The Effects of Uncertainty in Initial CME Input Parameters on Deflection, Rotation, Bz, and Arrival Time Predictions

Kay, C.; Gopalswamy, N.

doi:10.1029/2018ja025780

Cited by 47 publications

(47 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, the uncertainty in each initial condition parameter is assumed to take the form of a Gaussian distribution centred on the best-guess value. Correctly specifying the width of the Gaussian (and thus the uncertainty in the parameter) requires significant further study (Kay and Gopalswamy, 2018). Here, we use arbitrary values to demonstrate the principle.…”

Section: Cme Arrival Time Sensitivitymentioning

confidence: 99%

“…A further level of abstraction is to assume V r is prescribed and solve only for CME propagation, such as with a the one-dimensional drag-based model (DBM: Vrsnak and Gopalswamy, 2002;Cargill, 2004). While this typically assumes a uniform solar wind and provides no feedback between the solar wind and CME, it is very efficient and can be run in large ensembles (Dumbovic et al, 2018;Kay and Gopalswamy, 2018), with 10 5 ensemble members requiring only a few seconds on a modest desktop computer.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Computationally Efficient, Time-Dependent Model of the Solar Wind for Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic Simulations

et al. 2020

View full text Add to dashboard Cite

Near-Earth solar-wind conditions, including disturbances generated by coronal mass ejections (CMEs), are routinely forecast using three-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 10 1 to 10 2 members, which may be inadequate to sample the relevant high-dimensional parameter space. Here, we describe a simplified solarwind 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 one-dimensional, incompressible, hydrodynamic model, which has previously been used for the steady-state solar wind, but it 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 three-dimensional MHD models. For example, ensembles of 10 5 to 10 6 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. The model code is available as an Open Source download in the Python language. B M. Owens 5 Met Office, Exeter, UK 43 Page 2 of 17 M. Owens et al.Figure 1 An example of a three-dimensional numerical MHD solution of the solar wind, using the MAS coronal model and the HelioMAS heliospheric model. The photospheric magnetic field for Carrington Rotation 1833 (spanning September 1990) was used, as it results in both fast and slow wind in the equatorial plane. (a) Latitude-time plot at Earth longitude of V r at the corona-heliosphere interface (30 R ) from MAS. (Note if the solar-wind structure is time stationary, this is an exact mirror image of a Carrington map.) The white line shows the Heliographic Equator. (b) The associated time series of V r at the sub-Earth point. (c) Latitude-time plot at Earth longitude of V r at Earth orbit (215 R ) from HelioMAS. (d) The associated time series of V r at Earth from HelioMAS (black) and HUXt (red). See text for details of the HelioMAS and HUXt models.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a lin...

show abstract

Section: Cme Arrival Time Sensitivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Computationally Efficient, Time-Dependent Model of the Solar Wind for Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic Simulations

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Table 1 shows the fitted GCS parameters. Figure 8 shows results of ForeCAT ensemble modeling (Kay and Gopalswamy, 2018) in comparison to the GCS FR parameters. The two top panels show the latitude of the CME axis and the two bottom panels show the longitude in Stonyhurst coordinates.…”

Section: Forecat and Gcsmentioning

confidence: 99%

CME–HSS Interaction and Characteristics Tracked from Sun to Earth

et al. 2019

Self Cite

View full text Add to dashboard Cite

In a thorough study, we investigate the origin of a remarkable plasma and magnetic field configuration observed in situ on June 22, 2011, near L1, which appears to be a magnetic ejecta (ME) and a shock signature engulfed by a solar wind high-speed stream (HSS). We identify the signatures as an Earth-directed coronal mass ejection (CME), associated with a C7.7 flare on June 21, 2011, and its interaction with a HSS, which emanates from a coronal hole (CH) close to the launch site of the CME. The results indicate that the major interaction between the CME and the HSS starts at a height of 1.3 R up to 3 R . Over that distance range, the CME undergoes a strong north-eastward deflection of at least 30 • due to the open magnetic field configuration of the CH. We perform a comprehensive analysis for the CME-HSS event using multi-viewpoint data (from the Solar TErrestrial RElations Observatories, the Solar and Heliospheric Observatory and the Solar Dynamics Observatory), and combined modeling efforts (nonlinear force-free field modeling, Graduated Cylindrical Shell CME modeling, and the Forecasting a CME's Altered Trajectory -ForeCAT model).

show abstract

“…10.1029/2019SW002382 Kay and Gopalswamy (2018) found optimal arrival times using ANTEATR with a drag coefficient of 0.8 but only considered six CMEs.…”

Section: Space Weathermentioning

confidence: 99%

Identifying Critical Input Parameters for Improving Drag‐Based CME Arrival Time Predictions

2020

Self Cite

View full text Add to dashboard Cite

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 models. Drag-based models typically are much more computationally efficient than magnetohydrodynamic 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 hr. Here we use a drag-based model ANTEATR (Another Type of Ensemble Arrival Time Results) 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 9 × 10 29 and 6 × 10 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 hr. 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.Plain Language Summary Large explosions of plasma and magnetic field known as coronal mass ejections (CMEs) frequently erupt from the solar atmosphere. When CMEs head toward Earth, they interact with the near-Earth plasma and magnetic field, affecting the "space weather." CMEs typically cause the strongest space weather effects, so a major focus has been predicting the time it takes for a CME to propagate from the Sun to the Earth. Many models have been developed over the past decades to predict the arrival time of CMEs, but all have difficulty achieving absolute errors less than 10 hr. Here we use a simple model that integrates the drag force between a CME and the background solar wind. Due to the model's simplicity, we can run a large number of simulations, allowing us to explore how the arrival time changes as the various model inputs are changed. We consider CMEs of different strengths and find that the behavior differs between average and extreme CMEs. We determine the precision needed for each input parameter to achieve predictions that are accurate within 5 hr. We compare our results with those from a similar model. Both models exhibit the same sensitivity to the input parameters, suggesting that these results are representative for most drag-based models.Coronal mass ejections (CMEs) are large explosions of plasma and structured magnetic field that routinely erupt from the solar ...

show abstract

The Effects of Uncertainty in Initial CME Input Parameters on Deflection, Rotation, B_z, and Arrival Time Predictions

Cited by 47 publications

References 56 publications

A Computationally Efficient, Time-Dependent Model of the Solar Wind for Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic Simulations

A Computationally Efficient, Time-Dependent Model of the Solar Wind for Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic Simulations

CME–HSS Interaction and Characteristics Tracked from Sun to Earth

Identifying Critical Input Parameters for Improving Drag‐Based CME Arrival Time Predictions

Contact Info

Product

Resources

About