The Sunspot Drawing Collection of the National Solar Observatory at Sacramento Peak (1947–2004)

Carrasco, V. M. S.; Pevtsov, Alexei A.; Nogales, José M.; Vaquero, J. M.

doi:10.1007/s11207-020-01746-5

Cited by 4 publications

(4 citation statements)

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“…Figure 1a is a drawing from the Specola Solare Ticinese station in Locarno, Switzerland, showing the delineation of groups and counting of spots on 14 March 2000 near the maximum of Solar Cycle 23 (1996 -2008). Figure 1b shows a sunspot drawing taken on the same date, about 7 hours later, at the US National Solar Observatory at Sacramento Peak in Sunspot, New Mexico (Carrasco et al, 2021f). While the drawings exhibit significant similarities, there are differences in the total number of sunspots and groups.…”

Section: Extended Author Information Available On the Last Page Of Th...mentioning

confidence: 99%

“…The Astronomical Observatory of the Coimbra University published a catalog with sunspot observations (including G and S) from 1929 to 1941 (Lourenço et al, 2019). In addition, a dataset of sunspot drawings made at the Sacramento Peak Observatory (SPO) from 1947 -2004 has been recently digitized by Carrasco et al (2021f). This work is the first step for the publication of the complete SPO sunspot catalog that will include information on sunspot positions and areas.…”

Section: (F ) Modern Long-term Observersmentioning

confidence: 99%

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Recalibration of the Sunspot-Number: Status Report

et al. 2023

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We report progress on the ongoing recalibration of the Wolf sunspot number ($S_{\mathrm{N}}$ S N ) and group-sunspot number ($G_{\mathrm{N}}$ G N ) following the release of version 2.0 of $S_{\mathrm{N}}$ S N in 2015. This report constitutes both an update of the efforts reported in the 2016 Topical Issue of Solar Physics and a summary of work by the International Space Science Institute (ISSI) International Team formed in 2017 to develop optimal $S_{\mathrm{N}}$ S N and $G_{\mathrm{N}}$ G N reconstruction methods while continuing to expand the historical sunspot-number database. Significant progress has been made on the database side while more work is needed to bring the various proposed $S_{\mathrm{N}}$ S N and (primarily) $G_{\mathrm{N}}$ G N reconstruction methods closer to maturity, after which the new reconstructions (or combinations thereof) can be compared with (a) “benchmark” expectations for any normalization scheme (e.g., a general increase in observer normalization factors going back in time), and (b) independent proxy data series such as F10.7 and the daily range of variations of Earth’s undisturbed magnetic field. New versions of the underlying databases for $S_{\mathrm{N}}$ S N and $G_{\mathrm{N}}$ G N will shortly become available for years through 2022 and we anticipate the release of the next versions of these two time series in 2024.

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Section: Extended Author Information Available On the Last Page Of Th...mentioning

confidence: 99%

Section: (F ) Modern Long-term Observersmentioning

confidence: 99%

Recalibration of the Sunspot-Number: Status Report

et al. 2023

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“…As flares generally result from sunspots, sunspots are often used as markers for forecasting solar activity. Some of the contributors to the collected data are the National Solar Observatory (NSO) [11], ESAC Solar Observatory (Helios) [12], and the Wilcox Solar Observatory (WSO) [13].…”

Section: Related Workmentioning

confidence: 99%

Sunspot Occurrence Forecasting With Metaheuristic Optimized Recurrent Neural Networks

Devi V,

Mani,

Shaker

et al. 2023

Theor. Appl. Comput. Intell.

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Solar activity plays an important role when considering terrestrial communication. Solar activity plays an even more important role when working with communication systems relying on artificial satellites. Electromagnetic emissions, known as solar flares, can disrupt communications and damage important infrastructure. However, as powerful solar flares are often preceded by observable occurrences of sunspots, a robust system prognosis system can be leveraged to help improve chances of minimizing damage to infrastructure. This work explored the potential of recurrent neural networks coupled with an introduced modified metaheuristics algorithm to tackle the increasingly pressing challenge of forecasting solar activity based on historical data. Due to the heavy reliance of neural networks on proper parameter selections as well as adequate architectural structure, the introduced optimizer is leveraged for the optimization of these control parameters. The proposed approach is evaluated on a real-world dataset that is publicly available. The outcomes are compared to several well-established optimization metaheuristics, and the outcomes show great promise for tackling this increasingly important topic as we head into the peak of the current solar cycle.

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“…Examples of such observatories include Uccle Observatory (Clette et al, 2007), Locarno Specola Observatory (Clette et al, 2016; Cortesi et al, 2016), Royal Greenwich Observatory (Willis et al, 2013, Willis et al, 2016), Debrecen Observatory (Baranyi et al, 2016; Győri et al, 2017), Madrid Observatory (Aparicio et al, 2014, 2019), Mt. Wilson Observatory (Lefebvre et al, 2005; Pevtsov et al, 2019a), Sacramento Peak (Carrasco et al, 2021), and the National Astronomical Observatory of Japan (Nagasawa, 1967).…”

Section: Introductionmentioning

confidence: 99%