Predicting the strength of solar cycle 24 using a flux‐transport dynamo‐based tool

By M. Dikpati, G. de Toma, P. Gilman. 2006

277 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/a160a13d7aaa80b329e67bcd0f45d9c19e3ee957

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Citations in the corpus

Simulating and Predicting Solar Cycles Using a Flux-Transport Dynamo (2006, methodology): “In this way, we keep both the peak flux and the average flux of the cycle the same as before the stretching or compression (see also the discussion in x 2 of Dikpati et al. 2006a).”
Flux-Transport Dynamos with Lorentz Force Feedback on Differential Rotation and Meridional Flow: Saturation Mechanism and Torsional Oscillations (2006, background): “…dynamos have proven to be successful for modeling the evolution of the large scale solar magnetic field (Wang & Sheeley 1991; Durney 1995; Choudhuri et al. 1995; Dikpati & Charbonneau 1999; Küker et al. 2001; Dikpati & Gilman 2001; Dikpati et al. 2004; Dikpati 2005; Dikpati et al. 2006).”
The Solar Cycle (2007, methodology): “Dikpati et al. (2006) predicted an amplitude for cycle 24 of 150 – 180 using a flux transport dynamo that included a rotation profile and a near surface meridional flow based on helioseismic observations.”
Solar Dynamo Theory (2014, methodology): “Dikpati et al. (2006) have developed a novel, dynamo-based prediction scheme in which a parameterization of the zonally averaged observed surface magnetic flux is used as a surface boundary condition to a modified Babcock-Leighton model (see also Dikpati & Gilman 2006).”
Exploring the Physical Basis of Solar Cycle Predictions: Flux Transport Dynamics and Persistence of Memory in Advection- versus Diffusion-dominated Solar Convection Zones (2007, methodology): “They suggest that poloidal fields from cycles n−3, n−2, and n−1 combine to produce the toroidal field of cycle n. Based on an assumed proxy for the solar poloidal fields (sunspot area), this leads them to predict that Cycle 24 will be about 50% stronger than Cycle 23 (Dikpati et al. 2006).”
The Solar Cycle (2010, background): “Dikpati et al. (2008b) noted that the effect is not seen for sunspot area data.”
Geomagnetic activity indicates large amplitude for sunspot cycle 24 (2006, methodology): “This prediction is very much in line with the predictions of a large cycle 24 by Hathaway and Wilson [2004] and by Dikpati et al. [2006] but in contrast to the prediction of a small cycle 24 by Svalgaard et al. [2005]. All four of these predictions are based on different methods. The prediction of a”
ON THE WEAKENING OF THE POLAR MAGNETIC FIELDS DURING SOLAR CYCLE 23 (2009, background): “…next cycle are generated by converting the pre-existing poloidal field into toroidal flux (Cameron & Schüssler 2007; Choudhuri et al. 2007; Dikpati 2008; Dikpati & Gilman 2006; Dikpati et al. 2006, 2008; Hathaway 2009; Schatten 2005; Svalgaard et al. 2005; Wang & Sheeley 2009; Yeates et al. 2008).”
Solar activity forecast with a dynamo model (2007, methodology): “This prediction is in agreement with the dynamo-based prediction of Choudhuri et al. (2007), but not with the prediction of Dikpati et al. (2006).”
Solar Cycle Prediction Using Precursors and Flux Transport Models (2006, methodology): “Input model according to Dikpati et al.(2006) The DDG model (Dikpati et al. 2006) is described in some more detail in Dikpati & Gilman (2006).”