ROSES ID: NNH22ZDA001N-LWS Selection Year: 2022
Program Element: Focused Science Topic
Principal Investigator: Thomas Berger
Affiliation(s): LMSAL
Project Member(s):
Hoeksema, J. Todd Co-I Stanford University
Zhao, Junwei Collaborator Stanford University
Vincent, Mark A Consultant MAVERICK ANALYSIS
Hess Webber, Shea A Co-I Stanford University
Thayer, Jeffrey P Co-I University Of Colorado, Boulder
Laskar, Fazlul I Co-I REGENTS OF THE UNIVERSITY OF COLORADO, THE
Summary:
We propose to develop a supervised non-linear Machine Learning (ML) regression model of far-side AR helioseismic signal to EUV irradiance measures using SDO/HMI far-side maps and SECCHI EUV images as training data. Front-side EUV image data from SDO/AIA will be correlated to both SECCHI EUV images and to F10.7 cm flux to create a chained regression from far-side helioseismic signal to EUV image characteristic to proxy irradiance values that can be used in current thermospheric satellite drag forecasting models. We will also investigate the impact of large ARs rotating onto the disk on thermospheric temperature and composition, correlating EUV signal increases with measurements from the NASA/Global Observations of the Limb and Disk (GOLD) mission, enabling the development of direct thermospheric impact predictions from far-side AR measurements. _x000D_