Presentation on theme: "Forward Modeling from Simulations: Full-Sun and Active Regions Cooper Downs ISSI Workshop on Coronal Magnetism (2 nd Meeting), March 10 2014."— Presentation transcript:
Forward Modeling from Simulations: Full-Sun and Active Regions Cooper Downs ISSI Workshop on Coronal Magnetism (2 nd Meeting), March 10 2014
Outline -I’m hoping that we (PSI) can provide models/ideas that can support you with your coronal magnetometry interests. -I’ll talk briefly about the general types of MHD modeling that we do. -Show a couple of example models that we could use with FORWARD.
Why Forward Model Simulations? - Magnetic and thermal states of the corona are closely related. - Oftentimes the thermal structure strongly influences observables. (i.e. coronal line- emission / scattering). - We’d really like to be able to test our physical assumptions and interpretations of observations. - Even better we’d love to infer or ‘invert’ physical conditions from the measurements themselves. 3D Thermodynamic MHD simulations can help with these tasks by: - Forward modeling observables from simulation data. - Testing inversion methods using forward modeled data. - Q? Do we get the same answer back?
Corona is not Ideal! - Non-ideal terms dictate thermodynamic state in the low corona. - For the Transition region we add: - Electron heat conduction (due to high T, steep gradients). - Radiative losses. - Empirical term to encompass coronal heating: e.g. Unresolved Waves / Reconnection / resistive dissipation. - Turbulence based heating model is next. See Lionello 2009, and Downs 2010 for case-studies. Thermodynamic Energy EQ
Global Coronal Modeling Full-sun 3D Thermodynamic MHD simulations: - Driven by static or time-dependent magnetogram observations:
Global Coronal Modeling Full-sun 3D Thermodynamic MHD simulations: -Coronal Comparisons to EUV observables.
Active Region Modeling Localized Hi-Res MHD. -Freeze 3D NLFF solution -Solve for parallel plasma dynamics in time (to study coronal heating). Mok et al. 2008
CME/Flux-Rope Modeling Time-dependent Eruption Modeling -Insert or construct energized magnetic configuration. -Slowly drive the system towards eruption.
CME/Flux-Rope Modeling Time-Dependent Eruption Modeling -Thermal-Magnetic evolution can be connected to observables! -e.g. coronal dimmings:
Coronal Simulations from the Web Our website: http://www.predsci.com/hmi Thermodynamic runs from CR2096 to present are freely available for download 2 heating models to choose from (Density stratification and amount of opened up field differ slightly) I can provide the IDL routine to read and interpolate the simulation to a standard datacube. Even better, its compatible with FORWARD!
High Res Global Cases If you don’t like our website, we also have high res-runs for a few cases. - e.g. the July 2010 Eclipse, or the 2011 Comet Lovejoy perihelion. - We can run new ones as well! Fe XIII 1075 nm Stokes I (Intensity) Fe XIII 1075 nm Stokes L/I (total linear over intensity) AIA 193Å
MHD Field + MHD PlasmaMHD Field + Symmetric Plasma Test 1: Spherical Symmetry I
MHD Field + MHD PlasmaMHD Field + Symmetric Plasma Test 1: Spherical Symmetry L/I
POS MHD Field + MHD Plasma LOS integrated MHD Field + MHD Plasma Test 2: Plane of Sky Vs. Full Integration
PFSS + Symmetric Plasma LOS integrated MHD Field + MHD Plasma Test 3: MHD vs. PFSS
Active Region Model Yung Mok and collaborators at PSI have studied AR 7986 (August 1996) extensively (Mok et al. ‘05, ‘08, ‘14 in prep). -Current method is to freeze a NLFF state, and solve for the parallel plasma dynamics in time. -This gives time-dependent snapshots of loop heating and cooling cycles. -The time-dependent plasma state seems to agree well with observations. - (paper in-prep) but more importantly for us, it provides a high-res, strong field AR with a self-consistent temperature and density background.
Active Region Model Example Fe XIII 1075 nm from FORWARD L/IV/I I L
The Solar Atmosphere is inherently complex and 3D. –LOS effects need to at least be considered, particularly when studying specific events or complex geometric structures with density contrasts. Models and Observations can go hand in hand! –We can use them to interpret/understand the complexity / limitations of data. –We can use them to test inversion methods. Polarization measurements are rich in information content, and we have a range of simulations/tools at our disposal. Closing Words