Open Access
Issue |
J. Space Weather Space Clim.
Volume 11, 2021
|
|
---|---|---|
Article Number | 9 | |
Number of page(s) | 16 | |
DOI | https://doi.org/10.1051/swsc/2020073 | |
Published online | 29 January 2021 |
- Abbett WP. 2007. The magnetic connection between the convection zone and corona in the Quiet Sun. Astrophys J 665: 1469–1488. https://doi.org/10.1086/519788. [NASA ADS] [CrossRef] [Google Scholar]
- Abbett WP, Fisher GH. 2010. Improving large-scale convection-zone-to-corona models. Mem Soc Astron Italiana 81: 721. [Google Scholar]
- Asensio Ramos A, Requerey IS, Vitas N. 2017. DeepVel: Deep learning for the estimation of horizontal velocities at the solar surface. A&A 604: A11. https://doi.org/10.1051/0004-6361/201730783. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Attie R, Innes DE. 2015. Magnetic balltracking: Tracking the photospheric magnetic flux. A&A 574: A106. https://doi.org/10.1051/0004-6361/201424552. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Bouttier F, Courtier P. 2002. Data assimilation concepts and methods. https://www.ecmwf.int/node/16928. [Google Scholar]
- Cheung MCM, Rempel M, Chintzoglou G, Chen F, Testa P, et al. 2018. A comprehensive three-dimensional radiative magnetohydrodynamic simulation of a solar flare. Nat Astron 3: 160–166. https://doi.org/10.1038/s41550-018-0629-3. [NASA ADS] [CrossRef] [Google Scholar]
- Cheung MCM, Schüssler M, Moreno-Insertis F. 2007. The origin of the reversed granulation in the solar photosphere. A&A 461: 1163–1171. https://doi.org/10.1051/0004-6361:20066390. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Chollet F. 2017. Deep learning with Python, chap. 1, 2, 5, 7, 1–50, 19–143, 260–263, 1st edn, Manning Publications Co., Greenwich, CT, USA. ISBN 1617294438, 9781617294433. [Google Scholar]
- Cranmer SR, van Ballegooijen AA, Edgar RJ. 2007. Self-consistent coronal heating and solar wind acceleration from anisotropic magnetohydrodynamic turbulence. Astrophys J Suppl Ser 171(2): 520–551. https://doi.org/10.1086/518001. [NASA ADS] [CrossRef] [Google Scholar]
- Fisher GH, Welsch BT. 2008. FLCT: A fast, Efficient method for performing local correlation tracking. In Subsurface and atmospheric influences on solar activity, Howe R, Komm RW, Balasubramaniam KS, Petrie GJD (Eds.), Vol. 383 of Astronomical Society of the Pacific Conference Series, Astronomical Society of the Pacific, San Francisco, CA, USA, 373 p. [Google Scholar]
- Fisher GH, Abbett WP, Bercik DJ, Kazachenko MD, Lynch BJ, et al. 2015. The Coronal Global Evolutionary Model: Using HMI vector magnetogram and doppler data to model the buildup of free magnetic energy in the solar corona. Space Weather 13: 369. https://doi.org/10.1002/2015SW001191. [NASA ADS] [CrossRef] [Google Scholar]
- Fisher GH, Kazachenko MD, Welsch BT, Sun X, Lumme E, Bercik DJ, DeRosa ML, Cheung MCM. 2020. The PDFI_SS electric field inversion software. Astrophys J Suppl Ser 248(1): 2. https://doi.org/10.3847/1538-4365/ab8303. [CrossRef] [Google Scholar]
- Hathaway DH, Upton LA. 2016. Predicting the amplitude and hemispheric asymmetry of solar cycle 25 with surface flux transport. J Geophys Res(Space Phys) 121(11): 10744–10753. https://doi.org/10.1002/2016JA023190. [CrossRef] [Google Scholar]
- Hayashi K, Feng X, Xiong M, Jiang C. 2018. An MHD simulation of solar active region 11158 driven with a time-dependent electric field determined from HMI vector magnetic field measurement data. Astrophys J 855: 11. https://doi.org/10.3847/1538-4357/aaacd8. [NASA ADS] [CrossRef] [Google Scholar]
- Hoeksema JT, Liu Y, Hayashi K, Sun X, Schou J, et al. 2014. The helioseismic and magnetic imager (HMI) vector magnetic field pipeline: Overview and performance. Sol Phys 289: 3483–3530. https://doi.org/10.1007/s11207-014-0516-8. [NASA ADS] [CrossRef] [Google Scholar]
- Jiang C, Wu ST, Feng X, Hu Q. 2016. Data-driven magnetohydrodynamic modelling of a flux-emerging active region leading to solar eruption. Nat Commun 7: 11522. https://doi.org/10.1038/ncomms11522. [NASA ADS] [CrossRef] [Google Scholar]
