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Coronal mass ejections (CMEs) typically cause the strongest geomagnetic storms so a major focus of space weather research has been predicting the arrival time of CMEs. Most arrival time models fall into two categories: (1) drag-based models that integrate the drag force between a simplified CME structure and the background solar wind and (2) full magnetohydrodynamic (MHD) models. Drag-based models typically are much more computationally efficient than MHD models, allowing for ensemble modeling. While arrival time predictions have improved since the earliest attempts,both types of models currently have difficulty achieving mean absolute errors below 10 hours. Here we use a drag-based model ANTEATR to explore the sensitivity of arrival times to various input parameters. We consider CMEs of different strengths from average to extreme size, speed, and mass (kinetic energies between 9x10^29 and 6x10^32 erg). For each scale CME we vary the input parameters to reflect the current observational uncertainty in each and determine how accurately each must be known to achieve predictions that are accurate within 5 hours. We find that different scale CMEs are the most sensitive to different parameters. The transit time of average strength CMEs depends most strongly on the CME speed whereas an extreme strength CME is the most sensitive to the angular width. A precise CME direction is critical for impacts near the flanks, but not near the CME nose. We also show that the Drag Based Model has similar sensitivities, suggesting that these results are representative for all drag-based models.

How to cite: Kay, C.: Identifying Critical Input Parameters for Improving Drag-Based CME Arrival Time Predictions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-568, https://doi.org/10.5194/egusphere-egu2020-568, 2020.

Previous research has shown that the deflection of coronal mass ejections (CMEs) in interplanetary space, especially fast CMEs, is a common phenomenon. The deflection caused by the interaction with background solar wind is an important factor to determine whether CMEs could hit Earth or not. As the Sun rotates, there will be interactions between solar wind flows with different speeds. When faster solar wind runs into slower solar wind
ahead, it will form a compressive area corotating with the Sun, which is called a corotating interaction region (CIR). These compression regions always have a higher density than the common background solar wind. When interacting with CME, will this make a difference in the deflection process of CME? In this research, first, a three-dimensional (3D) flux-rope CME initialization model is established based on the graduated cylindrical shell (GCS)
model. Then this CME model is introduced into the background solar wind, which is obtained using a 3D IN (INterplanetary) -TVD-MHD model. The Carrington Rotation (CR) 2154 is selected as an example to simulate the propagation and deflection of fast CME when it interacts with background solar wind, especially with the CIR structure.

The simulation results show that: (1) the fast CME will deflect eastward when it propagates into the background solar wind without the CIR; (2) when the fast CME hits the CIR on its west side, it will also deflect eastward, and the deflection angle will increase compared with the situation without CIR.

How to cite: Shen, F., Liu, Y., and Yang, Y.: Numerical Simulation on the Propagation and Deflection of Fast Coronal Mass Ejections (CMEs) Interacting with a Corotating Interaction Region in Interplanetary Space, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1815, https://doi.org/10.5194/egusphere-egu2020-1815, 2020.

Near-Earth solar wind conditions, including disturbances generated by coronal mass ejections (CMEs), are routinely forecast using 3-dimensional, numerical magnetohydrodynamic (MHD) models of the heliosphere. The resulting forecast errors are largely the result of uncertainty in the near-Sun boundary conditions, rather than heliospheric model physics or numerics. Thus ensembles of heliospheric model runs with perturbed initial conditions are used to estimate forecast uncertainty. MHD heliospheric models are relatively cheap in computational terms, requiring tens of minutes to an hour to simulate CME propagation from the Sun to Earth. Thus such ensembles can be run operationally. However, ensemble size is typically limited to ~10¹-10², which may be inadequate to sample the relevant high-dimensional parameter space. Here, we describe a simplified solar wind model that can estimate CME arrival time in approximately 0.01 seconds on a modest desktop computer and thus enables significantly larger ensembles. It is a 1-dimensional, incompressible, hydrodynamic model, which has previously been used for the steady-state solar wind, but is here used in time-dependent form. This approach is shown to adequately emulate the MHD solutions to the same boundary conditions for both steady-state solar wind and CME-like disturbances. We suggest it could serve as a “surrogate” model for the full 3-dimensional MHD models. For example, ensembles of ~10⁵-10⁶ members can be used to identify regions of parameter space for more detailed investigation by the MHD models. Similarly, the simplicity of the model means it can be rewritten as an adjoint model, enabling variational data assimilation with MHD models without the need to alter their code. Model code is available as an Open Source download in the Python language.

