About this Research Topic
The Sun is a magnetically active star with spatio-temporal variability, which can impact our terrestrial system. Various MHD models can simulate certain aspects of the evolution of the solar magnetic field on different time scales. Recently data-driven models as well as coupling data with models by means of data assimilation and information theoretic approach are being explored. The use of high-resolution and high-cadence datasets in conjunction with historical solar data, which are being accumulated till date will allow the derivation of a wealth of spatio-temporal patterns in solar activity on various time-scales, covering a few days through months to decades, hence provide reliable inputs for model-simulations, and also the magnetic activity features to be predicted by the models.
Direct measurement of spatially-resolved solar magnetic field started only from 1967. Observations, before that, captured different magnetic proxies such as sunspots, plages and faculae, or filaments and prominences. These features, found both in modern datasets and historical archives, form at different heights above the solar surface and help immensely in understanding the magnetic field strength and topology. Several studies have found great corroboration between long-term spatiotemporal evolution of such magnetic proxies and the evolution derived from current magnetic measurements. Careful treatment of historical datasets may thus provide invaluable information about the evolutionary pattern in solar magnetic fields in the past and help establishing its causal connection with space weather events.
It must be noted that modern solar image datasets (e.g. SoHO, SDO, STEREO, HINODE) are captured directly through CCDs whereas historical image archives are primarily available in the form of digitized or non-digitized photographic plates and hand-drawn suncharts or Carrington maps. The most prominent historical archives are namely, Kodaikanal Solar Observatory Multi-wavelength digitized archive (available from early 1900’s), Meudon hand-drawn Carrington Maps, McIntosh hand-drawn archive, Kanzelhöhe prominence archive, Greenwich Sunspot archive, and Kislovodsk archive. There are several studies focusing on one of these datasets. Several factors such as datagaps, inter-observer variability, data degradation and calibration problems restrict the unified treatment of such datasets. However, applying indirect and sophisticated approaches can help to unify these datasets, thereby stretching the study time much further in the past.
This Research topic calls for contributions on the following using modern data analysis/Image processing/Machine learning/Numerical Simulation approaches:
(1) Calibration of historical solar data to produce modern CCD equivalent
(2) Homogenization of solar data (time series or images) from multiple instruments to create a composite survey spanning much longer than a single instrument
(3) Detection and long-term cataloguing of solar features and transient events from modern/historical solar data
(4) Reconstruction of solar magnetic field (signed/unsigned) from historical datasets
(5) Analyzing spatiotemporal evolution of solar activity features on time-scales of a few days through months to decades and identifying connections to space weather, climate patterns
(5) Building/refining solar MHD or analogous models that can use long-term solar data through data-driving, data assimilation, information theory and machine learning
Direct measurement of spatially-resolved solar magnetic field started only from 1967. Observations, before that, captured different magnetic proxies such as sunspots, plages and faculae, or filaments and prominences. These features, found both in modern datasets and historical archives, form at different heights above the solar surface and help immensely in understanding the magnetic field strength and topology. Several studies have found great corroboration between long-term spatiotemporal evolution of such magnetic proxies and the evolution derived from current magnetic measurements. Careful treatment of historical datasets may thus provide invaluable information about the evolutionary pattern in solar magnetic fields in the past and help establishing its causal connection with space weather events.
It must be noted that modern solar image datasets (e.g. SoHO, SDO, STEREO, HINODE) are captured directly through CCDs whereas historical image archives are primarily available in the form of digitized or non-digitized photographic plates and hand-drawn suncharts or Carrington maps. The most prominent historical archives are namely, Kodaikanal Solar Observatory Multi-wavelength digitized archive (available from early 1900’s), Meudon hand-drawn Carrington Maps, McIntosh hand-drawn archive, Kanzelhöhe prominence archive, Greenwich Sunspot archive, and Kislovodsk archive. There are several studies focusing on one of these datasets. Several factors such as datagaps, inter-observer variability, data degradation and calibration problems restrict the unified treatment of such datasets. However, applying indirect and sophisticated approaches can help to unify these datasets, thereby stretching the study time much further in the past.
This Research topic calls for contributions on the following using modern data analysis/Image processing/Machine learning/Numerical Simulation approaches:
(1) Calibration of historical solar data to produce modern CCD equivalent
(2) Homogenization of solar data (time series or images) from multiple instruments to create a composite survey spanning much longer than a single instrument
(3) Detection and long-term cataloguing of solar features and transient events from modern/historical solar data
(4) Reconstruction of solar magnetic field (signed/unsigned) from historical datasets
(5) Analyzing spatiotemporal evolution of solar activity features on time-scales of a few days through months to decades and identifying connections to space weather, climate patterns
(5) Building/refining solar MHD or analogous models that can use long-term solar data through data-driving, data assimilation, information theory and machine learning
Keywords: Historical datasets, The Sun, Multiwavelength data, Image processing, Machine learning, Solar activity, Long-term studies
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