Issue 
A&A
Volume 586, February 2016



Article Number  A42  
Number of page(s)  14  
Section  The Sun  
DOI  https://doi.org/10.1051/00046361/201527158  
Published online  25 January 2016 
Polarized radiative transfer in discontinuous media
^{1} Istituto Ricerche Solari Locarno (IRSOL), via Patocchi 57–Prato Pernice, 6605 LocarnoMonti, Switzerland
email: steiner@kis.unifreiburg.de, fzueger@irsol.ch; belluzzi@irsol.ch
^{2} KiepenheuerInstitut für Sonnenphysik (KIS), Schöneckstrasse 6, 79104 Freiburg, Germany
Received: 10 August 2015
Accepted: 2 November 2015
Context. Observations of the solar atmosphere of ever increasing spatial resolution reveal steep gradients in the magnetic field and in thermal states. Likewise, numerical simulations of the solar atmosphere show contact discontinuities and shock fronts. This asks for the development of robust methods for computing the radiative transfer of polarized light in discontinuous media.
Aims. Here, we propose a new concept for dealing with discontinuities in the radiative transfer of polarized light and carry out a few basic test calculations. While in the past, the focus was on interpolating the source function with everincreasing accuracy and smoothness, we propose to take the opposite approach by reconstructing it with piecewise continuous functions, taking discontinuities on purpose into account. This concept is known from computational fluid dynamics.
Methods. Test calculations were carried out for (i) a MilneEddington atmosphere; (ii) an atmosphere featuring a single discontinuity that is shifted across one grid cell; and (iii) a twolayered atmosphere with discontinuities in the source function, the velocity, and the magnetic field.
Results. It is shown that the method of piecewise continuous reconstruction is a viable approach to solving the radiative transfer equation for polarized light. In the special case where a discontinuity coincides with a computational cell interface, the method is capable of producing the exact solution. Overall, the assessment of the piecewise continuous reconstruction method turns out to be cautiously positive, but it does not lead to an orderofmagnitude improvement in accuracy over conventional methods for the examples considered here. More realistic model atmospheres need to be considered for judging practical applicability.
Key words: radiative transfer / polarization / methods: numerical / Sun: atmosphere / Sun: magnetic fields
© ESO, 2016
1. Introduction
The transfer and polarization of a beam of light traveling across an atmosphere is a problem that needs to be routinely solved in solar and stellar physics. The partial polarization of light in solar and stellar atmospheres is generated by magnetic fields, scattering processes, and atomic polarization, and it therefore bears a great deal of information about the physical state of the atmosphere from which the light emerges. In astrophysical research one can only remotely sense these atmospheres, which is why radiative transfer computations are an indispensable tool for recovering their physical state.
In general terms, the problem of the transfer of partially polarized light can be described by the following system of coupled firstorder, ordinary differential equations, (1)Here, K and S are, in the simplest case of local thermodynamic equilibrium (LTE), known functions of the coordinate s along the ray path. The intensity vector I = (I,Q,U,V)^{T} is termed as the Stokes vector, where I stands for the total intensity of the light beam, Q for the intensity difference of the linearly polarized light in two orthogonal axes in the plane perpendicular to the light beam, U for the corresponding intensity difference in the two axes rotated by 45° in the same plane, and V for the intensity difference between the righthanded and lefthanded circularly polarized light. Furthermore, S = (S_{I},S_{Q},S_{U},S_{V})^{T} is the source function for the four Stokes parameters and K the 4 × 4 propagation matrix that can be written in the form (see Landi Degl’Innocenti & Landolfi 2004, for a general derivation of the equations and the symmetry properties of K) (2)The coefficients η_{Q}, η_{U}, and η_{V} define the absorption with respect to the different polarization states of the light beam, ρ_{Q}, ρ_{U}, and ρ_{V} describe the coupling of the Stokes parameters owing to anomalous dispersion effects, while η_{I} is the usual absorption coefficient irrespective of the polarization state (Landi Degl’Innocenti & Landi Degl’Innocenti 1985; Landi Degl’Innocenti 1987). The seven independent coefficients that appear in the matrix K are functions of the wavelength and of other parameters describing the atmosphere. In the case of spectral lines formed in the presence of a magnetic field, these parameters depend, among others, on the strength and orientation of the magnetic field with respect to the ray of propagation. In the case of nonlocal thermodynamic equilibrium (nonLTE), the source function S and the propagation matrix K depend in a complicated manner on the Stokes vector I so that the equation system (1) becomes nonlinear. In this case, the radiative transfer Eq. (1) must be supplemented by rate equations describing the electron transition between atomic energy states. When atomic polarization is neglected and only the Zeeman effect is considered, the source function vector has a particularly simple form (see Sect. 4.1), and Eq. (1) is referred to as the UnnoRachkovsky equation (Unno 1956; Rachkovsky 1962a,b).
In this article, we consider the so called formal solution of Eq. (1) only for which S and K are supposed to be known. The formal solution is an essential part of iterative schemes for solving the nonlinear problem and is therefore of fundamental practical relevance. Section 2 reviews various methods of the formal solution for the transfer of partially polarized light. In Sect. 3, we then propose an alternative method for the formal solution in the case of a discontinuous medium and give details of it in Sect. 4. Section 5 presents a few examples and compares between different solution procedures. Summary, conclusions, and future plans are given in Sect. 6.
2. Formal solution
Generally, the formal solution of Eq. (1) can be written in terms of the evolution operator (or Stokes attenuation operator) O as (3)where O(s,s′) is a 4 × 4 matrix obeying the differential equation (4)and the limiting condition (5)where 1 is the 4 × 4 identity matrix. Equation (4) is as difficult to solve as Eq. (1), which is why early attempts to solve the radiative transfer for polarized light numerically was done by means of RungeKutta integration of Eq. (1) directly (Beckers 1969; Wittmann 1974; Landi Degl’Innocenti 1976). Alternatively, Eq. (1) can be recast to a secondorder differential equation for the sum of inward and outward propagating intensities (method of Feautrier 1964) and solved with a secondorder finite difference scheme (Auer et al. 1977; Rees et al. 1989).
In the following two sections, we take a closer look at two different analytical methods for solving the radiative transfer equation across an atmospheric slab of finite spatial and optical thickness.
2.1. Analytical form of the evolution operator
A particular case for which the evolution operator O(s,s′) for an atmospheric slab of finite thickness can be written in a closed analytical form is the one where the propagation matrix K is constant with s (or, more general K(s) = f(s)K′ with K′ = const.). Thus, K at a particular point s_{1} commutes with the same matrix at any other point s_{2}, so that we can expand (6)making use of the commutative property of K. Starting from here and decomposing K in terms of the unitary matrix and three complex and corresponding conjugate complex matrices, the evolution operator can finally be expressed in closed analytical form as was shown by Landi Degl’Innocenti & Landi Degl’Innocenti (1985) as (7)where x = (s−s′), M_{1} the unitary matrix, and M_{2}...M_{4} and Λ_{1,2} real 4 × 4 matrices and real scalars, respectively, consisting of combinations of the coefficients of the propagation matrix.
Since K = const. across the finite path from s_{1} to s_{2}, and since it commutes with the evolution operator O(s,s′), we can take it out of the integral so that Eq. (3) now reads (8)Subdividing the ray of propagation into a sequence of paths (short characteristics), each having constant but in general different propagation matrix K and integrable source transmission O(s_{2},s′) S(s′), we can thus integrate the transfer equation piece by piece analytically, using the emergent Stokes vector of the preceding path as the boundary condition of the subsequent path. We are going to make use of this solution strategy in Sect. 5.
2.2. From DELO to DELOPAR and BESSER
Currently, the most widely applied method for the numerical integration of the radiative transfer equation for polarized light is the “diagonal element lambda operator”, DELO (Rees et al. 1989), and variations of it. Noting that the diagonal elements of the propagation matrix are all identical, Eq. (1) can be written as (9)where dτ = −η_{I}ds, , and . The formal solution of Eq. (9) across the finite optical path from τ_{i + 1} to τ_{i} is then (10)where is called the effective source function and Δτ = τ_{i + 1}−τ_{i}. We notice that optical depths are measured in the opposite direction with respect to the ray path so that τ(i + 1) >τ(i). Thus, the evolution operator (commonly referred to as Λoperator in ordinary radiative transfer) of this pseudo formal solution derives only from the unity diagonal elements of the matrix K/η_{I}, hence the name DELO. All the rest, consisting of the offdiagonal elements, makes part of the effective source function. Since depends on I(τ), Eq. (10) really is an integral equation for I(τ), which reduces to the common formal solution in the absence of polarization when . To obtain an explicit formula for I(τ_{i}), Rees et al. (1989) linearly interpolated between τ_{i + 1} and τ_{i} so that (11)Then, Eq. (10) can be integrated analytically, yielding an explicit formula for I(τ_{i}). The crucial point here is that the effective source function is linearly interpolated and continuous at nodes i.
Trujillo Bueno (2003) realized that the DELO method can be improved by using parabolic interpolation between τ_{i−1}, τ_{i}, and τ_{i + 1} for the source function , while maintaining linear interpolation for . Both interpolants are continuous but not differentiable at the nodes τ_{i}. Trujillo Bueno (2003) called this improved version DELOPAR and stated that it was “substantially more accurate than the original DELO method”.
Because parabolic interpolation can be nonmonotonic between otherwise monotonic node values, Štěpán & Trujillo Bueno (2013) went even one step further (drawing from an idea of Auer 2003), using quadratic Bézier splines for (but still linear interpolation for ). In this case, the interpolant of the source function is strictly monotone for a monotonic sequence of node values and it can be made one times differentiable and it remains continuous as is the interpolant for . Štěpán & Trujillo Bueno (2013) called this method of integration enthusiastically BESSER (BEzier Spline SolvER, which is also German for better) and performed some accuracy tests proving the superiority of it.
De la Cruz Rodríguez & Piskunov (2013) used quadratic and cubic Bézier splines for the interpolation of the effective source function and provided detailed comparisons of the accuracy between these methods, the linear and parabolic DELO method, and the Hermitian interpolation method of Bellot Rubio et al. (1998). The latter method is not based on DELO but belongs to an own class of formal solution methods introduced to radiative transfer by Auer (1976) and extended to Stokes radiative transfer by Bellot Rubio et al. (1998). In the Hermitian method, the Stokes vector at s_{i} is obtained by Taylor expansion to fourthorder around s_{i−1}, where I(s_{i−1}) is known. Using its first and second derivative and the transfer Eq. (1), I(s_{i}) can then be expressed in terms of I(s_{i−1}) and the propagation matrix and source function and first derivatives of S and K, which both are obtained from quadratic interpolation of these quantities. De la Cruz Rodríguez & Piskunov (2013) concluded that the “DELOBezier schemes outperform all other algorithms when the density of depth points is low and the structure of the medium includes steep gradients”. A Bézier type interpolation for the formal solution of the polarized radiative transfer equation was also employed by Holzreuter & Solanki (2012). Steffen (1990) introduced an interpolation method based on thirdorder polynomials that produces a smooth and strictly monotonic interpolant with continuous firstorder derivatives. This method is applied for the interpolation of absorption coefficients in one of the radiative transfer modules (based on the Feautrier scheme and long characteristics) of the (magneto)hydrodynamics code CO^{5}BOLD (Freytag et al. 2012).
3. From BESSER back to simple
When DELO is good and BESSER better, what then is best? The answer to this question, we surmise, depends on the application. It is remarkable that De la Cruz Rodríguez & Piskunov (2013) also state that for very coarse grids with just one node per decade of optical depth “it is hard to beat the DELOlinear solution, which keeps the errors under control despite the strong gradients and poorly sampled stellar atmosphere”. Such poorly sampled model atmospheres are of practical relevance, for example for the Stokes inversion problem^{1}, for which it is difficult and expensive to deal with a large number of grid nodes. Other applications where loworder methods may be advisable include discontinuous atmospheres, which feature shock fronts and contact discontinuities. This is actually also the strategy pursued by shock capturing hydrodynamical schemes, which switch to loworder at locations of a discontinuity. Standard nonLTE radiative transfer codes notoriously show difficulties to converge when applied to the postprocessing of data from numerical simulations because of the steep gradients and discontinuities that occur in such atmospheres. In the following, we therefore propose a method specifically aimed at dealing with discontinuous atmospheres.
3.1. Learning from computational fluid dynamics
Very often in solar and stellar physics, one is confronted with the task of computing (synthesizing) spectral lines from model atmospheres that are the result of numerical simulations. Such atmospheres frequently show steep gradients, shock fronts, contact discontinuities, or intermittent peaks in temperature. Furthermore, it is important to realize that a physical quantity, q_{i}, from a finite volume numerical scheme represents the spatial average over the finite volume V_{i} = ΔxΔyΔz of the computational cell which q_{i} refers to, not the exact node value (see, e.g., Toro 2009; LeVeque 2002). This is in particular true for the conserved quantities: mass density, momentum density, and total energy density. Similarly, the numerical magnetic field components represent spatial averages over computational cell interfaces. Therefore, the reconstruction step of a hydrodynamical scheme comprises the interpolation of these quantities under the constraint of conservation, i.e., , where q is the reconstruction (interpolant) over volume V_{i}. While an interpolation function goes usually through its discrete node points, a reconstruction does not necessarily adopt the node value at the node location but assumes this value when integrated over the corresponding computational cell. This additional constraint may imply that the reconstruction is discontinuous at the cell boundaries.
The most basic reconstruction scheme, known under the name “Godunov scheme”, is the piecewise constant reconstruction. In this case q(x) = q_{i},x ∈ V_{i}. This implies that discontinuities occur at cell interfaces from one volume element to the neighboring one. In a continuous medium, these discontinuities are small, but they can become large at real discontinuities in the medium, which is indeed what we want to allow for. Yet, the piecewise constant reconstruction may be too inaccurate for representing the continuous, smooth parts of an atmosphere. The next higherorder reconstruction, still allowing for discontinuities, is the “van Leer scheme” (Van Leer 1979) consisting of the piecewise linear reconstruction. Thus, we write q = q_{i} + σ^{σ}σ(x−x_{i}),x ∈ V_{i}, where defines the slopes of the linear reconstruction in each coordinate direction and x_{i} refers to the volume center. Note that this reconstruction still obeys the conservation requirement and still allows for discontinuities at cell interfaces but leaves now the freedom of choosing the components of for obtaining a best reconstruction in parts where the atmosphere is continuous. Moreover, the slopes can be chosen, or limited, such that the reconstruction is monotonic, meaning that local extremes are avoided as was the purpose of the Bézierspline method. Moving one order of accuracy higher, one can use the piecewise parabolic reconstruction (PPM) of Colella & Woodward (1984), which still is conservative, preserves monotonicity, and allows for discontinuities.
Fig. 1
Definition of optical depth, ray path, and grid indices for cells, nodes, and cell interfaces. 

