SHINE: Prediction of Coronal Mass Ejections and Interplanetary Magnetic Fields Using Advanced Artificial Intelligence Techniques
Project Summary
Understanding and predicting violent solar eruptions
and their effect on Earth is a strategic national priority,
as it affects the daily life of human beings,
including communication, transportation,
power supply systems, national defense, space travel, and more.
Due to increasing spatial and temporal resolution of
solar instrumentation, researchers are facing tremendous challenges
in analyzing massive amounts of space weather data,
especially for the operational near real-time utilization.
This interdisciplinary project advances artificial intelligence (AI)
based tools to forecast geoeffective coronal mass ejections (CMEs).
This project supports fundamental research
on advanced AI to forecast CMEs and their potential
to cause geomagnetic storms near 1 AU.
The objectives are to
(i) employ AI to predict whether a solar active region
will produce a CME and estimate its transit time,
mass, and kinetic energy,
and (ii) predict the orientation of magnetic clouds (MCs) near 1 AU,
based on real time solar observations.
Data will be used by graph neural networks (GNNs)
and ensemble learning methods to combine the GNNs
with other conventional AI techniques
to predict orientations of MCs.
The project utilizes data from NASA, NOAA,
and NSF observatories,
including the NSF-funded Global H-alpha Network
and Big Bear Solar Observatory.
The study will also utilize existing measurements
and model results, which will be augmented with
additional measurements derived from global coronal field maps
as well as non-linear force-free field modeling.
Support
This material is based upon work partly supported
by the United States National Science Foundation under grant
AGS-2300341
(2023-2027).
Any opinions, findings, and conclusions or recommendations expressed
in this material are those of the authors and do not necessarily
reflect the views of the National Science Foundation.
This support is greatly appreciated.