Progress Towards a Near-Real-Time Far-side Magnetic Flux Data Product
Abstract
The prediction of solar wind conditions is a critical aspect of space weather forecasting. Solar wind models are highly dependent on the global magnetic field at the solar surface as their inner boundary condition, and the lack of true knowledge of the global field in traditional synoptic maps is a significant problem plaguing space weather forecasting. Currently, only near-side magnetic field observations exist, but far-side magnetic field can be essential for accurate modeling of the Sun's coronal field and solar wind. Current methods and observations exist that estimate the far-side active region configuration (e.g., helioseismic imaging, flux transport models, far-side EUV observations); however, each of these methods has drawbacks. Our work combines existing techniques with machine-learning and statistical analysis to develop reliable, calibrated far-side magnetic-flux maps in near-real-time, using helioseismic far-side images that are solely dependent on near-side observations. We present an overview of this method and discuss two important progress milestones: 1) We have established a statistical relation between the far-side acoustic images from 7 years of SDO/HMI data and STEREO/EUVI 304Å data, allowing us to remove some systematics and improve the quality of the far-side acoustic images; 2) We have trained and tested a deep learning algorithm between SDO/AIA 304Å images and SDO/HMI magnetic flux, and applied it to the far-side STEREO/EUVI 304Å far-side observations to obtain the far-side magnetic flux.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMSH14B..08H
- Keywords:
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- 7599 General or miscellaneous;
- SOLAR PHYSICS;
- ASTROPHYSICS;
- AND ASTRONOMYDE: 7899 General or miscellaneous;
- SPACE PLASMA PHYSICSDE: 7999 General or miscellaneous;
- SPACE WEATHER