- Jiang J, Cameron RH, Schüssler M. 2015. The cause of the weak solar cycle 24. Astrophys J Lett 808(1): L28. https://doi.org/10.1088/2041-8205/808/1/L28. [Google Scholar]
- Jin X, Cai S, Li H, Karniadakis GE. 2020. NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations. J Comput Phys, 109951. ISSN 0021-9991. https://doi.org/10.1016/j.jcp.2020.109951. http://www.sciencedirect.com/science/article/pii/S0021999120307257. [Google Scholar]
- Kazachenko MD, Fisher GH, Welsch BT. 2014. A comprehensive method of estimating electric fields from vector magnetic field and doppler measurements. Astrophys J 795: 17. https://doi.org/10.1088/0004-637X/795/1/17. [NASA ADS] [CrossRef] [Google Scholar]
- Labonville F, Charbonneau P, Lemerle A. 2019. A dynamo-based forecast of solar cycle 25. Sol Phys 294(6): 82. https://doi.org/10.1007/s11207-019-1480-0. [CrossRef] [Google Scholar]
- Liu Y, Schuck PW. 2012. Magnetic energy and helicity in two emerging active regions in the Sun. Astrophys J 761(2): 105. https://doi.org/10.1088/0004-637X/761/2/105. [CrossRef] [Google Scholar]
- Longcope DW. 2004. Inferring a photospheric velocity field from a sequence of vector magnetograms: The minimum energy fit. Astrophys J 612: 1181–1192. https://doi.org/10.1086/422579. [NASA ADS] [CrossRef] [Google Scholar]
- Lumme E, Kazachenko MD, Fisher GH, Welsch BT, Pomoell J, Kilpua EKJ. 2019. Probing the effect of cadence on the estimates of photospheric energy and helicity injections in eruptive active region NOAA AR 11158. Sol Phys 294(6): 84. https://doi.org/10.1007/s11207-019-1475-x. [CrossRef] [Google Scholar]
- Nagy M, Lemerle A, Labonville F, Petrovay K, Charbonneau P. 2017. The effect of “Rogue” active regions on the solar cycle. Sol Phys 292(11): 167. https://doi.org/10.1007/s11207-017-1194-0. [NASA ADS] [CrossRef] [Google Scholar]
- November LJ, Simon GW. 1988. Precise proper-motion measurement of solar granulation. Astrophys J 333: 427–442. https://doi.org/10.1086/166758. [CrossRef] [Google Scholar]
- Parker EN. 1988. Nanoflares and the solar X-ray corona. Astrophys J 330: 474. https://doi.org/10.1086/166485. [NASA ADS] [CrossRef] [Google Scholar]
- Parnell CE, De Moortel I. 2012. A contemporary view of coronal heating. Roy Soc Lond Philos Trans Ser A 370: 3217–3240. https://doi.org/10.1098/rsta.2012.0113. [Google Scholar]
- Potts HE, Barrett RK, Diver DA. 2004. Balltracking: An highly efficient method for tracking flow fields. A&A 424: 253–262. https://doi.org/10.1051/0004-6361:20035891. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Raissi M, Perdikaris P, Karniadakis GE. 2019. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378: 686–707. https://doi.org/10.1016/j.jcp.2018.10.045. [CrossRef] [Google Scholar]
- Rempel M, Cheung MCM. 2014. Numerical simulations of active region scale flux emergence: From spot formation to decay. Astrophys J 785: 90. https://doi.org/10.1088/0004-637X/785/2/90. [NASA ADS] [CrossRef] [Google Scholar]
- Rieutord M, Roudier T, Roques S, Ducottet C. 2007. Tracking granules on the Sun’s surface and reconstructing velocity fields. I. The CST algorithm. A&A 471: 687–694. https://doi.org/10.1051/0004-6361:20066491. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Rimmele TR, Warner M, Keil SL, Goode PR, Knölker M, et al. 2020. The Daniel K. Inouye solar telescope – observatory overview. Sol Phys 295(12): 172. https://doi.org/0.1007/s11207-020-01736-7. https://ui.adsabs.harvard.edu/abs/2020SoPh..295..172R. Provided by the SAO/NASA Astrophysics Data System. [CrossRef] [Google Scholar]
- Ronneberger O, Fischer P, Brox T. 2015. U-Net: Convolutional networks for biomedical image segmentation. In: International conference on Medical image computing and computer-assisted intervention, Springer, Cham, pp. 234–241. [Google Scholar]