Open with DEXTER 
3.2. Slope limiters
There exists a large number of different types of slope limiters for the van Leer reconstruction scheme. We choose for our purpose symmetric limiters, i.e., limiters that do not differentiate between upwind or downwind direction. Given node values q_{i−1}, q_{i}, and q_{i + 1}, where q is a general expression for quantities like the source function, or the effective source function, or absorption coefficients, or optical depth, etc., we consider the local ray path from to , where is half way from τ_{i−1} to τ_{i}, and half way from τ_{i} to τ_{i + 1} (see Fig. 1). We then compute the slope σ_{i} for the reconstruction in the computational cell i by (12)where φ is the slope limiter. For φ ≡ 0, we recover the piecewise constant reconstruction. Given (13)which represents the ratio of successive gradients on the solution mesh, the minmod limiter of Roe (1986) is given by (14)It selects for the slope in cell i, σ_{i} = 0 whenever the grid function has a a local minimum or a local maximum at node i. In the case of a monotonic grid function, it selects either or , i.e., the slope with respect to the node value to the left or to the right, whichever of their moduli is smaller. This choice minimizes the total variation of the reconstruction q and keeps it locally monotone so that no local minima or maxima can occur. Another slope limiter that is going to be used in the following section is the Van Leer limiter (Van Leer 1974) given by (15)Again, this slope limiter becomes zero in case that the grid function possesses a local minimum or maximum but it allows for twice as large gradients as with the minmod limiter in the case when r is large. Additionally, we will use the superbee limiter of Roe (1986), given by (16)which, once again, allows for steeper gradients than both the minmod and the Van Leer limiter.
The behavior of the different slope limiters can be appreciated with the help of Fig. 2. Panel (a) shows an initial function that is piecewise linear and continuous, except of a jump at the interface from cell 4 to cell 5. The black dots indicate discrete node values (discrete grid function) representing the average of the initial function over the corresponding computational cell, thereby satisfying the requirement of conservation. The nodes are located in the center of each computational cell and constitute the only available data for the subsequent reconstructions of the initial function in panels (c)–(f). Panel (b) shows a simple linear interpolation between the discrete node values, which violates conservation. Panel (c), shows the piecewise constant reconstruction, which, obviously, satisfies conservation. Panel (d), (e), and (f) show the piecewise linear reconstruction using the minmod, Van Leer, and superbee slope limiters, respectively. As can be expected from the comments on Eqs. (14)–(16), slopes get increasingly steeper from panel (c)–(f) in the third to fifth computational cells. Superbee in panel (f) reproduces the slopes of the original function exactly in the third and the fifth cell, but this on the expense of monotonicity. In fact, already the Van Leer reconstruction shows at the interface from the third to the fourth cell nonmonotonic behavior, and this is accentuated by superbee in panel (f) at the same location. We note that for the piecewise constant reconstruction (panel c), a local change of a discrete value affects the corresponding local cell alone, while for all other reconstructions, it also changes the slopes in the next neighbor cells.
Fig. 2
a) Initial, piecewise linear function with a single discontinuity at the interface from cell 4 to cell 5. The black dots indicate its conservative discretization (discrete grid function); b) nonconservative, continuous linear interpolation of the discrete grid function. Linear extrapolation is applied in half of the first and half of the last computational cell. c) Piecewise constant reconstruction of the discrete grid function; d) piecewise linear reconstruction using the minmod slope limiter of Eq. (14); e) piecewise linear reconstruction using the Van Leer slope limiter of Eq. (15); f) piecewise linear reconstruction using the superbee slope limiter of Eq. (16). Panels b)–f) replicate the discrete grid function (black dots) from panel a). 