How to cite: Owens, M.: Quantifying CME arrival time uncertainty with mega ensembles, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2648, https://doi.org/10.5194/egusphere-egu2020-2648, 2020.

We present a comprehensive study of all 39 superstorms (minimum Dst ≤ −250 nT) occurring from 1957 to present including analyzing their main phase developments, seasonal and solar cycle dependences, as well as their solar and interplanetary causes. We find that 87% of the superstorms have a multistep main phase development or are built upon preceding geomagnetic activities, and 90% of the superstorms occurred either near solar maximum or during the declining phase.  For the superstorm association with solar activities, 54% of the superstorms were associated with X‐class solar flares, 36% were associated with M‐class flares, and 5% with C‐class flares. All solar flares related to superstorms occurred in active regions, indicating the importance of active regions to superstorms. Most flares were located in the central meridian or slightly west of it as expected. For the interplanetary conditions leading to the development of the superstorm main phase, 95% of the 19 superstorms with available solar wind data are solely caused or partially caused by the sheath anti-sunward of an interplanetary coronal mass ejection (ICME), indicating the importance of the sheath structure in driving superstorms. For eight superstorms that have identifiable interplanetary shocks preceding the ICMEs, the shock normal angles were almost all quasi‐perpendicular. Larger shock normal angles statistically corresponded to greater superstorm intensities.

How to cite: Meng, X., Tsurutani, B., and Mannucci, A.: A Comprehensive Study of Superstorms from 1957 to present, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20900, https://doi.org/10.5194/egusphere-egu2020-20900, 2020.

The last decade has truly witnessed the rise of the machine age. The enormous expansion of technology that can generate and manipulate massive amounts of information has transformed all aspects of society. Missions such as SDO and MMS, and numerical models such as the Space Weather Modeling Framework (SWMF) are now routinely generating terabytes of science data, far beyond what can be analyzed directly by humans. Fortunately, concurrent with this explosion in information has come the development of powerful capabilities, such as machine learning (ML) and artificial intelligence (AI), that can retrieve revolutionary new understanding and utility from the massive data sets. 

SOLSTICE (Solar Storms and Terrestrial Impacts Center) is a recently selected NASA/NSF DRIVE Center. It will serve as the vanguard for developing and applying ML methods, which will then raise the capabilities of the entire community. We will combine next generation ML technology with our world-leading numerical models and the exquisite data from the space missions to make breakthrough advances in Heliophysics understanding and space weather capabilities, and then transition our technology to the CCMC for the benefit of all.

We use ML to attack Grand Challenge Problems that cover the major aspects of space weather science: (i) use interpretable deep learning models, archived solar observations and high-performance physics-based simulations to identify the onset mechanism of solar flares and coronal mass ejections; and (ii) use high-cadence observations and physics-based feature learning to predict solar storms many hours before eruption, training time-to-event models to predict event times and flare magnitudes using innovative machine learning techniques.

How to cite: Gombosi, T. and the SOLSTICE Team: SOLSTICE: Space Weather Modeling Meets Machine Learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5224, https://doi.org/10.5194/egusphere-egu2020-5224, 2020.

We present the solar wind forecast module to be implemented on the Sun – interplanetary space – Earth’s magnetosphere chain of the H2020 SafeSpace project. The wind modelling pipeline, developed at the IRAP, performs real-time robust simulations (forward modelling) of the physical processes that determine the state of the solar wind from the surface of the Sun up to the L1 point. The pipeline puts together different mature research models: determination of the background coronal magnetic field, computation of many individual solar wind acceleration profiles (1 to 90 solar radii), propagation across the heliosphere and formation of CIRs (up to 1 AU or more), estimation of synthetic diagnostics (white-light and EUV imaging, in-situ time-series) and comparison to observations and spacecraft measurements. Different magnotograms sources (WSO, SOLIS, GONG, ADAPT) can be combined, as well as coronal field reconstruction methods (PFSS, NLFFF), wind models (MULTI-VP), and heliospheric propagation models (CDPP/AMDA 1D MHD, ENLIL, EUHFORIA). We provide a web-based service that continuously supplies a full set of bulk physical parameters (wind speed, density, temperature, magnetic field, phase speeds) of the solar wind up to 6-7 days in advance, at a time cadence compatible with space weather applications.

