Comparison of sunspot area data bases

Baranyi, T.; Györi, L.; Ludmány, A.; Coffey, H. E.

doi:10.1046/j.1365-8711.2001.04195.x

Cited by 48 publications

(26 citation statements)

References 15 publications

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“…In late 1981 this procedure was changed to employ an overlay with a number of circles and ellipses with different areas. The sunspot areas reported by USAF/NOAA are significantly smaller than those from RGO (Fligge and Solanki, 1997;Baranyi et al, 2001;Hathaway et al, 2002;Balmaceda et al, 2009). Figure 7 shows the relationship between the USAF/NOAA sunspot areas and the International Sunspot Number.…”

Section: Sunspot Areasmentioning

confidence: 89%

The Solar Cycle

Hathaway

2010

Living Rev. Sol. Phys.

107

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The Solar Cycle is reviewed. The 11-year cycle of solar activity is characterized by the rise and fall in the numbers and surface area of sunspots. We examine a number of other solar activity indicators including the 10.7 cm radio flux, the total solar irradiance, the magnetic field, flares and coronal mass ejections, geomagnetic activity, galactic cosmic ray fluxes, and radioisotopes in tree rings and ice cores that vary in association with the sunspots. We examine the characteristics of individual solar cycles including their maxima and minima, cycle periods and amplitudes, cycle shape, and the nature of active latitudes, hemispheres, and longitudes. We examine long-term variability including the Maunder Minimum, the Gleissberg Cycle, and the Gnevyshev-Ohl Rule. Short-term variability includes the 154-day periodicity, quasi-biennial variations, and double peaked maxima. We conclude with an examination of prediction techniques for the solar cycle.

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Section: Sunspot Areasmentioning

confidence: 89%

The Solar Cycle

Hathaway

2010

Living Rev. Sol. Phys.

107

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“…There are large and potentially systematic errors in the tilt angle measurements, due to the difficulties in distinguishing the different groups, especially during the cycle maxima. The measured sunspot areas depend also on the instruments and procedures (Baranyi et al 2001;Foukal 2014).…”

Section: Implications For Solar Cycle Irregularities and Predictionmentioning

confidence: 99%

Flux Transport Dynamos: From Kinematics to Dynamics

et al. 2014

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Over the past several decades, Flux-Transport Dynamo (FTD) models have emerged as a popular paradigm for explaining the cyclic nature of solar magnetic activity. Their defining characteristic is the key role played by the mean meridional circulation in transporting magnetic flux and thereby regulating the cycle period. Most FTD models also incorporate the so-called Babcock-Leighton (BL) mechanism in which the mean poloidal field is produced by the emergence and subsequent dispersal of bipolar active regions. This feature is well grounded in solar observations and provides a means for assimilating observed surface flows and fields into the models in order to forecast future solar activity, to identify model biases, and to clarify the underlying physical processes. Furthermore, interpreting historical sunspot records within the context of FTD models can potentially provide insight into why cycle features such as amplitude and duration vary and what causes extreme events such as Grand Minima. Though they are generally robust in a modeling sense and make good contact with observed cycle features, FTD models rely on input physics that B.B. Karak et al. is only partially constrained by observation and that neglects the subtleties of convective transport, convective field generation, and nonlinear feedbacks. Here we review the formulation and application of FTD models and assess our current understanding of the input physics based largely on complementary 3D MHD simulations of solar convection, dynamo action, and flux emergence.

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“…Since no single observatory made such records over this whole interval of time, different data sets must be combined after an appropriate intercalibration. In this sense, several comparative studies have been carried out in order to get an appropriately cross‐calibrated sunspot area data set [see, e.g., Hoyt et al , 1983; Sivaraman et al , 1993; Fligge and Solanki , 1997; Baranyi et al , 2001; Foster , 2004, and references therein]. They pointed out that differences between data sets can arise because of random errors introduced by the personal bias of the observer, limited seeing conditions at the observation site, different amounts of scattered light, or the difference in the time when the observations were made.…”

Section: Introductionmentioning

confidence: 99%

“…They are related to the observing and measurement techniques and different data reduction methods. For example, areas measured from sunspot drawings are on average smaller than the ones measured from photographic plates [ Baranyi et al , 2001].…”

Section: Introductionmentioning

confidence: 99%

A homogeneous database of sunspot areas covering more than 130 years

et al. 2009

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[1] The historical record of sunspot areas is a valuable and widely used proxy of solar activity and variability. The Royal Greenwich Observatory regularly measured this and other parameters between 1874 and 1976. After that time records from a number of different observatories are available. These, however, show systematic differences and often have significant gaps. Our goal is to obtain a uniform and complete sunspot area time series by combining different data sets. A homogeneous composite of sunspot areas is essential for different applications in solar physics, among others for irradiance reconstructions. Data recorded simultaneously at different observatories are statistically compared in order to determine the intercalibration factors. Using these data we compile a complete and cross-calibrated time series. The Greenwich data set is used as a basis until 1976, the Russian data (a compilation of observations made at stations in the former USSR) are used between 1977 and 1985, and data compiled by the USAF network are used since 1986. Other data sets (Rome, Yunnan, and Catania) are used to fill up the remaining gaps. Using the final sunspot areas record the Photometric Sunspot Index is calculated. We also show that the use of uncalibrated sunspot areas data sets can seriously affect the estimate of irradiance variations. Our analysis implies that there is no basis for the claim that UV irradiance variations have a much smaller influence on climate than total solar irradiance variations.

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Comparison of sunspot area data bases

Cited by 48 publications

References 15 publications

The Solar Cycle

The Solar Cycle

Flux Transport Dynamos: From Kinematics to Dynamics

A homogeneous database of sunspot areas covering more than 130 years

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