-speed streams (HSSs) in the solar wind, whose interaction with the slow solar wind creates corotating interaction regions (CIRs) in the heliosphere. Whenever the CIRs hit the Earth, they can cause geomagnetic storms. We develop a method to predict the strength of CIR/HSS-driven geomagnetic storms directly from solar observations using the CH areas and associated magnetic field polarity. First, we build a data set comprising the properties of CHs on the Sun, the associated HSSs, CIRs, and orientation of the interplanetary magnetic field (IMF) at L1, and the strength of the associated geomagnetic storms by the geomagnetic indices Dst and Kp. Then, we predict the Dst and Kp indices using a Gaussian process model, which accounts for the annual variation of the orientation of Earth’s magnetic field axis. We demonstrate that the polarity of the IMF at L1 associated with CIRs is preserved in around 83 per cent of cases when compared to the polarity of their CH sources. Testing our model over the period 2010–2020, we obtained a correlation coefficient between the predicted and observed Dst index of R = 0.63/0.73, and Kp index of R = 0.65/0.67, for HSSs having a polarity towards/away from the Sun. These findings demonstrate the possibility of predicting CIR/HSS-driven geomagnetic storms directly from solar observations and extending the forecasting lead time up to several days, which is relevant for enhancing space weather predictions.

On the importance of investigating CME complexity evolution during interplanetary propagation

This perspective paper brings to light the need for comprehensive studies on the evolution of interplanetary coronal mass ejection (ICME) complexity during propagation. To date, few studies of ICME complexity exist. Here, we define ICME complexity and associated changes in complexity, describe recent works and their limitations, and outline key science questions that need to be tackled. Fundamental research on ICME complexity changes from the solar corona to 1 AU and beyond is critical to our physical understanding of the evolution and interaction of transients in the inner heliosphere. Furthermore, a comprehensive understanding of such changes is required to understand the space weather impact of ICMEs at different heliospheric locations and to improve on predictive space weather models.

Space Weather Environment During the SpaceX Starlink Satellite Loss in February 2022

AbstractOn 3 February 2022, SpaceX Starlink launched and subsequently lost 38 of 49 satellites due to enhanced neutral density associated with a geomagnetic storm. This study examines the space weather conditions related to the satellite loss, based on observations, forecasts, and numerical simulations from the National Oceanic and Atmospheric Administration Space Weather Prediction Center (SWPC). Working closely with the Starlink team, the thermospheric densities along the satellite orbits were estimated and the neutral density increase leading to the satellite loss was investigated. Simulation results suggest that during the geomagnetic storm, pre‐launch Monte Carlo analyses performed by the Starlink team using empirical neutral density inputs from NRLMSISE‐00 tended to underestimate the impact relative to predictions from the operational coupled Whole Atmosphere Model and Ionosphere Plasmasphere Electrodynamics physics‐based model. The numerical simulation indicated this minor to moderate geomagnetic storm was sufficient to create 50%–125% density enhancement at altitudes ranging between 200 and 400 km. With the increasing solar activity of Solar Cycle 25, satellites in low‐Earth orbit are expected to experience an increasing number of thermospheric expansion events. Currently, no alerts and warnings issued by SWPC are focused on satellite users concerned with atmospheric drag and related applications. Thus, during geomagnetic storms, it is crucial to establish suitable alerts and warnings based on neutral density predictions to provide users guidance for preventing satellite losses due to drag and to aid in collision avoidance calculations.

