Spatial-temporal forecasting the sunspot diagram

Covas, Eurico

doi:10.1051/0004-6361/201629130

Cited by 14 publications

(30 citation statements)

References 146 publications

(143 reference statements)

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“…(See also Sarp et al 2018.) A most remarkable extension of these methods was presented by Covas (2017) who, instead of focusing on the time series of SSN only, considered the problem of extending the whole spatiotemporal data set of sunspot positions (butterfly diagram) into the future. Neural networks, discussed in more detail in Sect.…”

Section: Nonlinear Methodsmentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

200

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A review of solar cycle prediction methods and their performance is given, including early forecasts for Cycle 25. The review focuses on those aspects of the solar cycle prediction problem that have a bearing on dynamo theory. The scope of the review is further restricted to the issue of predicting the amplitude (and optionally the epoch) of an upcoming solar maximum no later than right after the start of the given cycle. Prediction methods form three main groups. Precursor methods rely on the value of some measure of solar activity or magnetism at a specified time to predict the amplitude of the following solar maximum. The choice of a good precursor often implies considerable physical insight: indeed, it has become increasingly clear that the transition from purely empirical precursors to model-based methods is continuous. Model-based approaches can be further divided into two groups: predictions based on surface flux transport models and on consistent dynamo models. The implicit assumption of precursor methods is that each numbered solar cycle is a consistent unit in itself, while solar activity seems to consist of a series of much less tightly intercorrelated individual cycles. Extrapolation methods, in contrast, are based on the premise that the physical process giving rise to the sunspot number record is statistically homogeneous, i.e., the mathematical regularities underlying its variations are the same at any point of time, and therefore it lends itself to analysis and forecasting by time series methods. In their overall performance during the course of the last few solar cycles, precursor methods have clearly been superior to extrapolation methods. One method that has This article is a revised version of

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Section: Nonlinear Methodsmentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

200

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“…Spatial EDM (sEDM), uses both spatial neighbours and temporal lags to leverage this spatial coupling. sEDM has been used in a variety of fields since its introduction (Ørstavik & Stark, 1998; Parlitz & Merkwirth, 2000) including forecasting coastline changes (Grimes et al., 2015) and sunspots (Covas, 2017; Covas & Benetos, 2019). Although sEDM seems like a promising avenue for forecasting in ecology, there is, to our knowledge, no ecological application of it, and it has not been compared to the method of concatenating data.…”

Section: Introductionmentioning

confidence: 99%

Leveraging spatial information to forecast nonlinear ecological dynamics

2020

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There has been a recent demand for forecasting in ecology, particularly in the field of ecosystem management. Empirical dynamic modelling (EDM), an equation‐free nonlinear forecasting method, is receiving growing attention, but it requires long time series to produce accurate predictions. Though most ecological time series are short, spatial replicates are often available. Here we explore how utilizing available spatial data can improve our ability to forecast ecological dynamics. There are several ways to incorporate spatial information into EDM and not all have been applied in ecology. We compare spatial EDM approaches used in ecology and physics and introduce a flexible Bayesian model that makes use of prior movement information. We test these methods on simulated data generated with three population dynamics models with varying levels of complexity, time series length, spatial symmetry and heterogeneity. Adding spatial data generally improves accuracy, though the best method depends on the spatial process. We applied the methods to empirical fisheries data, highlighting the complexity of real population dynamics. Leveraging spatial data is an effective way to overcome the problem of short ecological time series. Since the best forecasting method depends on the underlying dynamics, we suggest that users apply several in concert and that this may be useful in identifying spatial heterogeneity in dynamics.

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“…Spots on the Sun have been observed for four centuries. Sunspot coverage and the latitudinal drift of the sunspot distribution have been correlated with long-term solar magnetic cycles (Maunder 1904;Hathaway 2015;Covas 2017). Magnetic activity in a solartype interface dynamo is thought to be generated deep in the convec-E-mail: shelley.zaleski@usq.edu.au tive zone at the tachocline where rotation interacts with convective flows (Spruit 1997;Spuit 2011).…”

Section: Introductionmentioning

confidence: 99%

Differential rotation of Kepler-71 via transit photometry mapping of faculae and starspots

Zaleski

Válio

Marsden

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Knowledge of dynamo evolution in solar-type stars is limited by the difficulty of using active region monitoring to measure stellar differential rotation, a key probe of stellar dynamo physics. This paper addresses the problem by presenting the first ever measurement of stellar differential rotation for a main-sequence solar-type star using starspots and faculae to provide complementary information. Our analysis uses modelling of light curves of multiple exoplanet transits for the young solar-type star Kepler-71, utilizing archival data from the Kepler mission. We estimate the physical characteristics of starspots and faculae on Kepler-71 from the characteristic amplitude variations they produce in the transit light curves and measure differential rotation from derived longitudes. Despite the higher contrast of faculae than those in the Sun, the bright features on Kepler-71 have similar properties such as increasing contrast towards the limb and larger sizes than sunspots. Adopting a solar-type differential rotation profile (faster rotation at the equator than the poles), the results from both starspot and facula analysis indicate a rotational shear less than about 0.005 rad d −1 , or a relative differential rotation less than 2 per cent, and hence almost rigid rotation. This rotational shear contrasts with the strong rotational shear of zero-age main-sequence stars and the modest but significant shear of the modern-day Sun. Various explanations for the likely rigid rotation are considered.

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Spatial-temporal forecasting the sunspot diagram

Cited by 14 publications

References 146 publications

Solar cycle prediction

Solar cycle prediction

Leveraging spatial information to forecast nonlinear ecological dynamics

Differential rotation of Kepler-71 via transit photometry mapping of faculae and starspots

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