Solar Wind Parameters Research Articles

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.

Relativistic electron ([formula omitted]) flux driving parameters over geostationary orbit using 30 years of data

The relativistic electron population at MeV energy in the Earth’s outer radiation belt at geostationary (GOE) orbit largely varies in association with solar wind disturbances. Although more researches have been done to understand the influence of different mechanisms that cause the variations in the relativistic electron flux in the region, but still the underlying physics is often complex and unclear. The present study uses relativistic electron (E>2MeV) fluxes data of Los Alamos National Laboratory (LANL) geosynchronous satellites to understand the variations and its relationship with the driving parameters. The study examines the relativistic electron fluxes variations associated with the interplanetary magnetic field (IMF) and solar wind parameters using daily averaged data from 01 January 1990 to 31 December 2018. The results indicated that the information transfer from solar wind and IMF parameters into relativistic electrons flux is dependent on the kind of driving parameter. The result identifies the rank of driving parameters based on their maximum values in the cross correlation and information transfer analysis. Thus, AE index and vsw are the dominant driving parameters for the dynamics of relativistic electrons flux at GEO orbit. The peak values of coefficient (correlation coefficients, mutual information and entropy transfer) are observed at similar lag times (τpeak). For instance, in the case of AE and Je, we have observed the maximum coefficient (associated with lag time) as follows; maximum correlation coefficient; CCmax=0.846(5days), maximum mutual information; MImax=0.803(5days), maximum transfer entropy; TEmax=0.892(5days). Such dependency analysis can help to select the model parameters that influence the relativistic electrons fluxes and improve the existed models of the dynamical system.

Forbush decreases and geomagnetic disturbances: 2. Comparison of solar cycles 23–24 and events with sudden and gradual onset

Statistical relationships between the values of geomagnetic indices and the characteristics of cosmic rays and interplanetary disturbances are studied for Forbush decreases with sudden and gradual onset associated with different types of solar sources: a) coronal mass ejections from active regions accompanied by solar flares; b) filament eruptions outside active regions; c) high-speed streams from coronal holes; d) several sources. Using statistical methods, the dependence of geomagnetic indices on cosmic ray and solar wind parameters for Forbush decreases in solar cycles 23 and 24 is also compared. The results obtained showed: a) interplanetary disturbances associated with coronal mass ejections from active regions cause mainly magnetic storms with a sudden onset; b) interplanetary disturbances associated with high-speed streams from coronal holes cause mainly storms with a gradual onset; c) interplanetary disturbances associated with filament eruptions outside active regions cause equally probable storms with a sudden and gradual onset. For sporadic Forbush decreases the values of cosmic ray and geomagnetic activity parameters are, on average, higher for events with a sudden onset; for recurrent Forbush decreases, the nature of the event onset does not affect the value of these parameters. For all types of solar sources the parameters of the disturbed solar wind are, on average, higher in events with a sudden onset. The geoefficiency of interplanetary disturbances is much higher in the 23rd cycle for events associated with ejections from active regions; for other types of disturbances, the difference between the cycles is weak.

Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

AbstractWe propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE

AbstractThis study presents a data‐driven approach to quantify uncertainties in the ionosphere‐thermosphere (IT) system due to varying solar wind parameters (drivers) during quiet conditions (Kp < 4) and fixed solar radiation and lower atmospheric conditions representative of 16 March 2013. Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM‐IPE) driven by synthetic solar wind drivers generated through a multi‐channel variational autoencoder (MCVAE) model are obtained. Applying the polynomial chaos expansion (PCE) technique, it is possible to estimate the means and variances of the QoIs as well as the sensitivities of the QoIs with regard to the drivers. Our results highlight unique features of the IT system's uncertainty: (a) the uncertainty of the IT system is larger during nighttime; (b) the spatial distributions of the uncertainty for electron density and zonal drift at fixed local times present 4 peaks in the evening sector, which are associated with the low‐density regions of longitude structure of electron density; (c) the uncertainty of the equatorial electron density is highly correlated with the uncertainty of the zonal drift, especially in the evening sector, while it is weakly correlated with the vertical drift. A variance‐based global sensitivity analysis suggests that the IMF Bz plays a dominant role in the uncertainty of electron density. A further discussion shows that the uncertainty of the IT system is determined by the magnitudes and universal time variations of solar wind drivers. Its temporal and spatial distribution can be modulated by the average state of the IT system.