Deep-learning Reconstruction of Sunspot Vector Magnetic Fields for Forecasting Solar Storms

Solar magnetic activity produces extreme solar flares and coronal mass ejections, which pose grave threats to electronic infrastructure and can significantly disrupt economic activity. It is therefore important to appreciate the triggers of explosive solar activity and develop reliable space weather forecasting. Photospheric vector magnetic field data capture sunspot magnetic field complexity and can therefore improve the quality of space weather prediction. However, state-of-the-art vector field observations are consistently only available from Solar Dynamics Observatory/Helioseismic and Magnetic Imager (HMI) since 2010, with most other current and past missions and observational facilities, such as Global Oscillations Network Group (GONG), only recording line-of-sight (LOS) fields. Here, using an inception-based convolutional neural network (CNN), we reconstruct HMI sunspot vector field features from LOS magnetograms of HMI and GONG with high fidelity (∼90% correlation) and sustained flare forecasting accuracy. We rebuild vector field features during the 2003 Halloween storms, for which only LOS field observations are available, and the CNN-estimated electric current helicity accurately captures the observed rotation of the associated sunspot prior to the extreme flares, showing a striking increase. Our study thus paves the way for reconstructing three solar cycles worth of vector field data from past LOS measurements, which are of great utility in improving space weather forecasting models and gaining new insights about solar activity.

Improving CME evolution and arrival predictions with AMR and grid stretching in Icarus

Context.Coronal mass ejections (CMEs) are one of the main drivers of disturbances in interplanetary space. Strong CMEs, when directed towards the Earth, cause geomagnetic storms upon interacting with the Earth’s magnetic field, and can cause significant damage to our planet and affect everyday life. As such, efficient space weather prediction tools are necessary to forecast the arrival and impact of CME eruptions. Recently, a new heliospheric model called Icarus was developed based on MPI-AMRVAC, which is a 3D ideal magnetohydrodynamics model for the solar wind and CME propagation, and it introduces advanced numerical techniques to make the simulations more efficient. In this model the reference frame is chosen to be co-rotating with the Sun, and radial grid stretching together with adaptive mesh refinement (AMR) can be applied to the numerical domain.Aims.Grid stretching and AMR speed up simulation results and performance. Our aim is to combine the advanced techniques available in the Icarus model in order to obtain better results with fewer computational resources than with the equidistant grid. Different AMR strategies are suggested, depending on the purpose of the simulation.Methods.In this study, we model the CME event that occurred on July 12, 2012. A cone model was used to study the CME’s evolution through the background solar wind, and its arrival at and impact with the Earth. Grid stretching and AMR were combined in the simulations by using multiple refinement criteria, to assess its influence on the simulations’ accuracy and the required computational resources. We compare simulation results to the EUHFORIA model.Results.We applied different refinement criteria to investigate the potential of solution AMR for different applications. As a result, the simulations were sped up by a factor of ∼17 for the most optimal configuration in Icarus. For the cone CME model, we found that limiting the AMR to the region around the CME-driven shock yields the best results. The results modelled by the simulations with radial grid stretching and AMR level 4 are similar to the results provided by the original EUHFORIA and Icarus simulations with the ‘standard’ resolution and equidistant grids. The simulations with 5 AMR levels yielded better results than the simulations with an equidistant grid and standard resolution.Conclusions.Solution AMR is flexible and provides the user the freedom to modify and locally increase the grid resolution according to the purpose of the simulation. We find that simulations with a combination of grid stretching and AMR can reproduce the simulations performed on equidistant grids significantly faster. The advanced techniques implemented in Icarus can be further used to improve the forecasting procedures, since the reduced simulation time is essential to make physics-based forecasts less computationally expensive.

To Rain or Not to Rain: Correlating GOES Flare Class and Coronal Rain Statistics

Post-flare arcades are well-known components of solar flare evolution, which have been observed for several decades. Coronal rain, cascades of catastrophically cooled plasma, outlines the loops and provides eye-catching evidence of the recent flare. These events are acknowledged to be common, but the scientific literature does not include any statistical overview documenting just how common the phenomenon actually is. This study reviews Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO AIA) observations of 241 flares collected from the Space Weather Prediction Center database between 2011 and 2018. The flares cover the entire strength range of the C, M, and X GOES classes, and are distributed evenly across the SDO-observed majority of Solar Cycle 24. We find that post-flare arcade rain occurs for nearly all X- and most M-class flares, but that it tapers off rapidly within C-class flares. There appears to be a cut-off point around C5, below which the occurrence of post-flare arcade rain drops significantly. There is also a general positive correlation between GOES class and the average duration of post-flare rain events. Post-flare arcade rain events in X- and M-class flares appear to track with the sunspot number, providing a potential new tool for estimating, if not predicting, solar cycle strength. Furthermore, arcades are observed to persist for up to several days after the originating flare, transitioning from hosting post-flare rain to typical quiescent active region condensations. These results open up further avenues for future research, including new methods to estimate energy deposition and to gain greater insight into steady active region heating.