How to cite: Pinto, R., Kieokaew, R., Lavraud, B., Génot, V., Bouchemit, M., Rouillard, A., Poedts, S., Bourdarie, S., and Daglis, Y.: Real time physics-based solar wind forecasts for SafeSpace, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15245, https://doi.org/10.5194/egusphere-egu2020-15245, 2020.

Solar proton events are comprised of energetic protons of solar and interplanetary origin. Such energetic particles are able to access the magnetosphere at various locations according to their cutoff rigidity. The specific properties of solar proton access are of great interest for space weather prediction purposes. Using Van Allen Probes/Relativistic Electron-Proton Telescope (REPT) 20-200 MeV proton data we examine four of the strongest solar proton events over the lifetime of the mission. We present evidence of the direct magnetospheric access of these energetic solar protons and find strong flux increases at L<4. Results indicate that small and sudden flux changes measured by ACE spacecraft sensors upstream of Earth are also seen in the near-equatorial inner magnetosphere. Using the East-West asymmetry of solar protons as a proxy for cutoffs we examine the highly dynamic cutoff rigidity. We find there is evidence for: (1) cutoff rigidity dependence on MLT; (2) suppressed cutoffs with rapid Dst changes; and (3) rapid evolution of cutoffs even during quiet magnetospheric conditions.

How to cite: Filwett, R., Jaynes, A., Baker, D., Kanekal, S., Blake, B., and Kress, B.: Energetic Solar Particle Access to the Near-Equatorial Inner Magnetosphere, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1642, https://doi.org/10.5194/egusphere-egu2020-1642, 2020.

A hyperbolic cell-centered finite volume solver (HCCFVS) is first proposed to obtain the potential magnetic field solutions prescribed by the solar observed magnetograms. By introducing solution gradients as additional unknowns and adding a pseudo-time derivative, HCCFVS transforms second-order Poisson equation into an equivalent first-order as well as pseudo-time-dependent hyperbolic system. Thus, instead of directly solving the second-order Poisson equation, HCCFVS obtains the solution to the Poisson equation by achieving the steady-state solution to this first-order hyperbolic system. The code is established in Fortran 90 with Message Passing Interface parallelization. To preliminarily demonstrate the effectiveness and accuracy of the code, two test cases with exact solutions are first performed. The numerical results show its second-order convergence. Then, we apply the code to the solar potential magnetic field problem that is often approximated analytically as an expansion of spherical harmonics. A comparison between the potential magnetic field solutions demonstrates the capability of our new HCCFVS to adequately handle the solar potential magnetic field problem, and thus it can be used as an alternative to the spherical harmonics approach. Furthermore, HCCFVS, like the spherical harmonics approach, can be used to provide the initial magnetic field for solar corona or solar wind magnetohydrodynamic (MHD) models. Using the potential magnetic field obtained by HCCFVS as input, the large-scale solar coronal structures during Carrington rotation (CR) 2098 have been studied. Meanwhile, HCCFVS automatically deals with the Poisson projection method to keep the magnetic field divergence-free constraint during the time-relaxation process of achieving the steady state. The numerical results show that the simulated corona captures main solar coronal features and the average relative magnetic field divergence error is maintained to be an acceptable level, which again displays the performance of HCCFVS.

How to cite: Feng, X.: Finite volume method for obtaining potential magnetic field solutions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4301, https://doi.org/10.5194/egusphere-egu2020-4301, 2020.

We introduce Helio4cast, an open source python package to provide real time solar wind predictions at the Sun-Earth L1 point, and to directly couple them to forecasts of the aurora oval, geomagnetically induced currents and further geomagnetic indices. We present its current status, using a combination of our PREDSTORM solar wind forecast and the real time modeling of the aurora with the OVATION model. The solar wind prediction is driven by data from either STEREO-A, a recurrence model, an empirical background solar wind model or a future L5 mission. For coronal mass ejections (CMEs), we plan to use our semi-empirical 3DCORE model to produce in situ magnetic flux rope signatures constrained by real-time solar observations, or a machine learning approach based on many previous observations of in situ CMEs. We are particularly interested in how the errors in the solar wind prediction propagate to ground-based observations. Challenges and future plans of the real-time implementation are discussed.