Open with DEXTER 
Figure 3 shows the function φ(r) for the three chosen limiters. The gray domain is the admissible codomain of φ(r), for which the total variation of the resulting reconstruction does not surpass the total variation of the original, discrete grid function. Its derivation and the definition of total variation and total variation diminishing (TVD) is given in the Appendix A. All limiters assume unity value for r = 1, which means that for a linear grid function (q_{i}−q_{i−1} = q_{i + 1}−q_{i}), the piecewise linear reconstruction assumes the same linearity as the grid function. Alone the minmod limiter is within the domain of total variation diminishing reconstruction and follows exactly the maximal admissible values. From this graph it also becomes clear that superbee is the most aggressive reconstruction allowing for steeper gradients than Van Leer, which in turn is more aggressive than minmod. For r< 0, φ(r) = 0 for all limiters, which reduces the method to piecewise constant reconstruction in the case of local maxima or minima in the grid function. We conjecture that smooth limiters, such as the Van Leer limiter for r> 0, may potentially be advantageous when applying them to problems where convergence is sought such as in iterative solution schemes for nonLTE and Stokes inversion problems, or when repetitively computing the formal solution as part of a hydrodynamic scheme.
4. Radiative transfer with piecewise continuous reconstruction
In the following, the methods of piecewise continuous reconstruction and slope limiters is applied to the source function. All other physical quantities, in particular the propagation matrix, are left either entirely constant or piecewise constant. We opt for this simplification because it keeps the assessment of the method transparent and permits a first proof of concept of the new approach.
Fig. 3
Three different slopelimiter functions: minmod (Eq. (14), solid curve), Van Leer (Eq. (15), dotdashed curve), and superbee (Eq. (16), dashed curve). The grey area indicates the admissible codomain of the function φ(r) for which the total variation of the resulting reconstruction does not surpass the total variation of the original, discrete grid function (see Appendix A). 

Open with DEXTER 
4.1. Transfer of polarized radiation in the Zeeman regime
We are interested in the radiative transfer at wavelengths of a magnetically sensitive spectral line and consider polarization introduced by the Zeeman effect alone. No scattering polarization or atomic polarization is taken into account. In this case, distinguishing between line and continuum processes, and assuming the continuum to be unpolarized, Eq. (1) can be written in the form (see Sect. 9.4 of Landi Degl’Innocenti & Landolfi 2004) (17)where S_{c} = (S_{c},0,0,0)^{T} and S_{L} = (S_{L},0,0,0)^{T} are the continuum and line source functions, respectively. We note that under the abovementioned assumptions both source functions are unpolarized. The quantity κ_{L} is the ratio between the frequencyintegrated line absorption coefficient, k_{L}, expressed in Doppler width units, and the continuum absorption coefficient, k_{c}, (18)where Δν_{D} is the Doppler width of the line (in frequency units). It can be observed that k_{L}/ Δν_{D} and k_{c} have the dimensions of the inverse of a length (true absorptions), while κ_{L} is a dimensionless quantity. The dimensionless propagation matrix H, due to line processes alone, is given by (19)where (20)and r_{Q}, r_{U}, and r_{V}, are computed analogously to h_{Q}, h_{U}, and h_{V} but with the absorption profiles η_{p}, η_{b}, and η_{r} replaced by the corresponding anomalous dispersion profiles ρ_{p}, ρ_{b}, and ρ_{r}, respectively (see, e.g., Eq. (9.32) of Landi Degl’Innocenti & Landolfi 2004).
Fig. 4
Angles θ and χ that specify the magnetic field direction with respect to the coordinate system ê_{1} and ê_{2} and the direction of the lineofsight s. Adapted from Landi Degl’Innocenti & Landolfi (2004). 