HUXt—An open source, computationally efficient reduced-physics solar wind model, written in Python

HUXt is an open source numerical model of the solar wind written in Python. It is based on the solution of the 1D inviscid Burger’s equation. This reduced-physics approach produces solar wind flow simulations that closely emulate the flow produced by 3-D magnetohydrodynamic (MHD) solar wind models at a small fraction of the computational expense. While not intended as a replacement for 3-D MHD, the simplicity and computational efficiency of HUXt offers several key advantages that enable experiments and the use of techniques that would otherwise be cost prohibitive. For example, large ensembles of 102–105 members can easily be run with modest computing resources, which are useful for exploring and quantifying the uncertainty in space weather predictions, as well as for the application of some data assimilation methods. In this article we present the developments in the latest version of HUXt, v4.0, and discuss our plans for future developments and applications of the model. The three key developments in v4.0 are: 1) a restructuring of the models solver to enable fully time-dependent boundary conditions, such that HUXt can in principle be initialised with in-situ observations from any of the fleet of heliospheric monitors; 2) new functionality to trace streaklines through the HUXt flow solutions, which can be used to track features such as the Heliospheric Current Sheet; 3) introduction of a small test-suite so that we can better ensure the reliability and reproducibility of HUXt simulations for all users across future versions. Other more minor developments are discussed in the article. Future applications of HUXt are discussed, including the development of both sequential and variational data assimilation schemes for assimilation of both remote sensing and in-situ plasma measures. Finally, we briefly discuss the progress of transitioning HUXt into an operational model at the UK’s Met Office Space Weather Operations Center as part of the UK governments SWIMMR programme.

Measurements of metastable helium in Earth’s atmosphere by resonance lidar

Monitoring and predicting space weather activity is increasingly important given society’s growing reliance on space-based infrastructure but is hampered by a lack of observational data. Airglow at 1083 nm from metastable helium He(23S) in the thermosphere has long been a target for remote-sensing instruments seeking to fill that gap; however, passive measurements of He(23S) fluorescence are limited by low brightness, and interpretation of these observations is complicated by the > 500 km depth of the He(23S) layer. Here, we demonstrate a lidar instrument that is able to stimulate and detect He(23S) fluorescence, and we present measured profiles of He(23S) density. These measurements provide crucial validation to space weather models, support predictions of peak number density ( ~ 1 cm−3) and the dependence of density on altitude, solar zenith angle, and season, and extend by a factor of 4 the maximum probed altitude range by an atmospheric profiling lidar. These measurements open the door for the development of more sophisticated lidars: by applying well-established spectroscopic lidar techniques, one can measure the Doppler shift and broadening of the He(23S) line, thereby retrieving profiles of neutral wind speed and temperature, opening a window for studying space weather phenomena.

A comparative study on geoeffective and non-geoeffective corotating interaction regions