Application of collisional analysis to the differential velocity of solar wind ions

Collisional analysis combines the effects of collisional relaxation and large-scale expansion to quantify how solar wind parameters evolve as the plasma expands through the heliosphere. Though previous studies have applied collisional analysis to the temperature ratio between protons (ionized hydrogen) and α-particles (fully ionized helium), this is the first study to explore α-proton differential flow with collisional analysis. First, the mathematical model for the collisional analysis of differential flow was derived. Then, this model was applied to individual in-situ observations from Parker Solar Probe (PSP; r = 0.1–0.27 au) to generate predictions of the α-proton differential flow in the near-Earth solar wind. A comparison of these predicted values with contemporaneous measurements from the Wind spacecraft (r = 1.0 au) shows strong agreement, which may imply that the effects of expansion and Coulomb collisions have a large role in governing the evolution of differential flow through the inner heliosphere.

Thermal performance investigation of membrane-assisted radiant cooling system for localised outdoor cooling hub

Heat waves pose increasing threats to human well-being, especially in urban areas, and staying safe beyond comfort during heat waves is a crucial issue. Localised cooling hubs may provide thermal stress relief in the outdoor environment. For outdoor cooling applications, conventional air conditioning systems are not effective but energy-intensive, and the membrane-assisted radiant cooling system can be a potential alternative, which directly treats the radiant loads while minimising the convective heat loss to the ambient. In the present study, a numerical model is developed to analyse the heat transfer behavior of membrane-assisted radiant cooling for outdoor applications, considering the effect of solar and long-wave radiation and wind parameters. As the transmissivity of the membrane material plays a crucial role in reducing the amount of solar heat, the performances of three types of membranes, i.e., non-selective, mid-infrared selective, and sky-window selective, were compared and analysed at different times on a typical summer day in Hong Kong. The results indicate that the sky-window selective membrane, which allows only transmission of infrared in the 8–15 µm wavelength range, saves energy up to 44 % and 47% compared with mid-infrared selective and non-selective membranes, respectively. In the outdoor application, the equivalent convective heat transfer coefficient of the cooling panel can be reduced in the range of 2.2 W/m2K to 2.6 W/m2K. It is observed that, when the panel is not exposed to direct solar radiation, there is no advantage in using the mid-infrared selective membrane over the non-selective membrane. However, under direct solar radiation, a non-selective membrane-assisted cooling panel demonstrates poor performance, absorbing solar heat flux 2.27 times higher compared to MIR-selective membrane-assisted panel.

Disentangling the solar activity–solar wind predictive causality at Space Climate scales

The variability in the magnetic activity of the Sun is the main source of the observed changes in the plasma and electromagnetic environments within the heliosphere. The primary way in which solar activity affects the Earth’s environment is via the solar wind and its transients. However, the relationship between solar activity and solar wind is not the same at the Space Weather and Space Climate time scales. In this work, we investigate this relationship exploiting five solar cycles data of Ca II K index and solar wind parameters, by taking advantage of the Hilbert–Huang Transform, which allows to separate the contribution at the different time scales. By filtering out the high-frequency components and looking at decennial time scales, we confirm the presence of a delayed response of solar wind to Ca II K index variations, with a time lag of ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 3.1-year for the speed and ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 3.4-year for the dynamic pressure. To assess the results in a stronger framework, we make use of a Transfer Entropy approach to investigate the information flow between the quantities and to test the causality of the relation. The time lag results from the latter are consistent with the cross-correlation ones, pointing out the presence of a statistical significant information flow from Ca II K index to solar wind dynamic pressure that peaks at time lag of 3.6-year. Such a result could be of relevance to build up a predictive model in a Space Climate context.

Commencement and Interruption of Relativistic Electron Dropout in the Heart of the Outer Radiation Belt Induced by a Magnetic Cloud Event

AbstractWe analyze and discuss the commencement and interruption of the relativistic electron dropout in the heart of the outer radiation belt (L* = 4–5) induced by a typical magnetic cloud (MC) event. This MC event impinged the Earth’s magnetosphere on 31 October 2012 and caused a moderate geomagnetic storm with a special prolonged initial phase lasting for >13 hr. The relativistic electrons phase space density (PSD) dropout commenced at L* > 5 during the initial phase. The PSD dropout penetrated deep beyond the heart of the radiation belt (reached L* < 4) at the onset of the main phase, while it was partially enhanced with a local PSD maximum around L* = 4.5, thus causing the interruption of the PSD dropout. The dropout became pronounced at L* > ∼4.7 while the local PSD maximum was maintained throughout the main phase. During the recovery phase, the dropout totally disappeared at L* < 5.5 with relativistic electron PSD gradually recovering to pre‐event level or higher. Further investigations on solar wind parameters and plasma waves give evidence that (a) persistent high dynamic pressure and the triggered Ultra‐Low Frequency waves contribute to the dropout of electrons to interplanetary space during the initial phase and the onset of the main phase; (b) local acceleration by chorus waves during the main phase and recovery phase could explain the interruption of the dropout. Our study underlines the persistent high dynamic pressure competing with intense chorus waves in triggering the commencement and interruption of the relativistic electrons PSD dropout in the heart of the outer radiation belt.

