Presentation is loading. Please wait.

Presentation is loading. Please wait.

Estimating Free Magnetic Energy from an HMI Magnetogram by Brian T. Welsch Space Sciences Lab, UC-Berkeley Several methods have been proposed to estimate.

Similar presentations


Presentation on theme: "Estimating Free Magnetic Energy from an HMI Magnetogram by Brian T. Welsch Space Sciences Lab, UC-Berkeley Several methods have been proposed to estimate."— Presentation transcript:

1 Estimating Free Magnetic Energy from an HMI Magnetogram by Brian T. Welsch Space Sciences Lab, UC-Berkeley Several methods have been proposed to estimate coronal free magnetic energy, U F, from magnetograms. Generally, each approach has significant shortcomings. Here, I present a half-baked idea to make a crude estimate, essentially using a dirty trick.

2 60 Sec. Review: Several methods have been used to estimate free energy, which powers flares & CMEs. [Extrap] Potential field, B (P) : actually assumes *no* free energy! – Still good for order-of-magnitude estimate (used in Emslie et al. 2012). – Viable for limb events. [Extrap] Linear, Force-Free Field (LFFF) from observed photosph. vector B (O) ; – currents extend to ∞, so energy = ∞ [Extrap] Non-Linear, Force-Free Field (NLFFF): – localized /finite free energy, but inconsistent with observed forces in photosph. Field – no data at limb; imprecise/ wrong in tests (Schrijver et al.) [Inject] Integrate Poynting flux: – initial energy unknown, so needs photosph. B (O) (t) for long ∆t; – no data at limb; imprecise/wrong in tests (Welsch et al. 2007) [Extrap+Inject] Evolve an initial “guess” for B(x,y,z,0) in time, using B (O) (x,y,0,t) – Difficult (and expensive) to do with MHD model – Can use “magnetofrictional” model, but dynamics are unphysical

3 Cheung & DeRosa (2012) have been running magnetogram-driven coronal models: inductive evolution mimics coronal memory.

4 Recently, we have been collaborating w/ Mark & Marc to supply photosph. electric fields to drive their code. Follows van Ballegooijen, Priest & Mackay (2000): vect. pot. A is evolved via ∂A/∂t = v × B – ηJ – guarantees ∇ ∙B = 0 ; relative helicity easy to calculate – Uses explicit 2nd-order time derivatives, – spatial discretization on a Yee (1966) grid By Faraday’s law, ∂B (O) /∂t at lower boundary determines ∇ × cE = - ∂A/∂t – Masha discussed deriving cE from ∂B (O) /∂t (see also Fisher et al. 2011, 2012); note: this specifies ∇ ∙ E, i.e., gauge! Energy in model arises from Poynting flux, S z =c(E × B (O) )/4π on bottom boundary (slide content courtesy G. Fisher et al.)

5 For AR 11158, the model field opened at the same time in the model sequence as in the observations. AR 11158 was on disk from c. 2011 Feb. 10 – 19 Model ran from Feb. 13 at 00:00 to Feb. 15 at 24:00 An X2.2 flare occurred on Feb. 15 at 01:45 Coincidence in time was probably due to flare- induced effects on HMI fields -- the model field was unstable to perturbation!

6 Hypothetical Evolution: Drive coronal model from init. pot. B (P), using E at model base, to match observed B (O) (x,y,0). Initial field has no free energy. Electric field E drives model’s photospheric B(x,y,0,t’) toward observed B (O) (x,y,0) supplies Poynting flux – This differs from Masha’s estimate of Poynting flux, which is derived from actual photospheric evolution Evolution ceases when B at model’s bottom boundary matches B (O) (x,y) observed at photosphere. Mikic & McClymont (1994) did this with an MHD code, and called it the “Evolutionary Method” Valori, Kliem, and Fuhrmann (2007) used a “magnetofrictional” code for this

7 Trick: Forget the coronal model! Just sum the Poynting flux implied by E needed to evolve B (P) (x,y,0) --> B (O) (x,y). Create a fictitious sequence of magnetogram fields, { B (P) (x,y), B 1 (x,y), B 2 (x,y), …, B i (x,y), … B (O) (x,y)} E that will evolve B i (x,y) to B i+1 (x,y) can then be estimated. Poynting flux can then be computed from ( E x B ) This approach requires only one magnetogram! – It also does not assume the photospheric field is force-free.

8 But it doesn’t work well: In tests with a known field (Low & Lou 1990), this approach only gets 1/6 th of free energy. Problem: the coronal field will “absorb” some of the imposed twist. Hence, to actually change model photospheric field, E must be applied for longer. This implies the Poynting flux is underestimated. The underestimate probably scales as ∆x/L, where ∆x is pix. size, and L is length scale of the coronal current system.

9 Aside: With real data, the estimated free energy is too small --- of order ∼ 10 31 erg, too small for a big CME.

10 Mismatch in twist between interior and corona implies twist will propagate between the two. This is what my approach does. Longcope & Welsch 2000 From sketch by Parker 1987 Corona Photosphere Recognizing this, McClymont et al. (1997) drive model in proportion to the discrepancy between model and observation. ---->

11 Conclusion: You (probably) can’t cheat --- you’ve actually got to do the coronal modeling. This is bad news for lazy people like me. But I still hold out (delusional?) hope that some similar “cheat” can exist.

12 Summary Available techniques for estimating magnetic free energy are lousy. - Assumptions that are unphysical, or in conflict with data are made. One promising approach is data-driven, time-dependent modeling of coronal fields. - This requires substantial effort by personnel and supercomputer time. A much simpler --- probably flawed! --- approach is to compute the Poynting flux for a hypothetical set of E fields. - These would evolve a potential magnetogram to the observed field. 12


Download ppt "Estimating Free Magnetic Energy from an HMI Magnetogram by Brian T. Welsch Space Sciences Lab, UC-Berkeley Several methods have been proposed to estimate."

Similar presentations


Ads by Google