Identification of photospheric activity features from SOHO/MDI data using the ASAP tool

Abstract

The variation of solar irradiance is one of the natural forcing mechanisms of the terrestrial climate. Hence, the time-dependent solar irradiance is an important input parameter for climate modelling. The solar surface magnetic field is a powerful proxy for solar irradiance reconstruction. The analyses of data obtained with the Michelson Doppler Imager (MDI) on board the SOHO mission are therefore useful for the identification of solar surface magnetic features to be used in solar irradiance reconstruction models. However, there is still a need for automated technologies that would enable the identification of solar activity features from large databases. To achieve this we present a series of enhanced segmentation algorithms developed to detect and calculate the area coverages of specific magnetic features from MDI intensitygrams and magnetograms. These algorithms are part of the Automated Solar Activity Prediction (ASAP) tool. The segmentation algorithms allow us to identify the areas on the solar disk covered by magnetic elements inside and outside boundaries of active regions. Depending on their contrast properties, magnetic features within an active region boundary are classified as sunspot umbra and penumbra, or faculae. Outside an active region boundary magnetic elements are identified as network. We present the detailed steps involved in the segmentation process and provide the area coverages of the segmented MDI intensitygrams and magnetograms. The feature segmentation was carried out on daily intensitygrams and magnetograms from April 21, 1996 to April 11, 2011. This offers an exciting opportunity to undertake further investigations that benefit from solar features segmentations, such as solar irradiance reconstruction, which we plan to investigate in the future.

Identification of Sunspots on SODISM Full-Disk Solar Images

“…The calculated filling factors for a feature reflect the fraction of the solar disk covered by the feature and assign reference synthetic spectra to it. [12] The filling factor is calculated as a function of radial position on the sun disk. The solar disk is divided by 11 concentric rings starting with an inner radius RI and an outer radius RO.…”

Automatic sunspots detection on SODISM solar images

“…The calculated filling factors for a feature reflect the fraction of the solar disk covered by the feature and the assignment of reference synthetic spectra to it has applications to modelling the Solar Spectral Irradiance [11]. The filling factor is calculated as a function of radial position on the sun disk.…”

Modeling total solar irradiance from PMOD composite using feed-forward neural networks

“…The fact that the TSI is on average higher near sunspot maximum implies that the influence of faculae and network is greater than that of sunspots (Fröhlich & Lean, 1998). Proxy-based models (see, Ashamari et al, 2015, for a recent review) try to answer the question: to what extent specific solar proxies, such as sunspot number, Mg II index, F10.7 radio flux, abundance of cosmogenic isotopes, spectral lines or specific wavelength bands in the observed solar spectra or solar intensitygrams, can be used to describe solar irradiance variability. This proxy-based empirical model is very successful in reproducing the observed TSI variations from 1978 to 2004, but it fails to explain the decrease observed since 2005 (Fröhlich, 2009).…”