Analysis of space weather and small satellite data in polar orbit using machine learning

This publication explores the dependencies between factors of space weather (geomagnetic indices Dst, Kp, and Ap, Wolf solar activity index, and solar wind parameters) and the telemetry of the small satellite Cubebel-1. The importance of maintaining the functionality of such satellites in orbit is described based on the research results of specialists in this field. A correlation analysis is presented, conducted using Python language tools. The results are presented in the form of a correlation matrix. A comparison is made between the results of this study and similar results of correlation analysis conducted with data from the satellite Siriussat-1. Suggestions for further research using the telemetry of the Cubebel-1 satellite are provided.

3D pressure-corrected ballistic extrapolation of solar wind speed in the inner heliosphere

Solar wind parameters at different locations in the inner heliosphere can be estimated using various solar wind extrapolation methods. The simple ballistic method extrapolates solar wind parameters from the point of measurement to a chosen heliospheric position by assuming that major solar wind structures are persistent and arrive relatively unaltered to the target position. The method considers the rotation period of the Sun while assuming a constant solar wind speed during radial propagation. We improve the simple ballistic model by considering the interaction between the slow and the fast solar wind with a pressure correction during the propagation. Instead of extrapolating from the position of a single spacecraft, we apply this pressure-corrected ballistic method to 2D speed maps of the solar source surface available from solar coronal models to determine the solar wind speed in the inner heliosphere in 3D, between latitudes of ±50°. We also take into account the effects of the solar differential rotation in our model. Our method is simple and fast, and it can be applied to different source surface datasets. The results of our model are validated with in situ data from the ACE spacecraft. We find that the pressure-corrected ballistic method can give accurate predictions of the solar wind in 3D.

Solar wind parameters in rising phase of solar cycle 25: Similarities and differences with solar cycles 23 and 24

Solar activity and solar wind parameters decreased significantly in solar cycles (SCs) 23–24. In this paper, we analyze solar wind measurements at the rising phase of SC 25 and compare them with similar data from the previous cycles. For this purpose, we simultaneously selected the OMNI database data for 1976–2022, both by phases of the 11-year solar cycle and by large-scale solar wind types (in accordance with catalog [http://www.iki.rssi.ru/pub/omni]), and calculated the mean values of the plasma and magnetic field parameters for the selected datasets. The obtained results support the hypothesis that the continuation of this cycle will be similar to that of cycle 24, i.e. SC 25 will be weaker than SCs 21 and 22

About the magnetic barrier of Venus

The magnetized supersonic solar wind, when flowing around planets, forms a magnetic barrier near the streamlined surface. The main feature of the magnetic barrier is that the magnetic pressure prevails over the plasma pressure. The Hall-MHD model is used to simulate the magnetic barrier in the case of solar wind flow around the atmosphere of Venus. The obtained numerical results are compared with an analytical approximation of the magnetic barrier thickness, which expresses the dependence of the magnetic barrier on solar wind parameters. Particular attention is paid to the physical reasons for the asymmetry of the magnetic barrier caused by the Hall effects, which are mainly concentrated in the boundary layer near the ionopause, where the electric current has a maximum strength. An additional source of asymmetry is also considered, which acts in the same direction and is associated with the influence of the normal component of the electric field on the specific behavior of new atmospheric ions. It is shown that solar wind protons are loaded by new atmospheric ions mainly in the E+ hemisphere. In the case of more intense loading, the boundary of the magnetic barrier and the shock wave are located farther from the ionopause.