Open with DEXTER 
The angles θ and χ determine the orientation of the magnetic field with respect to the lineofsight as shown in Fig. 4. The quantities η_{q} with q = −1,0, + 1, corresponding to “b” (blue), “p” (parallel) and “r” (red), respectively, are the absorption profiles associated with the various Zeeman components. The “b” component (also referred to as σ_{b}) is characterized by ΔM = M_{u}−M_{ℓ} = 1, and is generally shifted in frequency to the blue side with respect to the unperturbed line, the “r” component (also referred to as σ_{r}) has ΔM = −1 and is generally shifted to the red, while the “p” component (also referred to as π) has ΔM = 0 and falls in between the σ_{b} and σ_{r} components. As usual, M_{u} and M_{ℓ} are the magnetic quantum numbers of the upper and lower sublevel, respectively. The explicit expressions of the profiles are with H and L the Voigt function and the associated dispersion profile, respectively, as given in Eq. (5.45) in Landi Degl’Innocenti & Landolfi (2004).
The reduced frequency, v, is defined as v = (ν_{0}−ν)/Δν_{D} where ν_{0} is the linecenter frequency and Δν_{D} the Doppler width of the line. Indicating with w_{T} the random velocity of the atoms owing to thermal and, eventually, micro turbulent motions, then Δν_{D} = ν_{0}w_{T}/c, where c is the speed of light. The quantity v_{A} is the normalized frequency shift due to a bulk motion of velocity w_{A} in the medium: v_{A} = w_{A}/w_{T} = ν_{0}w_{A}/ (cΔν_{D}). The quantity v_{B} is the normalized Zeeman splitting: v_{B} = ν_{L}/ Δν_{D}, with ν_{L} the Larmor frequency (ν_{L} = 1.3996 × 10^{6}B, where B expressed in G and ν_{L} in s^{1}) and g_{u} and g_{ℓ} are the Landé factors of the upper and lower sublevels, respectively, of the Zeeman components under consideration.
The parameter a is the damping constant taking line broadening (natural and collisional) into account. The quantity , is the relative strength of the Zeeman component q connecting the upper sublevel (J_{u}M_{u}) and the lower sublevel (J_{ℓ}M_{ℓ}), where J is the total angular momentum quantum number. Using Wigner 3j symbols, its explicit expression is given by (23)For the examples shown in Sect. 5, we consider a normal Zeeman triplet corresponding to a transition between an upper level with J_{u} = 1 and g_{u} = 1, and a lower level with J_{ℓ} = 0. From Eq. (23), it can be easily shown that in this case the relative strength is unity for all the three Zeeman components.
For our purpose, we now rewrite the transfer Eq. (17) using optical depth instead of geometrical depth and we assume that the atmosphere is in LTE so that S_{c} = S_{L} = S = (B,0,0,0)^{T}, where B is the Planck function. Then (24)where (25)Here is k_{c}(ν_{0}) the continuum absorptioncoefficient at the linecenter frequency ν_{0}.
With Eqs. (19)−(24) and the textual equations in between, the radiative transfer problem for a Zeeman triplet is well defined given the parameters κ_{L}, v_{B}, v_{A}, θ, χ, and a, and defining the source function B(τ_{c}) and boundary intensities. The values for the standard case used in the following section are κ_{L} = 20, v_{A} = 0, v_{B} = 1.5, θ = 60°, χ = 0, and a = 0.05, which are the identical ones that are used by Landi Degl’Innocenti & Landolfi (2004) in Chap. 9.
4.2. Construction of piecewise continuous and strictly continuous source functions
Next, we define a computational grid on the axis of continuum optical depth, τ_{c}. We choose two possibilities; (i) a grid equidistant in τ_{c}; and (ii) a grid equidistant in log (τ_{c}). Since stellar atmospheres are gravitationally stratified, case (ii) can be expected to approximately correspond to an equidistant grid in the space coordinate s for a lineofsight parallel to the gravitational acceleration. It then resembles the situation of computational grids in hydrodynamic simulations for stellar atmospheres, which often are equidistant in space.
For the definition of the source function values, we proceed as follows. First, we define an analytical expression for S(τ_{c}). Here, S denotes the normalized source function S(τ_{c}) = B(τ_{c}) /B_{0} so that we obtain intensities normalized to some reference value, B_{0}. For B_{0} ~ B(τ_{c} = 1), we then expect intensities on the order of 1. To get an idea, we choose (26)which is known as the MilneEddington model in conjunction with depthindependent constants κ_{L}, Δν_{D}, v_{B}, v_{A}, θ, χ, a and a semiinfinite atmosphere^{2}. In this case, Eq. (24) can be solved analytically (Rachkovsky 1962b), which circumstance we conveniently use for testing our program (see also Landi Degl’Innocenti & Landolfi 2004, Chap. 9.7 and 9.8). For the continuum intensity, the MilneEddington model produces I_{c}(τ_{c} = 0) = 1 + β. Another, more productive example for the present investigation is given by (27)which exhibits a discontinuity at continuum optical depth τ_{c} = τ_{d}, where the source function jumps from S_{1} to S_{2}. Despite that the source function is constant elsewhere, the jump alone gives rise to the production of a spectral line.
Having defined the analytical form of S(τ_{c}), we then map this function to the discrete node values at τ_{i}, by averaging over the corresponding computational cell according to (28)where Δτ = τ_{i + 1/2}−τ_{i−1/2} and where we use the index convention of Fig. 1. We then have a grid function similar to that presented to us in tables of stellar atmospheres, or produced by numerical simulations, or solved for in case of the inversion problem.
Having defined the discrete grid function S_{i}, we then reconstruct a continuous or piecewise continuous source function as was explained in Sect. 3, where we observe conservation of the source, (29)whenever possible. This condition is trivially fulfilled for the piecewise constant reconstruction (30)and it is also fulfilled for the piecewise linear reconstruction (31)provided that τ_{i} is halfway between and , independent of the value of the slope σ_{i} that can be freely chosen or determined by the selected slope limiter (see Sect. 3.2).
Condition (29) can be less easily satisfied for reconstructions that are both piecewise linear and strictly continuous (i.e., also continuous at the cell boundaries). Forcing the conservation condition for such reconstructions leads potentially to excessive variation of the reconstruction even when constrained to minimize it, or, the reconstruction may be ill defined in the sense that it strongly depends on the boundary values, which are normally least significant in the radiative transfer problem. For the present investigation, however, a strictly continuous interpolation is sought for comparison with the here proposed piecewise continuous reconstructions. A continuous interpolation is thought to approximately represent the DELO method in which the effective source function is linearly interpolated in between discrete node values. We choose two different continuous interpolations, both violating the conservation principle, Eq. (29). In a first method, we keep the variation least by simply connecting node values with linear interpolations. Thus, (32)which defines a piecewise linear continuous function. In the boundary cell 1, we extrapolate the linear source function of the interval [τ_{1},τ_{2}] to the interval [τ_{1−1/2},τ_{1}] and in the boundary cell N from [τ_{N−1},τ_{N}] to [τ_{N},τ_{N + 1/2}]. The propagation matrix κ_{L}H for the interval [τ_{i},τ_{i + 1}] is evaluated at τ_{i + 1/2}.
Second, we define sourcefunction values at the cell boundaries by taking the average of the corresponding cellcenter values, i.e., S(τ_{i + 1/2}) = 0.5(S_{i} + S_{i + 1}), and linearly interpolate between these values. Thus, we have (33)In this case, the propagation matrix in the interval is given by the data on node i. Equations (32) and (33) provide us with two continuous, piecewise linear interpolations, which can be considered rough equivalences to the DELO interpolation.
Having defined the reconstruction (or interpolation in case of Eqs. (32) and (33)), we solve the transfer Eq. (24) within one single computational cell using the analytical solution of Landi Degl’Innocenti & Landi Degl’Innocenti (1985) given by Eqs. (7) and (8), keeping all quantities κ_{L}, Δν_{D}, v_{B}, v_{A}, θ, χ, a constant within that cell. Thereby, the boundary Stokes vector (I(s_{1}) in Eq. (8)) is given by the solution in the preceding computational cell. In practice, we start at a sufficiently large optical depth τ_{N} with the outwardly directed boundary Stokes vector I(τ_{N + 1/2}), which is computed using the analytical UnnoRachkovsky solution for a semiinfinite atmosphere given by (34)where (35)Then, the analytical integration of the transfer equation is carried out over cell N, yielding I(τ_{N−1/2}), which is used for the boundary Stokes vector for cell N−1, and so on until cell 1 for which τ_{1−1/2} = 0 or some small value in case of a logarithmic τ scale.
Fig. 5
a) Source function S(τ_{c}) using any of Eqs. (31)−(33) (red, solid) and using the piecewise constant reconstruction of Eq. (30) (blue, dotted) as a function of continuum optical depth τ_{c}. The corresponding resulting Stokes profiles are given in panels b)−e) as functions of the reduced frequency v: (black, solid) for the source function of Eqs. (31)−(33) and (blue, dotted) for the piecewise constant reconstruction of Eq. (30). b) Stokes I; c) Stokes Q; d) Stokes U; e) Stokes V. 