A long database of corotating interaction regions (CIRs) is used to investigate their geoeffectiveness. Among 290 CIR events identified during Solar Cycle 24 (2008–2019), 88 (30%) are found to be geoeffective in causing geomagnetic storms with the symmetric ring current index [SYM-H] peak ⩽-50nT, and 202 (70%) are non-geoeffective with the minimum SYM-H >-50nT. Based on a comparative study of the geoeffective and non-geoeffective CIRs, following results are obtained. Geoeffective and non-geoeffective CIRs have comparable duration (28±15hours, 26±14hours, respectively) and radial extent (0.33±0.17AU, 0.30±0.17AU, respectively), on average. While the mean solar-wind plasma speed during the geoeffective (503±70km s−1) and non-geoeffective (491±63km s−1) CIRs exhibits no statistical difference, the geoeffective CIRs have ≈25% higher peak plasma density, ≈45% higher ram pressure, ≈42% higher temperature, ≈39% higher interplanetary magnetic field (IMF) magnitude, ≈57% stronger IMF southward component, and ≈61% stronger reconnection electric field than the non-geoeffective events, on average. The auroral electrojet index [AE] and SYM-H are found to be, respectively, ≈58% and ≈192% stronger during the geoeffective CIRs than the non-geoeffective events. The typical characteristic solar-wind and geomagnetic activity parameters given in this article can be useful for space weather modeling and prediction purposes.

Integrated Geostationary Solar Energetic Particle Events Catalog: GSEP

We present a catalog of solar energetic particle (SEP) events covering solar cycles 22, 23 and 24. We correlate and integrate three existing catalogs based on Geostationary Operational Environmental Satellite integral proton flux data. We visually verified and labeled each event in the catalog to provide a homogenized data set. We have identified a total of 341 SEP events of which 245 cross the Space Weather Prediction Center threshold of a significant proton event. The metadata consists of physical parameters and observables concerning the possible source solar eruptions, namely flares and coronal mass ejections for each event. The sliced time-series data of each event, along with intensity profiles of proton fluxes in several energy bands, have been made publicly available. This data set enables researchers in machine learning and statistical analysis to understand the SEPs and the source eruption characteristics useful for space weather prediction.

Evolution of the critical torus instability height and coronal mass ejection likelihood in solar active regions

Aims. Working towards improved space weather predictions, we aim to quantify how the critical height at which the torus instability drives coronal mass ejections (CMEs) varies over time in a sample of solar active regions. Methods. We model the coronal magnetic fields of 42 active regions and quantify the critical height at their central polarity inversion lines throughout their observed lifetimes. We then compare these heights to the changing magnetic flux at the photospheric boundary and identify CMEs in these regions. Results. In our sample, the rates of CMEs per unit time are twice as high during phases when magnetic flux is increasing than when it is decreasing, and during those phases of increasing flux, the rate of CMEs is 63% higher when the critical height is rising than when it is falling. Furthermore, we support and extend the results of previous studies by demonstrating that the critical height in active regions is generally proportional to the separation of their magnetic polarities through time. When the separation of magnetic polarities in an active region increases, for example during the continuous emergence and expansion of a magnetic bipole, the critical height also tends to increase. Conversely, when the polarity separation decreases, for example due to the emergence of a new, compact bipole at the central inversion line of an existing active region or into a quiet-Sun environment, the critical height tends to decrease.

SunRunner1D: A Tool for Exploring ICME Evolution through the Inner Heliosphere

Accurate forecasts of the properties of interplanetary coronal mass ejections (ICMEs) prior to their arrival at Earth are unquestionably a key goal for space weather. Currently, there are several promising techniques for accomplishing this, including the more focused but limited objective of predicting the time of arrival (ToA) of the ICME at Earth. In this study, we describe a new tool, sunRunner1D, with the initial goal of being able to reproduce the structure and evolution of four categories of CMEs as they propagate from the corona to 1 AU. We demonstrate that sunRunner1D can reproduce the essential properties of these ICMEs to varying degrees of success. We suggest that, ultimately, this tool could assist operational forecasters in predicting space weather events, and their associated geomagnetic consequences. In the nearer term, we anticipate that it could potentially provide useful forecasts for ToA.

