As modern society becomes increasingly dependent on technology, space weather events will have a farther-reaching impact than ever before. For nearly 10 years, NASA's Solar Dynamics Observatory (SDO) has continuously monitored the Sun, however, the SDO-era coincides with the weakest solar cycle of the last century: over the last 40 years, there have been nearly 500 X-class solar flares—around 10 times the number of events observed by SDO alone. It is also clear that there is no single observational survey with sufficient time coverage to enable an effective deep learning space weather forecasting application. Crucially, over the past 40 years, numerous observatories have monitored the Sun's magnetic field. However, cross calibrating magnetograms is a complex and non-trivial endeavour as the relationship between observed pixels is strongly affected by a wide range of systematics. Here we present a deep learning application that can convert magnetograms to a target survey while preserving the features and systematics of the target survey. We will first present our approach for upscaling and cross-calibrating images obtained by the Michelson Doppler Imager (MDI; on-board the Solar and Heliospheric Observatory, SOHO), to the resolution of the Helioseismic and Magnetic Imager (SDO/HMI). We will discuss the physics-based metrics, deep learning architectures, and the lessons learned along the way. This work was performed at NASA’s Frontier Development Laboratory (FDL), a public-private partnership to apply AI techniques to accelerate space science discovery and exploration.

AGU Fall Meeting
Multiscale Dynamics

Wright, P, Gitiaux, X, Jungbluth, A, Maloney, S.A, Shneider, C, & Muñoz-Jaramillo, A. (2019). Super-Resolution Maps of the Solar Magnetic Field Covering 40 Years of Space Weather Events.