Параметры солнечного ветра на фазе роста 25-го солнечного цикла: сходства и различия с 23-м и 24-м солнечными циклами

Solar activity and solar wind parameters decreased significantly in solar cycles (SCs) 23–24. In this paper, we analyze solar wind measurements at the rising phase of SC 25 and compare them with similar data from the previous cycles. For this purpose, we simultaneously selected the OMNI database data for 1976–2022, both by phases of the 11-year solar cycle and by large-scale solar wind types (in accordance with catalog [http://www.iki.rssi.ru/pub/omni]), and calculated the mean values of the plasma and magnetic field parameters for the selected datasets. The obtained results support the hypothesis that the continuation of this cycle will be similar to that of cycle 24, i.e. SC 25 will be weaker than SCs 21 and 22

Interaction of global electron content with the Sun and solar wind during intense geomagnetic storms

Assessment of solar and solar wind parameters driving the ionosphere model is essential for prediction of the ionospheric weather. In the present paper impact of the different solar, interplanetary and geomagnetic parameters on the global electron content (GEC) during intense space weather storms is investigated. Hourly GEC values are calculated from JPL global maps of total electron content GIM-TEC from 1995 to 2023. The sample comprises 90 intense storms from 1995 to 2023 associated with monthly peak of the weighted accumulation of the geomagnetic Apo(τ, t) index exceeding 90 nT. The 27 day weighted accumulation of the solar sunspot numbers SSN2(τ), solar radio flux F10.7(τ), the solar hydrogen emission Lyman_α(τ) and the composite magnesium MgII(τ) indices are explored as precursors of GEC enhancements. As distinct from the positive ionosphere storm, the solar wind speed Vsw, the solar wind electric field Ey, merging electric field Em and Apo(τ, t) indices proved to be effective as potential drivers of the negative GEC depletion. The positive and negative dGEC deviations from hourly GEC are produced by subtracting a quiet reference GECav averaged during 24h prior the storm normalized by GECav. The hourly storm profiles Vsw(t), Em(t), Ey(t), Apo(τ, t), Dst(t), GEC(t) and dGEC(t) were reduced by method of superposed epochs. The zero epoch t0 = 0 was taken at the peak Apo*(τ, t0) and the storm time lasted for 48h from −12h prior t0 and 35h afterwards. The best correlation of the positive storm dGECp amplitude is obtained with MgII(τ) and the negative storm dGECn with Em* and Apo*(τ, t0) which are used to derive characteristics of five key points of storm-time dGEC(t) model: 1 – onset of the storm profile t1 = t(dGECp); 2 – the amplitude dGECpmax and its time t2(dGECpmax); 3 – the time of transition t3(dGEC = 0) from the positive to negative (±) GEC storm; 4 – minimum negative disturbance dGECnmin and its time t4(dGECnmin), 5 – the end of the storm profile t5(dGECn). Analytical model of dGEC(t) is derived with Epstein step functions fitting 5 key points. Deviations dGEC(t) are inverted to GEC(t) using quiet reference pre-storm GECav. The model is validated for three intense storms on 26–28 February, 23–25 March and 23–25 April 2023. The results show improvement of dGEC forecast with RMS error reduced from 45 to 80% compared to results produced by the international reference ionosphere−plasmasphere model IRI-Plas.

Rainfall over Iraq,, was Affected by Solar Wind Parameters

Rainfall over Iraq,, was Affected by Solar Wind Parameters

Analysis of the first coronagraphic multi-band observations of a sungrazing comet

Context. Between 24 and 25 December 2021 a sungrazing comet (SOHO-4341) approached the Sun, being observed by “classical” visible light (VL) coronagraphs on board the SOHO and STEREO missions, and also by the innovative Metis coronagraph on board the ESA-NASA Solar Orbiter mission in the VL and ultraviolet (UV H I Lyman-α) band. Aims. We show how VL data acquired by the Metis coronagraph can be combined with those provided by other space-based coronagraphs to reconstruct the comet orbit, but also to provide information on the dust composition from the polarized VL emission. Moreover, we show how the UV emission can be employed to measure local plasma parameters of the ambient solar wind. Methods. By using the comet positions tracked with VL Metis images (with spatial resolution that is four times better than UV), the UV images (with a time cadence that is five times faster than VL) have been coaligned to maximize the signal-to-noise ratio in the UV band. The local electron density ne was measured from the observed exponential decay of the UV Lyman-α intensity along the tail, while the solar wind speed vwind was measured from the UV Lyman-α tail inclination with respect to the cometary orbital path deprojected in 3D. Moreover, the proton kinetic temperature Tk was also obtained by the aperture angle of the UV Lyman-α tail. Results. When the comet was at an average heliocentric distance of 14.3 R⊙, the comet had a radial speed of 155 km s−1 and a tangential speed of 59 km s−1. The comet had a UV Lyman-α tail extending in the anti-solar direction over more than 1.5 R⊙. From the analysis of the tail shape in UV we obtained the local solar wind speed (vwind = 190 km s−1), electron density (ne = 1.5 × 104 cm−3), and proton temperature (Tk = 1.2 × 106 K). Moreover, theoretical analysis of the measured UV Lyman-α intensity allowed us to estimate the radius of the cometary nucleus (Rcom = 65 m) and the water outgassing rate (QH2O = 4.8 × 1028 molec s−1). Conclusions. These results show that sungrazing comets are unique “local probes” for the ambient coronal plasma, providing measurements that are not as affected by the line-of-sight integration effects as those provided by remote sensing instruments, in regions of the Heliosphere that are not explored in situ by the ongoing space missions.