Knowledge‐Informed Deep Neural Networks for Solar Flare Forecasting

AbstractRecently, although various deep learning techniques have been applied to building space weather prediction models, a large amount of relevant prior knowledge of solar eruptions and magnetic properties is ignored during the model development. By integrating prior knowledge in flare production into the convolutional neural network (CNN) structures, we have developed a knowledge‐informed deep neural network model aiming at forecasting solar flares. The line‐of‐sight magnetograms of Space‐weather HMI Active Region Patches (SHARP) from May 2010 to December 2018 are selected. We have surveyed the relationships between solar flares and both the active region (AR) area and magnetic type. When integrating prior knowledge into the CNNs, three aspects are considered: (a) keeping the magnetic structure unchanged (data preprocessing) while filling SHARP magnetograms into squares, (b) grouping the data samples into two subsets according to different flare productivities (sample grouping), and (c) adding AR area as an extra input parameter to the CNN (extra input parameter implementation). Pure CNN model, Fusion model 1, and Fusion model 2 are built to forecast M‐class or above flares in the next 48 hr, which involve data preprocessing, data preprocessing and sample grouping, and all the three aspects, respectively. Fusion model 2 that augments the most prior knowledge has the best performance. Our results imply that prior knowledge can play an important role in building deep learning flare forecasting models. In the future, adopting knowledge‐informed deep neural networks will be an effective way to further improve the forecasting performance for other space weather events.

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

AbstractThe ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

Active region chromospheric magnetic fields

Context. A proper estimate of the chromospheric magnetic fields is thought to improve modelling of both active region and coronal mass ejection evolution. However, because the chromospheric field is not regularly obtained for sufficiently large fields of view, estimates thereof are commonly obtained through data-driven models or field extrapolations, based on photospheric boundary conditions alone and involving pre-processing that may reduce details and dynamic range in the magnetograms. Aims. We investigate the similarity between the chromospheric magnetic field that is directly inferred from observations and the field obtained from a magnetohydrostatic (MHS) extrapolation based on a high-resolution photospheric magnetogram. Methods. Based on Swedish 1-m Solar Telescope Fe I 6173 Å and Ca II 8542 Å observations of NOAA active region 12723, we employed the spatially regularised weak-field approximation (WFA) to derive the vector magnetic field in the chromosphere from Ca II, as well as non-local thermodynamic equilibrium (non-LTE) inversions of Fe I and Ca II to infer a model atmosphere for selected regions. Milne-Eddington inversions of Fe I serve as photospheric boundary conditions for the MHS model that delivers the three-dimensional field, gas pressure, and density self-consistently. Results. For the line-of-sight component, the MHS chromospheric field generally agrees with the non-LTE inversions and WFA, but tends to be weaker by 16% on average than these when larger in magnitude than 300 G. The observationally inferred transverse component is systematically stronger, up to an order of magnitude in magnetically weaker regions, but the qualitative distribution with height is similar to the MHS results. For either field component, the MHS chromospheric field lacks the fine structure derived from the inversions. Furthermore, the MHS model does not recover the magnetic imprint from a set of high fibrils connecting the main polarities. Conclusions. The MHS extrapolation and WFA provide a qualitatively similar chromospheric field, where the azimuth of the former is better aligned with Ca II 8542 Å fibrils than that of the WFA, especially outside strong-field concentrations. The amount of structure as well as the transverse field strengths are, however, underestimated by the MHS extrapolation. This underscores the importance of considering a chromospheric magnetic field constraint in data-driven modelling of active regions, particularly in the context of space weather predictions.

Geomagnetic disturbances reduce heart rate variability in the Normative Aging Study

