Main Tasks
- Discuss the current state of the art in modeling AR coronae with different methods (i.e., “data- constrained” Optimization/MF and “data-driven” MF/MHD) and quantitative comparisons.
- Assess how methods perform on the noise-free (and artificially-noisy) synthetic vector magnetogram series from a publicly available 3D radiative MHD simulation of a solar eruption.
- Analyze models produced from the provided observational HMI time-series (Table 3 of proposal) and validate results against observations via synthesis of observables (“data-driven” MHD) or proxies (“data-constrained” Optimization/MF or “data-driven” MF) (see Table 1 of the proposal).
- Identify a new set of ARs (e.g., from Cycle 25, possibly Solar Orbiter data) to be modeled with all methods, which will be used for testing over the following year.
- All members agree on a timeline with specific milestones to ensure steady progress after the first meeting.
- Assign preliminary writing tasks to Team members for journal article(s).
Initial Modeling Tasks:
We kindly ask the modelers to produce models using the noise-free synthetic vector magnetogram series (right click to get the data). Also, at this stage it would be great if we could have at least one observational case, NOAA AR11158 (right click to get the data). But the obvious first priority is the noise-free synthetic vector magnetogram series.
We will have to ensure all models are produced using the same grid specifications or make mutually agreed compromises to allow for a fair comparison between models. Please join the Slack channel mentioned in our e-mails to have a group chat and quickly (within a day or two) determine a mutually agreed grid size.
Main Science Questions:
Q1: [Inter-comparison between different modeling approaches] To what extent the modeled 3D pre-eruptive structures and the energies from a “data-driven” code differ from those from “data-constrained” MF-relaxation or optimization NLFF models?
Q2: [Emphasis on data-driven models] What are the relative strengths and weaknesses of different “data-driven” methods and how can we quantify them?
Agenda
Day 1: April 25 2022 [full day]
Welcome and logistics [Georgios/Mike]
Introduction to ISSI [Mark Sargent from ISSI]
Science questions to be addressed and dataset selection & preparation [Georgios/Mike/Gherardo]
Data-inspired 3D MHD models [Matthias/Georgios/Mark/Shin/Feng/Chaowei]
Volumetric helicity, energy, topology calculations & comparisons [Gherardo/DeRosa/Georgios/Satoshi/Shin]
Day 2: April 26 2022 [full day]
NLFFF Data-constrained Grad-Rubin Method [Mike]
NLFFF Data-constrained Optimization Method [Thomas Wiegelmann/Julia Thalmann]
NLFFF Magnetofrictional relaxation data-constrained Method [Satoshi Inoue]
Q1 results [modelers]
Q1 discussion [group]
Day 3: April 27 2022 [full day]
[…continue from previous day]
Electric field inversion methods [Masha Kazachenko/Benoit/Mark Cheung/Duncan Mackay/Jens]
Magnetofrictional Data-driven Method [Mark Cheung/Marc DeRosa/Georgios]
Magnetofrictional Data-driven Method [Jens]
Magnetofrictional Data-driven Method [Duncan Mackay]
Q2 results [modelers]
Q2 discussion [group]
Group dinner [TBD]
Day 4: April 28 2022 [full day]
[…continue from previous day]
Data-driven 3D MHD Method [Feng Chen/Matthias Rempel]
Data-driven 3D MHD Method [Chaowei Jiang]
A study combining 3D MHD w/ Magnetofrictional Data-driven modeling [Andrei/Masha]
Day 5: April 29 2022 [half day]
[…continue discussions from previous day]
Timeline and additional runs needed from modelers [group]
Paper outline & assign preliminary writing tasks to Team members [Georgios/Mike]
Timeframe for next workshop [Georgios/Mike]
Adjourn