Abstract From 1915 to 1985 the monitoring program of the Mount Wilson Observatory, one of the Observatories of the Carnegie Institution of Washington, has taken over 35,000 daily images (spectroheliograms) of the Sun in the chromospheric resonance line of CaiiK. This important database constitutes a unique resource for a variety of retrospective analyses of the state of solar magnetism on multidecadal timescales. These observations may also hold the key for untangling some of the mysteries behind the solar dynamo, which in turn could result in a better predictive capability for current dynamo models. We describe here a procedure to calibrate and rescale these images so that homogeneous Carrington synoptic maps can be derived for the whole period covered by these observations. Temporal variations in full-disk chromospheric activity clearly show the signature of the 11 yr solar cycle, but no evidence is found for a statistically significant north/south hemispheric asymmetry. Using a feature-tracking technique we were also able to obtain the average solar rotation profile. We find no indication of any detectable periodicity in the temporal behavior of the orthogonalized rotation rate coefficients, suggesting the global chromospheric dynamics has not changed during the 70 years investigated in this work. We found also no significant evidence in our analysis for a hemispheric asymmetry in rotation rates.
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Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Abstract Solar extreme-ultraviolet (EUV) irradiance plays a crucial role in heating the Earth’s ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time scales. Although significant effort has been spent studying short-term EUV variations from solar transient events, there is little work to explore the long-term evolution of the EUV flux over multiple solar cycles. Continuous EUV flux measurements have only been available since 1995, leaving significant gaps in earlier data. In this study, we propose a Bayesian deep learning model, named SEMNet, to fill the gaps. We validate our approach by applying SEMNet to construct Solar and Heliospheric Observatory/Solar EUV Monitor EUV flux measurements in the period between 1998 and 2014 using CaIIK images from the Precision Solar Photometric Telescope. We then extend SEMNet through transfer learning to reconstruct solar EUV irradiance in the period between 1950 and 1960 using CaIIK images from the Kodaikanal Solar Observatory. Experimental results show that SEMNet provides reliable predictions along with uncertainty bounds, demonstrating the feasibility of CaIIK images as a robust proxy for long-term EUV fluxes. These findings contribute to a better understanding of solar influences on Earth’s climate over extended periods.
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- PAR ID:
- 10675220
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- The Astrophysical Journal Supplement Series
- Volume:
- 280
- Issue:
- 2
- ISSN:
- 0067-0049
- Page Range / eLocation ID:
- 50
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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