Solar Wind Modeling with the Alfvén Wave Solar atmosphere Model Driven by HMI-based Near-real-time Maps by the National Solar Observatory

Sachdeva, Nishtha; Manchester, W.; Соколов, И. В.; Huang, Zhenguang; Pevtsov, A. A.; Bertello, L.; Pevtsov, Alexei A.; Tóth, G.; Holst, B. van der; Henney, C. J.

doi:10.3847/1538-4357/acda87

Cited by 4 publications

(2 citation statements)

References 65 publications

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“…Jin et al (2022) assessed the influence of input magnetic maps on modeling the solar wind and concluded that it is important to consider the model uncertainty due to the imperfect magnetic field measurements. Sachdeva et al (2021Sachdeva et al ( , 2023 used AWSoM to simulate solar maximum conditions with different input magnetograms, and confirmed that the simulated solar wind at 1 au can be very different when different input magnetograms are used.…”

Section: Introductionmentioning

confidence: 75%

Solar Wind Driven from GONG Magnetograms in the Last Solar Cycle

Huang,

Tóth,

Sachdeva

et al. 2024

ApJ

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In a previous study, Huang et al. used the Alfvén Wave Solar atmosphere Model, one of the widely used solar wind models in the community, driven by ADAPT-GONG magnetograms to simulate the solar wind in the last solar cycle and found that the optimal Poynting flux parameter can be estimated from either the open field area or the average unsigned radial component of the magnetic field in the open field regions. It was also found that the average energy deposition rate (Poynting flux) in the open field regions is approximately constant. In the current study, we expand the previous work by using GONG magnetograms to simulate the solar wind for the same Carrington rotations and determine if the results are similar to the ones obtained with ADAPT-GONG magnetograms. Our results indicate that similar correlations can be obtained from the GONG maps. Moreover, we report that ADAPT-GONG magnetograms can consistently provide better comparisons with 1 au solar wind observations than GONG magnetograms, based on the best simulations selected by the minimum of the average curve distance for the solar wind speed and density.

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Section: Introductionmentioning

confidence: 75%

Solar Wind Driven from GONG Magnetograms in the Last Solar Cycle

Huang,

Tóth,

Sachdeva

et al. 2024

ApJ

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“…et al, 2021a, 2021bSachdeva et al, 2019). This effect becomes particularly pronounced during solar maxima, where the simulated results can vary with the specific input magnetograms utilized (Arge et al, 2024;Huang et al, 2024;Sachdeva et al, 2021Sachdeva et al, , 2023. Another factor could be the significant gradients in strong magnetic fields, which pose challenges for numerical methods in terms of convergence and non-physical deviations, complicating high-quality numerical simulations (Gressl et al, 2014).…”

Section: Experiments For Solar Maximummentioning

confidence: 99%

Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation

Zhao,

Feng

2024

Space Weather

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This paper constructs the structures of the solar corona (SC) using Fourier neural operators (FNO) based on solar photospheric magnetic field observation. The purpose is to learn the mapping between two infinite‐dimensional function spaces, which takes the photospheric magnetic field as input and the magnetohydrodynamic (MHD) solar wind plasma parameters as output, from a finite collection of input‐output pairs. The FNO‐SC model is established using MHD simulated results of 36 Carrington rotations (CRs) from 2008, 2009, and 2020. The performance of the FNO‐SC model is tested for 6 CRs during various phases of the solar activity such as descending, minimum, and ascending phases to generate the 3D structures of the SC. With the MHD simulations as references, the average structure similarity index measure (SSIM) value for the magnetic field topology from 1 to 3Rs is around 0.88. From 1 to 20Rs, the SSIM values for the number density and radial speed surpass 0.9. Relative to OMNI observations, the mean absolute percentage error for the radial speed generated from the FNO‐SC model does not exceed 0.25. These results indicate that the FNO‐SC model effectively captures the solar coronal structures typical of the periods investigated, by recovering the MHD simulations as well as the observations. The FNO‐SC model is further trained with enriched data from the maximum phase to assess the capability of modeling such a situation. The FNO‐SC model costs 48.7 s for a single CR prediction, and thus facilitates real‐time space weather forecasting.

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CORHEL-CME: An Interactive Tool For Modeling Solar Eruptions

Linker,

Torok,

Downs

et al. 2024

J. Phys.: Conf. Ser.

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Coronal Mass Ejections (CMEs) are immense eruptions of plasma and magnetic fields that are propelled outward from the Sun, sometimes with velocities greater than 2000 km/s. They are responsible for some of the most severe space weather at Earth, including geomagnetic storms and solar energetic particle (SEP) events. We have developed CORHEL-CME, an interactive tool that allows non-expert users to routinely model multiple CMEs in a realistic coronal and heliospheric environment. The tool features a web-based user interface that allows the user to select a time period of interest, and employs Regularized Biot-Savart Law (RBSL) flux ropes to create stable and unstable pre-eruptive configurations within a background global magnetic field. The properties of these configurations can first be explored in a zero-beta magnetohydrodynamic (MHD) model, followed by complete CME simulations in thermodynamic MHD, with propagation out to 1 AU. We describe design features of the interface and computations, including the innovations required to efficiently compute results on practical timescales with moderate computational resources. CORHEL-CME is now implemented at NASA's Community Coordinated Modeling Center (CCMC) using NASA Amazon Web Services (AWS). It will be available to the public in early 2024.

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Solar Wind Modeling with the Alfvén Wave Solar atmosphere Model Driven by HMI-based Near-real-time Maps by the National Solar Observatory

Cited by 4 publications

References 65 publications

Solar Wind Driven from GONG Magnetograms in the Last Solar Cycle

Solar Wind Driven from GONG Magnetograms in the Last Solar Cycle

Prediction of Solar Coronal Structures Using Fourier Neural Operators Based on the Solar Photospheric Magnetic Field Observation

CORHEL-CME: An Interactive Tool For Modeling Solar Eruptions

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