Odele Coddington | Searcharxiv

arXiv2025

Historical Reconstruction of Solar Surface Magnetism from Cycle 1-24 Using the Synthetic Active Region Generator (SARG) and the Advective Flux Transport (AFT) Model

The historical reconstruction of the Sun's surface magnetic field remains a persistent challenge, limiting our ability to investigate the long-term global properties of the Sun, including the evolution of the large-scale magnetic field, solar cycle prediction, reconstruction of total solar irradiance (TSI), and secular solar variability. In this study, we employ the Advective Flux Transport (AFT) model in conjunction with our newly developed Synthetic Active Region Generator (SARG) to construct a catalog of synthetic active regions (ARs) spanning Solar Cycles 1-24 (1755-2020). We use the SIDC/SILSO sunspot number version 2.0 as the sole input governing the properties of the synthetic ARs in this catalog. This SARG catalog is then incorporated into the AFT model, which simulates the emergence of new ARs on the Sun, which are then transported under the influence of surface flows to produce maps of the full-Sun radial photospheric magnetic field over the entire 265-year period. We modulate the active region tilt for each cycle in order to ensure that the polar fields are consistent with the solar cycle amplitudes. We find that the polar fields derived from these simulations exhibit excellent correlation (r > 0.8) with observational proxies, including polar faculae counts and Ca ii K polar network indices. Daily synchronic maps from these simulations for the entire 265-year period are made publicly available to support a wide range of applications beyond those presented in this work.

astro-ph.SR↗

arXiv2025

Matrix Factorization-Based Solar Spectral Irradiance Missing Data Imputation with Uncertainty Quantification

The solar spectral irradiance (SSI) depicts the spectral distribution of solar energy flux reaching the top of the Earth's atmosphere. Daily SSI measurements constitute a matrix with spectrally (rows) and temporally (columns) resolved solar energy flux measurements. The most recent SSI measurements have been made by NASA's Total and Spectral Solar Irradiance Sensor-1 (TSIS-1) Spectral Irradiance Monitor (SIM) since March 2018. This data has considerable missing data due to both random factors and instrument downtime, a periodic trend related to the Sun's cyclical magnetic activity, and varying degrees of correlation among the spectra, some approaching unity. We propose a low-rank matrix factorization method for SSI reconstruction that incorporates autoregressive temporal regularization, periodic spline detrending, and cross-spectral covariance information. The method is implemented as a two-stage procedure designed to address scattered missingness and extended downtime missingness, respectively, and is fitted using efficient alternating optimization algorithms. We further accompany the reconstructed SSI values with a distribution-free interval estimation procedure based on conformal prediction. Through synthetic experiments and real-data analyses, we compare this method with Gaussian process regression, linear time series smoothing, and existing matrix-completion approaches in terms of imputation accuracy, interval coverage, interval length, and computational efficiency. The results show that exploiting the periodic, temporal, and cross-spectral structure of SSI substantially improves reconstruction performance and yields calibrated uncertainty intervals, producing a reconstructed SSI data product suitable for downstream climate science studies.

stat.AP↗