How to cite: Möstl, C., Bailey, R. L., Amerstorfer, U. V., Amerstorfer, T., Weiss, A. J., Reiss, M. A., Hinterreiter, J., and Bauer, M.: Helio4Cast - a real time test environment to enhance space weather prediction at Earth, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5202, https://doi.org/10.5194/egusphere-egu2020-5202, 2020.

How to cite: Bauer, M., Amerstorfer, T., Hinterreiter, J., Möstl, C., Davies, J. A., Amerstorfer, U. V., Bailey, R. L., Reiss, M. A., and Weiss, A. J.: Using STEREO-HI beacon data to predict CME arrival time and speed with the ELEvoHI model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5247, https://doi.org/10.5194/egusphere-egu2020-5247, 2020.

Forecasting of coronal mass ejection magnetic flux rope fields at L1 is a long-standing challenge and one of the major problems in space weather forecasting. We attempt to make progress by using two approaches: 1) machine learning approaches (e.g., linear regression, lars lasso, RANSAC, or random forest), and 2) analogue ensemble methods. For our study, we take events observed at the Wind, Stereo-A and Stereo-B satellites from the ICME list created within the EU-project HELCATS. We analyse different scores (e.g., RMSE, or the skill of the model) of the presented methods. Further, we investigate how well the flux rope field can be anticipated when the first few hours of the flux rope have already been observed at L1.

How to cite: Amerstorfer, U., Möstl, C., Bailey, R., Weiss, A., Reiss, M., Amerstorfer, T., Hinterreiter, J., and Bauer, M.: Predicting the magnetic flux rope fields at the Sun-Earth L1 point, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6921, https://doi.org/10.5194/egusphere-egu2020-6921, 2020.

One of the very common in-situ signatures of ICMEs, as well as other interplanetary transients are Forbush decreases (FDs), i.e. short-term reductions in the galactic cosmic ray (GCR) flux. A two-step FD is often regarded as a textbook example, which presumably owns its specific morphology to the fact that the measuring instrument passed through the ICME head-on, encountering first the shock front (if developed), then the sheath and finally the magnetic structure. The interaction of GCRs and the shock/sheath region as well as CME magnetic structure occurs all the way from Sun to Earth, therefore, FDs are expected to reflect the evolutionary properties of CMEs and their sheaths. We apply modelling to different ICME regions in order to obtain a generic two-step FD profile, which qualitatively agrees with our current observation-based understanding of FDs. We next adapt the models for energy dependence to enable comparison with different GCR measurement instruments (as they measure in different particle energy ranges). We test these modelling efforts against a set of multi-spacecraft observations of the same event.

How to cite: Dumbovic, M., Vrsnak, B., Guo, J., Heber, B., Dissauer, K., Carcaboso-Morales, F., Temmer, M., Veronig, A., Podladchikova, T., Möstl, C., Amerstorfer, T., and Kirin, A.: CME evolution and the corresponding Forbush decrease: modelling vs multi-spacecraft observation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10446, https://doi.org/10.5194/egusphere-egu2020-10446, 2020.

Forbush decreases are depressions in the galactic cosmic rays (GCRs) which are mostly caused by the modulations of interplanetary coronal mass ejections (ICMEs) and also sometimes by stream/corotating interaction regions (SIRs/CIRs). Forbush decreases have been studied extensively using neutron monitors at Earth and have been recently, for the first time, measured on the surface of another planet - Mars by the Radiation Assessment Detector (RAD), on board Mars Science Laboratory’s (MSL) rover Curiosity. The modulation of the GCR particles by heliospheric transients in space is energy-dependent and afterwards these particles are also interacting with the Martian atmosphere with the interaction process depending on the particle type and energy. In order to study the space weather environment near Mars using the ground-measured Forbush decreases, it is important to understand and quantify the energy-dependent modulation of the GCR particles by not only the pass-by heliospheric disturbances but also the Martian atmosphere. In this study, we develop a model which combines the heliospheric modulation of GCRs and the atmospheric modification of such modulated GCR spectra to quantify the amplitudes of the Forbush decreases at Mars: both on ground and in the interplanetary space near Mars during the pass-by of an ICME/SIR. The modeled results are in good agreement when compared to studies of Forbush decreases caused by ICMEs/SIRs measured by MSL on the surface of Mars and by the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft in orbit.  This supports the validity of both the Forbush decrease description and the Martian atmospheric transport models.  Our model can be potentially used to understand the property of ICMEs and SIRs passing Mars.