BackgroundSolar and geomagnetic activity (GA) have been linked to increased cardiovascular (CVD) events. We hypothesize that heart rate variability (HRV) may be the biological mechanism between increased CVD risk and intense geomagnetic disturbances (GMD). MethodsTo evaluate the impact of GA and intense GMD on HRV in 809 elderly men [age mean 74.5 (SD = 6.8)] enrolled in the Normative Aging Study (Greater Boston Area), we performed repeated-measures using mixed-effects regression models. We evaluated two HRV outcomes: the square root of the mean squared differences of successive normal-to-normal intervals (r-MSSD) and the standard deviation of normal-to-normal heartbeat intervals (SDNN) in milliseconds (ms). We also compared the associations between Kp and HRV in patients with and without comorbidities such as diabetes and coronary heart diseases (CHD). We used data on global planetary K-Index (Kp) from middle latitudes as a GA and GMD (>75th Kp) parameters from the National Oceanic and Atmospheric Agency's Space Weather Prediction Center. ResultsWe found a near immediate effect of continuous and higher Kp on reduced HRV for exposures up to 24 h prior to electrocardiogram recording. A 75th percentile increase in 15-hour Kp prior the examination was associated with a −14.7 ms change in r-MSSD (95 CI: −23.1, −6.3, p-value = 0.0007) and a −8.2 ms change in SDNN (95 CI: −13.9, −2.5, p-value = 0.006). The associations remained similar after adjusting the models for air pollutants over the exposure window prior to the event. In periods of intense GMD, the associations were stronger in patients with CHD and non-diabetes. ConclusionsThis is the first study to demonstrate the potential adverse effects of geomagnetic activity on reduced heart rate variability in a large epidemiologic cohort over an extended period, which may have important clinical implications among different populations.

EAST-Educational Adaptive-optics Solar Telescope

For the public having a better understanding of solar activities, the Educational Adaptive-optics Solar Telescope (EAST) was built in July 2021 and is located at the Shanghai Astronomy Museum. The EAST consists of a 65 cm aperture solar telescope with a 177-element adaptive optics system and two-channel high resolution imaging system at the Hα and TiO bands, in addition to three full disk solar telescopes at Ca K, Hα and TiO bands equipped on the tube of the main telescope. In this paper, the configuration of the EAST is described. Its performance and on-sky observational results are presented. The EAST, to our knowledge, is the most advanced solar telescope for the popularization of science in the world. Due to its excellent performance, the data acquired by the EAST can also be used for research on solar physics and space weather prediction.

Multimode Representation of the Magnetic Field for the Analysis of the Nonlinear Behavior of Solar Activity as a Driver of Space Weather

Estimating and predicting space weather is important to the space industry and space missions. The driver of space weather, especially near the Earth, is solar activity, the study of which is an important task. In particular, there is a direction of problems based on models of solar magnetic field generation that require research. In our work, we build a nonlinear dynamic system of equations that describes the behavior of the solar magnetic field harmonics based on the alpha-omega dynamo model. We found that, at the beginning of the magnetic field generation process, when the dynamo number significantly exceeds the threshold, the most rapidly growing waves are in the lead. Then, over time, these waves stop growing quite quickly. In this case, the initially slowly increasing harmonics of the magnetic field become the leaders, which then make the main contribution to the process of magnetic field generation.

Multipoint Analysis of the Interaction between a Shock and an ICME-like Structure around 2011 March 22

This work reports on the interaction between a fast forward shock and an interplanetary coronal-mass-ejection-like structure (ICMELS) as observed by in situ observations of radially aligned spacecraft. Around 2011 March 22, the Venus EXpress (VEX) and Solar TErrestrial RElations Observatory-A (STEREO-A) were nearly at the same longitude, providing us with an excellent opportunity to study the formation and evolution of the complex structures. The shock and ICMELS investigated in this paper are isolated near Venus, but when they approach STEREO-A, the shock nearly approaches the front edge of the ICMELS and forms a shock–ICMELS complex structure. The maximal magnetic field in the ICMELS increased 2.3 times due to shock compression, according to the observation. The recovery model, which restores the shocked portion of the shock–ICMELS to its uncompressed condition, likewise confirms this improvement. The interaction with the ICMELS, on the other hand, weakens shock 2. The magnetic compression ratio falls from 2.4 at Venus to 2.0 at STEREO-A. This research enables us to have a better physical knowledge of the impacts of the interaction between a shock and an ICME (or ICMELS), which will aid future space weather predictions.