Open with DEXTER 
Errors in the Stokes profiles of three different examples with different reconstructions of the source function.
5. Examples, validation and comparisons
We have created a simple GNU Octave program for solving Eq. (17) for the normal Zeeman triplet and the various reconstruction and interpolation schemes for arbitrary atmospheres that has served us for a comparison of the different methods.
As a first example, let us compute the solution for a MilneEddington atmosphere. We take the source function of Eq. (26) with β = 5 and compute on an equidistant grid in τ_{c} that extends from τ_{1−1/2} = 0 to τ_{N + 1/2} = 2 subdivided into N = 5 computational cells so that the optical depth changes by Δτ_{c} = 0.4 in each cell. Here, and in the following examples, we intentionally choose a very coarse grid to obtain perceivably different solutions for the different methods. The outwardly propagating, incident intensity at τ_{N + 1/2} = 2 is computed using the UnnoRachkovsky solution, Eq. (34). The propagation matrix is determined using the standard values for the parameters κ_{L}, v_{B}, v_{A}, θ, χ, and a given at the end of Sect. 4.1. In frequency space, we compute from v = (ν_{0}−ν)/Δν_{D} = −5 to 5 with a frequency resolution of Δv = 0.1.
All the linear schemes, i.e., the piecewise continuous linear reconstruction of Eq. (31) and the continuous linear interpolations of Eqs. (32), (33), reproduce this linear source function in τ_{c} exactly. In theses cases the numerical solution of the emerging Stokes vector at τ_{c} = 0 is identical to the analytical UnnoRachkovsky solution. We checked for any discrepancies and found that the agreement is to machine accuracy. Only with the piecewise constant reconstruction, one obtains, as expected, a substantial deviation from the analytical solution.
Figure 5 shows the comparison. Figure 5a shows in solid red the source function from any of the reconstructions or interpolations of Eqs. (31)−(33), which is identical to the MilneEddington source function S(τ_{c}) = 1 + 5τ_{c} and in dotted blue the piecewise constant reconstruction of Eq. (30). Panels (b)–(e) show the four Stokes parameters I, Q, U, and V as a function of reduced frequency v for the linear reconstruction and interpolations of Eqs. (31)–(33) (solid black curves) and for the piecewise constant reconstruction of Eq. (30) (dotted blue curves). It is worth mentioning that Stokes U would be identically zero in the absence of the magnetooptical effect because of χ = 0.
Fig. 6
Second example. Source function (first row) and Stokes I, Q, U, and V as a function of reduced frequency v (second to fifth row, respectively) at five different positions of the discontinuity, τ_{c} = 0.6, 0.675, 0.75, 0.825, and 0.9 shown in this sequence from the first column to the last column. Each panel shows the curves for the continuous interpolation according to Eq. (32) (light blue), the piecewise constant reconstruction according to Eq. (30) (blue), the piecewise linear reconstruction with minmod slope limiter according to Eqs. (31) and (14) (red), and the reference solution (orange, dashed). 

Open with DEXTER 
Recalling that we use a very coarse grid, it is remarkable that the differences are not dramatic, also not in the continuum. For the computation of the errors, we compute the maximal difference in the frequency range [ν_{0}−5,ν_{0} + 5] between the actual solution and the reference solution relative to the continuum intensity of the reference solution, thus (36)and correspondingly for δQ_{max}, δU_{max}, and δV_{max}. I_{ref} refers to a numerical solution obtained with a large number of grid cells, typically 1000. In the present case, it is identical to the analytical solution of UnnoRachkovsky. We also define the rms of the error, (37)where N_{ν} is the number of frequency points, and correspondingly for δQ_{rms}, δU_{rms}, and δV_{rms}, and the error in the continuum of Stokes I, (38)Table 1 gives the relative errors in percents according to Eqs. (36)–(38) for the different examples presented in this section. The top three rows refers to the first example, i.e., Fig. 5. From it we can see that the piecewise constant reconstruction leads to an error of 1% in the continuum intensity, and to a maximal error in Stokes I of 5.6%, while Q and V have maximal deviations of 3% and 2.5%, respectively. Because all linear reconstructions reproduce the Rachkovsky solution exactly, the first and third rows of Table 1 have zero errors.
In a second example, we consider a single discontinuity in the source function as given by Eq. (27), where we choose S_{1} = 0 and S_{2} = 5 and shift the discontinuity from τ_{d} = 0.6 to τ_{d} = 0.9 in steps of 0.075 across one grid cell of width Δτ_{c} = 0.3. At the same time, κ_{L} = 0.5 ahead of the discontinuity, i.e., for τ_{c}<τ_{d}, and κ_{L} = 20 behind the discontinuity, i.e., for τ_{c}>τ_{d}. With this choice, the spectral line and continuum are confined to form close to the discontinuity for obtaining maximal impact from it, not being attenuated or diluted by opacity and source ahead of it. All other parameter values are equal to those of the first example. Because of the small line opacity ahead of the discontinuity, the line depression of Stokes I is, with about 10% of the continuum intensity, weak, at the advantage that differences between the various reconstruction methods become the more visible. The computational domain in τ_{c} extends from τ_{1−1/2} = 0 to τ_{N + 1/2} = 1.5 subdivided into N = 5 computational cells so that the optical depth changes by Δτ_{c} = 0.3 in each cell. The outwardly propagating, incident intensity at τ_{N + 1/2} = 1.5 is equal to S_{2} = 5.
Fig. 7
Third example. Source function (first row) and Stokes I, Q, U, and V as a function of reduced frequency v (second to fifth row, respectively) at four different positions of the discontinuity, τ_{c} = 0.6, 0.7, 0.8, and 0.9 shown in this sequence from the first column to the last column. Each panel shows the curves for the continuous interpolation according to Eq. (32) (light blue), the piecewise constant reconstruction according to Eq. (30) (blue), the piecewise linear reconstruction with minmod slope limiter according to Eqs. (31) and (14) (red), and the reference solution (orange, dashed). 