How to cite: Guo, J., Wimmer-Schweingruber, R., Dumbovic, M., Heber, B., and Wang, Y.: A new model describing Forbush Decreases at Mars: combining the heliospheric modulation and the atmospheric influence, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12647, https://doi.org/10.5194/egusphere-egu2020-12647, 2020.

During the last years, we focused on developing a prediction tool that utilizes the wide-angle observations of STEREO's heliospheric imagers. The unsurpassable advantage of these imagers is the possibility to observe the evolution and propagation of a coronal mass ejection (CME) from close to the Sun up to 1 AU and beyond. We believe that using this advantage instead of relying on coronagraph observations that are limited to observe only 14% of the Sun-Earth line, it is possible to improve today's CME arrival time predictions.
The ELlipse Evolution model based on HI observations (ELEvoHI) assumes an elliptic frontal shape within the ecliptic plane and allows the CME to adjust to the ambient solar wind speed, i.e. it is drag-based. ELEvoHI is used as an ensemble simulation by varying the CME frontal shape within given boundary values. The results include a frequency distrubution of predicted arrival time and arrival speed and an estimation of the arrival probability. ELEvoHI can be operated using several kinds of inputs. In this study we investigate 15 well-defined single CMEs when STEREO was around L4/5 between the end of 2009 and the beginning of 2011. Three different sources of input propagation directions (and shapes) are used together with three different sources of ambient solar wind speed and two different ways of defining the most appropriate fit to the HI data. The combination of these different approaches and inputs leads to 18 different model set-ups used to predict each of the 15 events in our list leading to 270 ELEvoHI ensemble predictions and all in all to almost 60000 runs. To identify the most suitable and most accurate model set-up to run ELEvoHI, we compare the predictions to the actual in situ arrival of the CMEs.
This model is specified for using data from future space weather missions carrying HIs located at L5 or L1 and can also directly be used together with STEREO-A near real-time HI beacon data to provide real-time CME arrival predictions during the next 7 years when STEREO-A is observing the Sun-Earth space.

How to cite: Amerstorfer, T., Hinterreiter, J., Reiss, M. A., Bauer, M., Möstl, C., Bailey, R. L., Weiss, A. J., Amerstorfer, U. V., Davies, J. A., and Harrison, R.: CME arrival prediction and its dependency on input data and model parameters, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4703, https://doi.org/10.5194/egusphere-egu2020-4703, 2020.

Understanding space weather driven by the solar activity is crucial as it can affect various human technologies, health as well as it can have important implications for the space environment near the Earth and the Earth’s atmosphere. In order to better asses space weather forecasts various empirical, drag-based and MHD models have been developed to predict the arrival time of CMEs. One of them is the analytical Drag-based Model (DBM) applying the equation of CME motion which is determined by the drag force from the background solar wind acting on the CME. DBM predictions depend on various initial parameters such as CME launch speed, background solar wind speed and empirically derived drag parameter as well CME’s angular half-width and longitude of CME source region for a DBM CME cone geometry. Since many of input parameters may be inaccurate or unreliable due to limited observations, the Drag-Based Ensemble Model (DBEM) was developed that considers the variability of model input parameters by making an ensemble of a number of different input parameters to calculate a distribution and significance of DBM results. DBM has the advantage of having very short computational time (< 0.01s) and DBEM ensemble runs with many thousand members can be performed within few seconds on a normal computer. Using such approach, DBEM can determine the most likely CME arrival times and speeds, quantify the prediction uncertainties and calculate the forecast confidence intervals. Recently, DBEM web interface was also integrated as one of the ESA Space Situational Awareness web portal space weather services (http://swe.ssa.esa.int/heliospheric-weather). We’ll present the recent DBEM developments together with the validation of its predictions using observations and other models as well as the input parameter sensitivity tests.

How to cite: Čalogović, J., Dumbović, M., Vršnak, B., Sudar, D., Temmer, M., and Veronig, A.: Predicting heliospheric propagation of CMEs with probabilistic Drag-Based Ensemble Model (DBEM), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9847, https://doi.org/10.5194/egusphere-egu2020-9847, 2020.