- Schou J, Scherrer PH, Bush RI, Wachter R, Couvidat S, et al. 2012. Design and ground calibration of the helioseismic and magnetic imager (HMI) instrument on the solar dynamics observatory (SDO). Sol Phys 275: 229–259. https://doi.org/10.1007/s11207-011-9842-2. [NASA ADS] [CrossRef] [Google Scholar]
- Schrijver CJ, De Rosa ML, Metcalf TR, Liu Y, McTiernan J, Régnier S, Valori G, Wheatland MS, Wiegelmann T. 2006. Nonlinear force-free modeling of coronal magnetic fields part I: A quantitative comparison of methods. Sol Phys 235: 161–190. https://doi.org/10.1007/s11207-006-0068-7. [NASA ADS] [CrossRef] [Google Scholar]
- Schuck PW. 2005. Local correlation tracking and the magnetic induction equation. Astrophys J Lett 632: L53–L56. https://doi.org/10.1086/497633. [CrossRef] [Google Scholar]
- Schuck PW. 2006. Tracking magnetic footpoints with the magnetic induction equation. Astrophys J 646: 1358–1391. https://doi.org/10.1086/505015. [CrossRef] [Google Scholar]
- Schuck PW. 2008. Tracking Vector magnetograms with the magnetic induction equation. Astrophys J 683: 1134–1152. https://doi.org/10.1086/589434. [CrossRef] [Google Scholar]
- Stein RF. 2012. Solar surface magneto-convection. Living Rev Sol Phys 9: 4. https://doi.org/10.12942/lrsp-2012-4. [CrossRef] [Google Scholar]
- Stein RF, Nordlund Å. 1998. Simulations of Solar Granulation. I. General Properties. Astrophys J 499: 914–933. https://doi.org/10.1086/305678. [NASA ADS] [CrossRef] [Google Scholar]
- Stein RF, Nordlund Å. 2012. On the formation of active regions. Astrophys J Lett 753: L13. https://doi.org/10.1088/2041-8205/753/1/L13. [NASA ADS] [CrossRef] [Google Scholar]
- Tremblay B, Attie R. 2020. Inferring plasma flows at granular and supergranular scales with a new architecture for the DeepVel neural network. Front Astron Space Sci 7: 25. https://doi.org/10.3389/fspas.2020.00025. [CrossRef] [Google Scholar]
- Tremblay B, Roudier T, Rieutord M, Vincent A. 2018. Reconstruction of horizontal plasma motions at the photosphere from intensitygrams: A comparison between DeepVel, LCT, FLCT, and CST. Sol Phys 293: 57. https://doi.org/10.1007/s11207-018-1276-7. [CrossRef] [Google Scholar]
- Tremblay B, Roudier T, Cossette J-F, Attié R, Rieutord M, Vincent A. 2019. Neural network to emulate numerical simulations of the Sun and infer synthetic observations for data assimilation. In: Solar Heliospheric and INterplanetary Environment (SHINE 2019), 30 p. https://shinecon.org/Publications.php. [Google Scholar]
- Vögler A, Shelyag S, Schüssler M, Cattaneo F, Emonet T, Linde T. 2005. Simulations of magneto-convection in the solar photosphere. Equations, methods, and results of the MURaM code. A&A 429: 335–351. https://doi.org/10.1051/0004-6361:20041507. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Wachter R, Schou J, Rabello-Soares MC, Miles JW, Duvall TL, Bush RI. 2012. Image quality of the helioseismic and magnetic imager (HMI) onboard the solar dynamics observatory (SDO). Sol Phys 275(1–2): 261–284. https://doi.org/10.1007/s11207-011-9709-6. [NASA ADS] [CrossRef] [Google Scholar]
- Warner M, Rimmele TR, Martinez Pillet V, Casini R, Berukoff S, et al. 2018. Construction update of the Daniel K. Inouye Solar Telescope project. In: Ground-based and Airborne Telescopes VII, Vol. 10700 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, SPIE, Bellingham, WA, USA, 107000V p. https://doi.org/10.1117/12.2314212. [Google Scholar]
- Welsch BT. 2006. Magnetic flux cancellation and coronal magnetic energy. Astrophys J 638(2): 1101–1109. https://doi.org/10.1086/498638. [NASA ADS] [CrossRef] [Google Scholar]
- Welsch BT. 2015. The photospheric Poynting flux and coronal heating. Publ Astron Soc Jpn 67(2): 18. https://doi.org/10.1093/pasj/psu151. [CrossRef] [Google Scholar]
- Yeates AR, Bianchi F, Welsch BT, Bushby PJ. 2014. The coronal energy input from magnetic braiding. A&A 564: A131. https://doi.org/10.1051/0004-6361/201323276. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.