Open with DEXTER 
Figure 6 shows in the first row in orange, dashed the original and, in solid, the reconstructed and the interpolated source functions as the discontinuity moves from τ_{c} = 0.6 to τ_{c} = 0.9 (from left to right) across one grid cell. Here, we recall that a discrete source function is derived from the original (orange, dashed) one with the help of Eq. (28), which then is the starting point for all reconstructed and interpolated (solid) source functions. This is also true for the line opacity κ_{L} for which we then always use the piecewise constant reconstruction to obtain a constant propagation matrix for each grid cell.
The second to fifth rows show the resulting StokesI to StokesV profiles, respectively. At τ_{c} = 0.6 (first column) and τ_{c} = 0.9 (rightmost column), the discontinuity of the original source function exactly coincides with a cell boundary. In these cases, the piecewise continuous reconstructions exactly reproduce the original source function and therefore also exactly match with the reference solution in all Stokes profiles. This is also reflected in Table 1, which lists vanishing errors at τ_{d} = 0.6 and τ_{d} = 0.9 for all the piecewise continuous reconstruction schemes. The continuous interpolation, on the other hand, shows substantial deviations from the reference solution in all Stokes parameters (light blue curves)^{3}.
When the discontinuity is located in the middle of the cell at τ_{c} = 0.75 (middle column), none of the reconstruction methods perform particularly well; the continuous interpolation (light blue curves) is slightly better than all the piecewise continuous reconstructions (blue and red curves). In between cell boundary and cell center at τ_{c} = 0.675 and τ_{c} = 0.825, the piecewise continuous reconstructions again outperform the continuous interpolation, where it is the piecewise constant reconstruction (blue curve) that performs best. This good performance of the piecewise constant reconstruction is certainly due to the fact that in this example, the original source function is constant everywhere with exception of the discontinuity, while all other reconstruction methods show gradients across at least one grid cell, to which the formation of spectral lines is sensibly reacting on.
From Table 1, one can see that for the piecewise constant reconstruction, the maximal errors grow up to 1% in the continuum intensity, and up to 16%, 9.6%, and 8.5% in Stokes I, Q, and V, respectively. These numbers are 1.4%, 22%, 13%, and 11.5%, respectively, for the continuous interpolation. Although the piecewise continuous reconstruction method reproduces the exact solution when τ_{d} = 0.6 and τ_{d} = 0.9, the overall improvement over the strictly continuous method is not dramatic.
The third example, is a bit more complicated then the first two and produces asymmetric Stokes profiles. The source function is given by (39)The line opacity κ_{L} = 10 is the same in both layers, but the bulk velocity and the normalized Zeeman splitting change from v_{A} = 0 and v_{B} = 1.5 ahead of the discontinuity, i.e., for τ_{c}<τ_{d} (top layer), to v_{A} = 2.0 and v_{B} = 0 behind the discontinuity, i.e., for τ_{c}>τ_{d} (bottom layer). All other parameters are kept constant and assume the standard values given in Sect. 4.1. Notice that only the outer (canopy) layer contains a magnetic field, which is illuminated by a medium beneath it that moves with v_{A} = 2.0 in the upward direction. With this choice, the illumination of the top layer at frequencies of the two Zeeman components is asymmetric because the absorption line in the bottom layer is shifted to the frequency of the blue Zeeman component, which produces asymmetric Stokesprofiles (GrossmannDoerth et al. 1988; Steiner 2000).
Figure 7 shows from the second to the last column the Stokes profiles as the discontinuity moves from τ_{d} = 0.6 to τ_{d} = 0.9 in steps of Δτ = 0.1. Different from the former two examples, at τ_{d} = 0.6 and τ_{d} = 0.9, when the discontinuity exactly coincides with a cell boundary, the piecewise continuous reconstructions do not reproduce the reference solution exactly anymore, but still, they match it closely. On the other hand, when the discontinuity straddles the cell at τ_{d} = 0.7, the piecewise continuous reconstructions are not substantially better than the continuous interpolation but overall, they perform not bad. At τ_{d} = 0.8, the continuous interpolation performs definitely better than the piecewise continuous reconstructions. It is remarkable that the piecewise constant and the piecewise linear reconstructions are very similar despite that the corresponding source functions (top row) look distinctly different.
The modest performance of the piecewisecontinuous reconstructions in this case is probably due to the inexact tracing of the local minimum of the source function immediately ahead of the discontinuity. Since it is a local minimum, TVDschemes discard it and replace it by a locally flat function. At least for the important Stokes V, the maximal deviation of the piecewise continuous reconstructions from the reference solution is always less than that of the continuous interpolation (see Table 1).
6. Conclusions
This paper deals with the problem of computing the formal solution of the radiative transfer equation for polarized light across an atmosphere that harbors discontinuities. Discontinuities may arise, e.g., at the interface of media at different physical states or at shock fronts of ideal fluids. In the case of a nonideal fluid, one still deals with a quasi discontinuity when the jump in physical states occurs on a spatial scale much smaller than that of the numerical discretization. Given the physical quantities that characterize the atmosphere on a discrete grid by discrete grid functions, we reconstruct corresponding functions that define these quantities at any point along a lineofsight, which is needed for the integration of the radiative transfer equation. In the past, this reconstruction has been achieved with polynomials of ever higher degree and smoothness. For example, for the effective source function of the DELO method, linear, then quadratic interpolation, and finally quadratic and cubic Bézier splines have been employed.
Here, we propose to take the opposite path, reconstructing the source function with piecewise continuos functions, accepting discontinuities at computational cell interfaces. As was the purpose of the Bézier splines, the piecewise continuous reconstruction is chosen such that it remains strictly positive and monotone. This is trivially fulfilled for the piecewise constant reconstruction and, as shown in Appendix A, it is fulfilled for the piecewise linear reconstruction using the minmod function as a slope limiter. This approach for solving the radiative transfer equation for polarized light may be considered similar in spirit to previously designed codes by Dykema et al. (1996) and Korčáková & Kubát (2003) for the radiative transfer in moving media that made use of the discontinuous finite element method (see also Castor 2004,Sect. 11.9). There however, the piecewise continuous reconstruction applies to the intensity itself, not only to the source function as we do here and slopes are determined by the Galerkin formalism.
The performance of the new method is assessed with three simple test cases. An atmosphere featuring a single discontinuity at an arbitrary position is first discretized in a conservative manner as expressed by Eq. (28) (and analogous for other quantities that exhibit a jump at the same location as the source function). Next, the source function is reconstructed using (i) a strictly continuous linear interpolation; (ii) the piecewise constant reconstruction; and (iii) the piecewise linear reconstruction with a variety slope limiters. For other discontinuous quantities, piecewise constant reconstruction is always used. From these reconstructions, the emergent Stokes vector is computed and the Stokes profiles are compared with the reference solution obtained with very high resolution in optical depth. The strictly continuous linear interpolation (i) represents here the classical DELO method, which also uses linear interpolation. However, different from DELO, we always use the formal solution of Landi Degl’Innocenti & Landi Degl’Innocenti (1985) for the integration across one computational cell and do not deal with the effective source function used by the original DELO method of Rees et al. (1989).
For a MilneEddington atmosphere (source function linear with optical depth), all methods, with the exception of the piecewise constant reconstruction, reproduce the analytical solution exactly. The piecewise constant reconstruction leads to an error of 1% in the continuum intensity, and to a maximal error in Stoke I of 5.6%, while Q and V have maximal deviations of 3% and 2.5%, respectively (see Table 1). These errors are relatively small considering that the chosen computational grid is very coarse with a constant optical depth step of Δτ_{c} = 0.4.
The piecewise constant reconstruction in turn performs best in the case of a step source function, which exhibits a single jump, otherwise being constant. In fact, all piecewise continuous reconstruction methods reproduce the exact solution when the jump of the original source function coincides with a computational cellinterface, while the DELOlike linear interpolation shows an error of 0.3% in the continuum intensity and maximal errors of 22%, 13%, 2.4%, and 11.5% in Stokes I, Q, U, and V, respectively. When the discontinuity of the original source function is located somewhere within a computational cell, then the piecewise constant reconstruction performs still best: the maximal errors grow to 1% in the continuum intensity, and to 16%, 9.6%, and 8.5% in Stokes I, Q, and V, respectively. In turn, these numbers are 1.4%, 22%, 13%, and 11.5%, respectively, for the DELOlike continuous interpolation. The piecewise linear reconstruction with minmod limiter shows slightly worse behavior than the piecewise constant reconstruction (with corresponding maximal errors of 0.6%, 18.8%, 10.3%, and 8.9% for the continuum intensity and Stokes I, Q, and V, respectively). When using piecewise linear reconstruction with Van Leer and superbee limiter, the fidelity to the reference solution further deteriorates but remains over all still better than the continuous interpolation.
The last test case consists in a step source function with linear (MilneEddington like) behavior away from the discontinuity. Together with the source function, also the magnetic field strength (normalized Zeeman splitting) and the lineofsight bulk velocity jumps at the location of the discontinuity, which produces asymmetric Stokes profiles. In this case, none of the reconstructions reproduce the reference solution exactly anymore, even when the jump of the original source function coincides with a cell interface. In these instances, the reconstructions still perform considerably better than the linear interpolation. But as the jump moves sufficiently far into the cell, the linear interpolation starts to outperform the piecewise continuous reconstructions.
Over all, the assessment of the piecewise continuous reconstruction method turns out cautiously positive but does not lead to an orderofmagnitude improvement over conventional methods for the examples considered here. For a better and more meaningful assessment we plan to first improve the piecewise continuous reconstruction method by applying it also to the
propagation matrix, not only to the source function. This is still possible within the analytical formalism of Landi Degl’Innocenti & Landi Degl’Innocenti (1985). Next, we plan to implement the true DELO method in our test code in replacement of the current approximative DELO representative for better comparison. Finally, the new method will be applied to real life atmospheres from numerical, threedimensional magnetohydrodynamic simulations.
The Stokes inversion problem consists in the determination of the kernel of the integral Eq. (3) and with it of the physical state of the atmosphere, given the emergent intensity I(0).
Here and in the following, we use Eq. (32) as the interpolation scheme.
Proof: first, σ_{i} ≥ 0 because q_{i + 1} ≥ q_{i} and φ(r) ≥ 0, while σ_{i−1} ≥ 0 because q_{i} ≥ q_{i−1} and φ(r) ≥ 0. Furthermore, σ_{i + 1} = φ(r_{i + 1})(q_{i + 1}−q_{i}) /r_{i + 1} ≥ 0 because if r_{i + 1} ≤ 0 it follows that φ(r_{i + 1}) = 0 and if r_{i + 1}> 0, σ_{i + 1} ≥ 0 because φ(r_{i + 1}) ≥ 0 and (q_{i + 1}−q_{i}) ≥ 0, qed.
Acknowledgments
This work has benefited from discussions within the international team “Heating of the magnetized chromosphere” (B. De Pontieu & S. McIntosh, conveners) at the International Space Science Institute (ISSI). Special thanks are extended to J. de la Cruz Rodriguez and J. C. del Toro Iniesta for explaining to us details of the DELO approach, to M. Steffen for pointing us to the work of Korčáková & Kubát (2003), and to J. M. Borrero and G. Janett for reading and commenting on a previous version of the manuscript. We thank the anonymous referee for careful reading and helping us improving legibility of the text.
References
 Auer, L. 1976, J. Quant. Spec. Radiat. Transf., 16, 931 [NASA ADS] [CrossRef] [Google Scholar]
 Auer, L. 2003, in Stellar Atmosphere Modeling, eds. I. Hubeny, D. Mihalas, & K. Werner, ASP Conf. Ser., 288, 3 [Google Scholar]
 Auer, L. H., Heasley, J. N., & House, L. L. 1977, ApJ, 216, 531 [NASA ADS] [CrossRef] [Google Scholar]
 Beckers, J. M. 1969, Sol. Phys., 9, 372 [NASA ADS] [CrossRef] [Google Scholar]
 Bellot Rubio, L. R., Ruiz Cobo, B., & Collados, M. 1998, ApJ, 506, 805 [NASA ADS] [CrossRef] [Google Scholar]
 Castor, J. I. 2004, Radiation Hydrodynamics (Cambridge University Press), 368 [Google Scholar]
 Colella, P., & Woodward, P. R. 1984, J. Comp. Phys., 54, 174 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
 De la Cruz Rodríguez, J., & Piskunov, N. 2013, ApJ, 764, 33 [NASA ADS] [CrossRef] [Google Scholar]
 Dykema, P. G., Klein, R. I., & Castor, J. I. 1996, ApJ, 457, 892 [NASA ADS] [CrossRef] [Google Scholar]
 Feautrier, P. 1964, Comptes Rendus de L’Académie des Sciences, 258, 3189 [NASA ADS] [Google Scholar]
 Freytag, B., Steffen, M., Ludwig, H.G., et al. 2012, J. Comp. Phys., 231, 919 [NASA ADS] [CrossRef] [Google Scholar]
 GrossmannDoerth, U., Schüssler, M., & Solanki, S. K. 1988, A&A, 206, L37 [NASA ADS] [Google Scholar]
 Harten, A. 1983, J. Comp. Phys., 49, 357 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
 Holzreuter, R., & Solanki, S. K. 2012, A&A, 547, A46 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Korčáková, D., & Kubát, J. 2003, A&A, 401, 419 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Landi Degl’Innocenti, E. 1976, A&AS, 25, 379 [NASA ADS] [Google Scholar]
 Landi Degl’Innocenti, E. 1987, in Numerical Radiative Transfer, ed. W. Kalkofen (Cambridge University Press), 265 [Google Scholar]
 Landi Degl’Innocenti, E., & Landi Degl’Innocenti, M. 1985, Sol. Phys., 97, 239 [NASA ADS] [CrossRef] [Google Scholar]
 Landi Degl’Innocenti, E., & Landolfi, M. 2004, Polarization in Spectral Lines (Dordrecht: Kluwer Academic Publishers), Astrophys. Space Sci. Library, 307 [Google Scholar]
 LeVeque, R. 2002, Finite Volume Methods for Hyperbolic Problems (Cambridge University Press) [Google Scholar]
 Rachkovsky, D. N. 1962a, Izvestiya Ordena Trudovogo Krasnogo Znameni Krymskoj Astrofizicheskoj Observatorii, 27, 148 [Google Scholar]
 Rachkovsky, D. N. 1962b, Izvestiya Ordena Trudovogo Krasnogo Znameni Krymskoj Astrofizicheskoj Observatorii, 28, 259 [NASA ADS] [Google Scholar]
 Rees, D. E., Durrant, C. J., & Murphy, G. A. 1989, ApJ, 339, 1093 [NASA ADS] [CrossRef] [Google Scholar]
 Roe, P. L. 1986, Ann. Rev. Fluid Mech., 18, 337 [NASA ADS] [CrossRef] [Google Scholar]
 Steffen, M. 1990, A&A, 239, 443 [NASA ADS] [Google Scholar]
 Steiner, O. 2000, Sol. Phys., 196, 245 [NASA ADS] [CrossRef] [Google Scholar]
 Štěpán, J., & Trujillo Bueno, J. 2013, A&A, 557, A143 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Sweby, P. K. 1984, SIAM J. Num. Analysis, 21, 995 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
 Toro, E. F. 2009, Riemann Solvers and Numerical Methods for Fluid Dynamics, (Springer) [Google Scholar]
 Trujillo Bueno, J. 2003, in Stellar Atmosphere Modeling, eds. I. Hubeny, D. Mihalas, & K. Werner, ASP Conf. Ser., 288, 551 [Google Scholar]
 Unno, W. 1956, PASJ, 8, 108 [NASA ADS] [Google Scholar]
 Van Leer, B. 1974, J. Comp. Phys., 14, 361 [NASA ADS] [CrossRef] [Google Scholar]
 Van Leer, B. 1979, J. Comp. Phys., 32, 101 [NASA ADS] [CrossRef] [Google Scholar]
 Wittmann, A. 1974, Sol. Phys., 35, 11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Appendix A: Total variation diminishing codomain
Figure A.1 shows the computational cell i with discrete value q_{i} and neighboring values q_{i−1} and q_{i + 1} and the piecewise linear reconstruction (dashed, red) with slope σ_{i} in cell i and slopes σ_{i−1} and σ_{i + 1} in the neighboring cells i−1 and i + 1, respectively. The reconstruction in cell i−1 ends on the left interface of cell i with value and the reconstruction in cell i + 1 on the right interface of cell i with value . Slope σ_{i} is, according to Eq. (12), given by (A.1)where r_{i} is the ratio of successive gradients, (A.2)Corresponding equations exist for σ_{i−1} and σ_{i + 1} and r_{i−1} and r_{i + 1}.
In the following, we seek finding the admissible codomain of the function φ(r_{i}) that constrains the slope σ_{i} within bounds such that the reconstruction stays monotone for monotone discrete values q_{i−1}, q_{i}, and q_{i + 1}. Thus, we demand that (A.3)where we assume without loss of generality that q_{i−1} ≤ q_{i} ≤ q_{i + 1}. In case that the sequence q_{i−1}, q_{i}, q_{i + 1} is nonmonotone, then r_{i}< 1. In this case, we demand that φ(r_{i}) = 0, hence, σ_{i} = 0. Also φ(r) ≥ 0∀r ∈R, else the slope σ would be contrarian to the monotonic trend of the discrete values. Condition (A.3) implies that the reconstruction is also monotone in the interval [x_{i−1},x_{i + 1}]^{4}.
From the first relation of condition (A.3), where we just need to consider , and using that and substituting σ_{i−1} with the corresponding right hand side of Eq. (A.1), it follows that (A.4)Correspondingly, from the second relation of condition (A.3) it follows (A.5)which is equivalent to Eq. (A.4).
Fig. A.1
Computational cell i with discrete value q_{i} and neighboring values q_{i−1} and q_{i + 1}. The piecewise linear reconstruction (dashed, red lines) assumes value in cell i−1 on the left interface of cell i and value in cell i + 1 on the right interface of cell i. 

