Predicting Solar Cycle 24 With a Solar Dynamo Model

Choudhuri, Arnab Rai; Chatterjee, Piyali; Jiang, Jingyang

doi:10.1103/physrevlett.98.131103

Cited by 317 publications

(312 citation statements)

References 15 publications

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“…However it did not take long before another prediction was presented. Choudhuri et al (2007) claimed their model suggests a weak Cycle 24. The models differ in the number of solar cycles they need to make Cycle 24: Choudhuri et al (2007) suggested only one, but indicated several cycles.…”

Section: The Solar Dynamomentioning

confidence: 99%

Solar Weather Event Modelling and Prediction

et al. 2009

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Key drivers of solar weather and mid-term solar weather are reviewed by considering a selection of relevant physics-and statistics-based scientific models as well as a selection of related prediction models, in order to provide an updated operational scenario for space weather applications. The characteristics and outcomes of the considered scientific and prediction models indicate that they only partially cope with the complex nature of solar activity for the lack of a detailed knowledge of the underlying physics. This is indicated by the fact that, on one hand, scientific models based on chaos theory and non-linear dynamics reproduce better the observed features, and, on the other hand, that prediction models based on statistics and artificial neural networks perform better. To date, the solar weather prediction success at most time and spatial scales is far from being satisfactory, but the forthcoming ground-and space-based high-resolution observations can add fundamental tiles to the modelling and predicting frameworks as well as the application of advanced mathematical approaches in the analysis of diachronic solar observations, that are a must to provide comprehensive and homogeneous data sets.

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Section: The Solar Dynamomentioning

confidence: 99%

Solar Weather Event Modelling and Prediction

et al. 2009

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“…This is the first time that predictions in this category are available. The two most complete models (Dikpati, de Toma, and Gilman, 2006;Choudhuri, Chatterjee, and Jiang, 2007) predict high and low solar activity, respectively. Cameron and Schüssler (2007) discuss the progress and problems in using these models for predictions of solar activity.…”

Section: Dynamo Modelmentioning

confidence: 99%

Predictions of Solar Cycle 24

2008

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A summary and analysis of more than 50 predictions of the amplitude of the upcoming Solar Cycle 24 is presented. All of the predictions were published before solar minimum and represent our efforts to anticipate solar maximum at ever-earlier epochs. The consistency of the predictions within their assigned categories is discussed. Estimates of the significance of the predictions, compared to the climatological average, are presented.

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“…Most of the present day predictions are based on statistical analyses of solar activity in the past (Hathaway et al 1999;Hathaway 2009). A more physics-based approach is offered by models of the evolution of the Sun's magnetic field, although recent dynamo computations have given controversial results for cycle 24 (Dikpati & Gilman 2006;Choudhuri et al 2007;Jiang et al 2007). …”

Section: Introductionmentioning

confidence: 99%

Sunspot group tilt angles and the strength of the solar cycle

Dasi-Espuig¹,

Solanki

Krivova³

et al. 2010

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Context. It is well known that the tilt angles of active regions increase with their latitude (Joy's law). It has never been checked before, however, whether the average tilt angles change from one cycle to the next. Flux transport models show the importance of tilt angles for the reversal and build up of magnetic flux at the poles, which is in turn correlated to the strength of the next cycle. Aims. Here we analyse time series of tilt angle measurements and look for a possible relationship of the tilt angles with other solar cycle parameters, in order to glean information on the solar dynamo and to estimate their potential for predicting solar activity. Methods. We employed tilt angle data from Mount Wilson and Kodaikanal observatories covering solar cycles 15 to 21. We analyse the latitudinal distribution of the tilt angles (Joy's law), their variation from cycle to cycle, and their relationship to other solar cycle parameters, such as the strength (or total area covered by sunspots in a cycle), amplitude, and length. Results. The two main results of our analysis follow. 1. We find an anti-correlation between the mean normalised tilt angle of a given cycle and the strength (or amplitude) of that cycle, with a correlation coefficient of r c = −0.95 (99.9% confidence level) and r c = −0.93 (99.76% confidence level) for Mount Wilson and Kodaikanal data, respectively. 2. The product of the cycle's averaged tilt angle and the strength of the same cycle displays a significant correlation with the strength of the next cycle (r c = 0.65 at 89% confidence level and r c = 0.70 at 92% confidence level for Mount Wilson and Kodaikanal data, respectively). An even better correlation is obtained between the source term of the poloidal flux in Babcock-Leighton-type dynamos (which contains the tilt angle) and the amplitude of the next cycle. Further we confirm the linear relationship (Joy's law) between the tilt angle and latitude with slopes of 0.26 and 0.28 for Mount Wilson and Kodaikanal data, respectively. In addition, we obtain good positive correlations between the normalised-areaweighted tilt angle and the length of the following cycle, whereas the strength or the amplitude of the next cycle does not appear to be correlated to the tilt angles of the current cycle alone. Conclusions. The results of this study indicate that, in combination with the cycle strength, the active region tilt angles play an important role in building up the polar fields at cycle minimum.

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Predicting Solar Cycle 24 With a Solar Dynamo Model

Cited by 317 publications

References 15 publications

Solar Weather Event Modelling and Prediction

Solar Weather Event Modelling and Prediction

Predictions of Solar Cycle 24

Sunspot group tilt angles and the strength of the solar cycle

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