Muon Flux Variations Measured by Low‐Cost Portable Cosmic Ray Detectors and Their Correlation With Space Weather Activity

AbstractWe present a comparison of the measured cosmic ray (CR) muon fluxes from two identical portable low‐cost detectors at different geolocations and their sensitivity to space weather events in real time. The first detector is installed at Mount Wilson Observatory, CA, USA (geomagnetic cutoff rigidity Rc ∼ 4.88 GV), and the second detector is running on the downtown campus of Georgia State University in Atlanta, GA, USA (Rc ∼ 3.65 GV). The variation of the detected muon fluxes is compared to the changes in the interplanetary solar wind parameters at the L1 Lagrange point and geomagnetic indexes. In particular, we have investigated the muon flux behavior during three major interplanetary shock events and geomagnetic disturbances that occurred during July and August of 2022. To validate the interpretation of the measured muon signals, we compare the muon fluxes to the measurement from the Oulu neutron monitor (NM, Rc ∼ 0.8 GV). The results of this analysis show that the muon detector installed at Mount Wilson Observatory demonstrates a stronger correlation with a high‐latitude NM. Both detectors typically observe a muon flux decrease during the arrival of interplanetary shocks and geomagnetic storms. Interestingly, the decrease could be observed several hours before the onset of the first considered interplanetary shocks at L1 at 2022‐07‐23 02:28:00 UT driven by the high‐speed Coronal Mass Ejection and related geomagnetic storm at 2022‐07‐23 03:59:00 UT. This effort represents an initial step toward establishing a global network of portable low‐cost CR muon detectors for monitoring the sensitivity of muon flux changes to space and terrestrial weather parameters.

Statistical Properties of the Distribution and Generation of Kinetic‐Scale Flux Ropes in the Terrestrial Dayside Magnetosheath

AbstractThe generation of kinetic‐scale flux ropes (KSFRs) is closely related to magnetic reconnection. Both flux ropes and reconnection sites are detected in the magnetosheath and can impact the dynamics upstream of the magnetopause. In this study, using the Magnetospheric Multiscale satellite, 12,623 KSFRs with a scale <20 RCi are statistically studied in the Earth's dayside magnetosheath. It is found that they are mostly generated near the bow shock (BS), and propagate downstream in the magnetosheath. Their quantity significantly increases as the scale decreases, consistent with a flux rope coalescence model. Moreover, the solar wind parameters can control the occurrence rate of KSFRs. They are more easily generated at high Mach number, large proton density, and weak magnetic field strength of the solar wind, similar to the conditions that favor BS reconnection. Our study shows a close connection between KSFR generation and BS reconnection.

Rotation of a Stealth CME on 2012 October 5 Observed in the Inner Heliosphere

Coronal mass ejections (CMEs) are subject to changes in their direction of propagation, tilt, and other properties. This is because CMEs interact with the ambient solar wind and other large-scale magnetic field structures. In this work, we report on the observations of the 2012 October 5 stealth CME using coronagraphic and heliospheric images. We find clear evidence of a continuous rotation of the CME, i.e., an increase in the tilt angle, estimated using the graduated cylindrical shell (GCS) reconstruction at different heliocentric distances, up to 58 R ⊙. We find a further increase in the tilt at L1 estimated from the toroidal and cylindrical flux rope fitting on the in situ observations of interplanetary magnetic field (IMF) and solar wind parameters. This study highlights the importance of observations of Heliospheric Imager (HI), on board the Solar Terrestrial Relations Observatory. In particular, the GCS reconstruction of CMEs in the HI field of view promises to bridge the gap between the near-Sun and in situ observations at the L1. The changes in the CME tilt have significant implications for the space weather impact of stealth CMEs.