Open with DEXTER 
We can further postulate that φ(1) = 1, since then, with r_{i} = 1, the reconstruction in cell i assumes the same linearity as the grid function for the values q_{i−1},q_{i}, and q_{i + 1}, which in this case fall all on a straight line because q_{i}−q_{i−1} = q_{i + 1}−q_{i}.
Setting r_{i} = 1, we then obtain from Eq. (A.4) φ(r_{i−1}) ≤ 1∀r_{i−1} ≥ 0, hence, we must have φ(r) ≤ 1∀r ≥ 0. Setting r_{i−1} = 1, we obtain from the same equation (1 /r_{i})φ(r_{i}) ≤ 1∀r_{i} ≥ 0, and hence, we must also have φ(r) ≤ r∀r ≥ 0. Both together, and recalling that φ(r) = 0forr< 0 and φ(r) ≥ 0∀r ∈R, can be expressed as (A.6)where the function minmod(.,.) is defined as (A.7)Next, we can show that any continuous, single valued function φ(r) that satisfies Eq. (A.6) also fulfills condition (A.4) (or condition (A.5)) for any combination of r_{i}, r_{i−1}, r_{i + 1}. Considering Eq. (A.4), we have only to deal with r_{i} and r_{i−1}, in which case we have the only combinations (i) r_{i},r_{i−1} ∈ [0,1], (ii) r_{i−1} ∈ [0,1] ,r_{i} ≥ 1; (iii) r_{i} ∈ [0,1] ,r_{i−1} ≥ 1, and (iv) r_{i},r_{i−1} ≥ 1. For combination (i), Eq. (A.6) implies that Using these inequalities in Eq. (A.4) leads to an inequality, which is unconditionally fulfilled. Likewise, for combination (ii), Eq. (A.6) implies that which again, when inserting in Eq. (A.4), leads to an inequality that is always fulfilled. For combination (iii), Eq. (A.6) implies that again leading to an unconditional inequality when inserting in Eq. (A.4), and the same is true for combination (iv) when Eq. (A.6) implies that Likewise, the same exercise can be performed for condition (A.5) so that any curve with 0 ≤ φ(r) ≤ minmod(r,1),r ≥ 0 fulfills conditions (A.5) and (A.6) for any combinations of r_{i}, r_{i−1}, r_{i + 1}, qed. Thus, together with φ(r) = 0 for r ≤ 0, the grey area in Fig. 3 defines the codomain of limiters φ(r) that guarantee monotonicity of the reconstruction.
The total variation of a grid function q_{i} or an arbitrary, differentiable function (or distribution) q(x) is, respectively, defined as (A.8)where q_{i} or q(x) must approach constant values q^{±} as x,i → ± ∞. The step from the discrete grid function q_{i} to the reconstruction q(x) is called total variation nonincreasing (TVNI) or short TVD for total variation diminishing if (A.9)According to Harten (1983), the transition from q_{i} to q(x) is TVD if it is monotonicity preserving, which is the condition from which Eq. (A.6) was derived, i.e., Eq. (A.3) with corollary proven in footnote 4. Thus, piecewise linear reconstructions with limiters satisfying Eq. (A.6) are TDV.
We can also derive Eq. (A.6) from condition (A.9) directly. For that we first write the reconstruction in the interval ] x_{i−3/2},x_{i + 3/2} [ in terms of the unit step distribution (A.10)For to evaluate the total variation of this function, we need its derivative, which is (A.11)The total variation of q(x) in the interval [x_{i−1},x_{i + 1}] is then according to the right expression in Eq. (A.8) (A.12)which simply is the sum of the two jumps at and plus the variation of the reconstruction in cell i and half of cells i−1 and i + 1. The corresponding expression for the grid function q_{i} is according to the left expression in Eq. (A.8)(A.13)Using Eq. (A.1) and corresponding expressions for σ_{i−1} and σ_{i + 1} in Eq. (A.12) and rearranging terms and using Eq. (A.13), condition (A.9) then becomes (A.14)In the same way as for Eqs. (A.4) and (A.5), this inequality must be satisfied for all possible combinations of r_{i}, r_{i−1}, r_{i + 1}. Postulating φ(r) = 0 for r< 0, φ(r) ≥ 0 for r ≥ 0, and φ(1) = 1 as done in the previous derivation, we then can again derive Eq. (A.6). Likewise, having condition (A.6), we can check if Eq. (A.14) is satisfied for all possible combinations of r_{i}, r_{i−1}, r_{i + 1}.
In the numerical analysis of advection schemes, a similar gray shaded region as shown in Fig. 3 is known as the (firstorder) Sweby region (see Sweby 1984; LeVeque 2002) but it comprises the (larger) region 0 ≤ φ(r) ≤ minmod(2,2r). The reason for this difference is that we consider here exclusively the reconstruction step, i.e., the step from the grid function q_{i} to the reconstruction q(x), while Sweby regions are derived for the full sequence of advectionscheme steps consisting of reconstruction, evolution, and averaging. Especially the averaging step has a diffusive character, which relaxes conditions for the slope limiter in the reconstruction step, admitting a larger codomain for φ(r) than when considering the reconstruction step alone.
All Tables
Errors in the Stokes profiles of three different examples with different reconstructions of the source function.
All Figures
Fig. 1
Definition of optical depth, ray path, and grid indices for cells, nodes, and cell interfaces. 