We present the project SPREAdFAST – Solar Particle Radiation Environment Analysis and Forecasting - Acceleration and Scattering Transport. This investigation fulfills a vital component of the space weather requirements of ESA’s Space Situational Awareness program by contributing to the capability to protect space assets from solar activity space radiation. It will allow for producing predictions of SEP fluxes at multiple locations in the inner heliosphere, by modelling their acceleration at Coronal Mass Ejections (CMEs) near the Sun, and their subsequent interplanetary transport using a physics-based, data-driven approach. The system prototype will incorporate results from our scientific investigations, the modification and linking of existing open source scientific software, and its adaptation to the goals of the proposed work. It will incorporate a chain of data-driven analytic and numerical models, for estimating: coronal magnetic fields; dynamics of large-scale coronal (CME-driven) shock waves; energetic particle acceleration; scatter-based (not simple ballistic), time-dependent SEP propagation in the heliosphere to specific time-dependent locations.

How to cite: Kozarev, K., Miteva, R., Dechev, M., and Zucca, P.: Development of a Physics-Based Prototype Model Chain for Solar Energetic Particle Acceleration and Transport Forecasting for the Inner Heliosphere, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17156, https://doi.org/10.5194/egusphere-egu2020-17156, 2020.

Estimating space weather parameters for the solar cycle 25, which has already started, is essential to anticipate the behavior of the near-Earth space environment. Artificial Neural Networks have in recent years become very widely used in several scientific fields owing to the advancement in computational power and the availability of big data. In this work, we take advantage of utilizing Recurrent Neural Network models in time-series analysis. We have developed and trained a Long-Short Term Memory (LSTM) model, in order to make long-term predictions of the hourly-averaged energetic proton fluxes at 1AU. We have used as input a combination of solar and interplanetary magnetic field indices (from the OMNI database) from the past four solar cycles and generated predictions of the solar energetic proton fluxes at three energies. So far, we found that the root-mean-square errors for the predictions over a three-month period were 0.0240, 0.0173, and 0.0309, respectively. We also found that the model underestimates the prediction at the highest energy band. We will extend the model architecture in order to estimate the future SEP fluxes over the whole solar cycle.

How to cite: Nedal, M. and Kozarev, K.: Estimating the SEP Flux for the Upcoming Solar Cycle 25 Using LSTM Network, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16131, https://doi.org/10.5194/egusphere-egu2020-16131, 2020.

ICME (Interplanetary Coronal Mass Ejection) are violent phenomena of solar activity that affect the whole heliosphere and the prediction of their impact on different solar system bodies is one of the primary goals of the planetary space weather forecasting. The travel time of an ICME from the Sun to the Earth can be computed through the Drag-Based Model (DBM), which is based on a simple equation of motion for the ICME defining its acceleration as a=-Γ(v-w)v-w, where a and v are the CME acceleration and speed, w is the ambient solar-wind speed and Γ is the so-called drag parameter (Vršnak et al., 2013).
In this framework, Γ depends on the ICME mass and cross-section, on the solar-wind density and, to a lesser degree, on other parameters. The typical working hypothesis for DBM implies that both Γ and w are constant far from the Sun. To run the codes, forecasters use empirical
input values for Γ and w, derived by pre-existent knowledge of solar-wind condition and by solving the “inverted problem” (where the ICME travel time is known and the unknowns are Γ and/or w). In
the 'Ensemble' approaches (Dumbovich et al., 2018; Napoletano et al. 2018), the uncertainty about the actual values of such inputs are rendered by Probability Distribution Functions (PDFs), accounting for the values variability and our lack of knowledge. Among those PDFs, that of Γ is poorly defined due to the relatively scarce statistics of recorded values. 

Employing a list of past ICME events, for which initial conditions when leaving the Sun and arrival conditions at the Earth are known, we employ a statistical approach to the Drag-Based Model to determine a measure of Γ and w for each case. This allows to obtain distributions for the model parameters on experimental basis and, more importantly, to test whether different conditions of relative velocity to the solar wind influence the value of the drag efficiency, as it must be expected for solid objects moving into an external fluid. In addition, we perform numerical simulations of a solid ICME-shaped structure moving into the solar-wind modelled as an external fluid. Outcomes from these simulations are compared with our experimental results, and thus employed to interpret them on physical basis.

How to cite: Napoletano, G., Foldes, R., Del Moro, D., Berrilli, F., Giovannelli, L., and Pietropaolo, E.: On the Drag parameter of ICME propagation models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21007, https://doi.org/10.5194/egusphere-egu2020-21007, 2020.