Open with DEXTER  
In the text 
Fig. 2
a) Initial, piecewise linear function with a single discontinuity at the interface from cell 4 to cell 5. The black dots indicate its conservative discretization (discrete grid function); b) nonconservative, continuous linear interpolation of the discrete grid function. Linear extrapolation is applied in half of the first and half of the last computational cell. c) Piecewise constant reconstruction of the discrete grid function; d) piecewise linear reconstruction using the minmod slope limiter of Eq. (14); e) piecewise linear reconstruction using the Van Leer slope limiter of Eq. (15); f) piecewise linear reconstruction using the superbee slope limiter of Eq. (16). Panels b)–f) replicate the discrete grid function (black dots) from panel a). 

Open with DEXTER  
In the text 
Fig. 3
Three different slopelimiter functions: minmod (Eq. (14), solid curve), Van Leer (Eq. (15), dotdashed curve), and superbee (Eq. (16), dashed curve). The grey area indicates the admissible codomain of the function φ(r) for which the total variation of the resulting reconstruction does not surpass the total variation of the original, discrete grid function (see Appendix A). 

Open with DEXTER  
In the text 
Fig. 4
Angles θ and χ that specify the magnetic field direction with respect to the coordinate system ê_{1} and ê_{2} and the direction of the lineofsight s. Adapted from Landi Degl’Innocenti & Landolfi (2004). 

Open with DEXTER  
In the text 
Fig. 5
a) Source function S(τ_{c}) using any of Eqs. (31)−(33) (red, solid) and using the piecewise constant reconstruction of Eq. (30) (blue, dotted) as a function of continuum optical depth τ_{c}. The corresponding resulting Stokes profiles are given in panels b)−e) as functions of the reduced frequency v: (black, solid) for the source function of Eqs. (31)−(33) and (blue, dotted) for the piecewise constant reconstruction of Eq. (30). b) Stokes I; c) Stokes Q; d) Stokes U; e) Stokes V. 

Open with DEXTER  
In the text 
Fig. 6
Second example. Source function (first row) and Stokes I, Q, U, and V as a function of reduced frequency v (second to fifth row, respectively) at five different positions of the discontinuity, τ_{c} = 0.6, 0.675, 0.75, 0.825, and 0.9 shown in this sequence from the first column to the last column. Each panel shows the curves for the continuous interpolation according to Eq. (32) (light blue), the piecewise constant reconstruction according to Eq. (30) (blue), the piecewise linear reconstruction with minmod slope limiter according to Eqs. (31) and (14) (red), and the reference solution (orange, dashed). 

Open with DEXTER  
In the text 
Fig. 7
Third example. Source function (first row) and Stokes I, Q, U, and V as a function of reduced frequency v (second to fifth row, respectively) at four different positions of the discontinuity, τ_{c} = 0.6, 0.7, 0.8, and 0.9 shown in this sequence from the first column to the last column. Each panel shows the curves for the continuous interpolation according to Eq. (32) (light blue), the piecewise constant reconstruction according to Eq. (30) (blue), the piecewise linear reconstruction with minmod slope limiter according to Eqs. (31) and (14) (red), and the reference solution (orange, dashed). 

Open with DEXTER  
In the text 
Fig. A.1
Computational cell i with discrete value q_{i} and neighboring values q_{i−1} and q_{i + 1}. The piecewise linear reconstruction (dashed, red lines) assumes value in cell i−1 on the left interface of cell i and value in cell i + 1 on the right interface of cell i. 

Open with DEXTER  
In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.