Granulation, with a typical size of 1.5 Mm, is well understood as a convective phenomenon and can be studied with realistic numerical simulations (see, e.g., Stein and Nordlund, 2000). Supergranules, however, have remained puzzling since their detection by Hart (1954, 1956). In a classic paper, Leighton et al. (1962) reported “large cells of horizontally moving material distributed roughly uniformly over the entire solar surface” with a characteristic scale of 30 Mm that are outlined by the chromospheric network; they suggested that the cells are a “surface manifestation of a supergranulation pattern of convective currents”. Unfortunately, there is no accepted theory that explains why solar convection should favor a 30 Mm scale. The solar convection zone is so highly turbulent and stratified that numerical modeling at supergranular scales has remained elusive (see, e.g., Rincon, 2004, and references therein).
Simon and Leighton (1964) presumed that a convective instability due to the recombination of ionized Helium is at the origin of the distinct supergranular scale. Forty years later, this hypothesis has not been proved to be right or wrong. The depth of the supergranulation layer is not known either. We note that Parker (1973) believes that the effect of stratification on convection may imply that supergranules are a deep phenomenon, with depths in excess of their horizontal diameters. Antia and Chitre (1993) investigated the stability of linear convective modes in the solar convection zone and found that a few convective modes dominate the power spectrum, one of which was identified as supergranulation. Using 1D numerical caculations, Ploner et al. (2000) suggested that the scale of supergranulation may be due to the interaction and merging of individual granular plumes (see also Rast, 2003). A somewhat related model was proposed by Rieutord et al. (2000, 2001) whereby supergranulation is the result of a nonlinear largescale instability of the granular flow, triggered by exploding granules. In the two previous models, supergranulation is not a proper scale of thermal convection. Regarding the influence of supergranular flows on magnetic fields, it is well established that a stationary cellular flow tends to expel the magnetic field from the regions of fluid motion and concentrate the magnetic flux into ropes at the cell boundaries (Parker, 1963; Galloway et al., 1977; Galloway and Weiss, 1981).
On the observational side, the original work of Leighton and coworkers has been refined. A variety of methods have been used to characterize the distribution of the cell sizes. A characteristic scale can be obtained from the spatial autocorrelation function (see, e.g., Hart, 1956; Simon and Leighton, 1964; Duvall Jr, 1980), the spatial Fourier spectrum (see, e.g., Hathaway, 1992; Beck, 1997), and segmentation or tessellation algorithms (Hagenaar et al., 1997). Although definitions vary, average cell sizes are in the range 15 – 30 Mm. It is unclear whether there is a variation of cell sizes with latitude: Rimmele and Schroeter (1989) and Komm et al. (1993a) report a possible decrease with latitude, Berrilli et al. (1999) an increase, and Beck (1997) no significant variation. The typical lifetime of the supergranular/chromospheric network is found to be in the range 1 – 2 d (e.g Rogers, 1970; Worden and Simon, 1976; Duvall Jr, 1980; Wang and Zirin, 1989). The rms horizontal velocity of supergranular flows is known to be about 300 m s^{–1} (see, e.g., Hathaway et al., 2000). The vertical component of the flows, however, has been extremely difficult to measure (see, e.g., Giovanelli, 1980) or infer (November, 1989). Indeed, Miller et al. (1984) caution that Doppler velocity measurements at the cell boundaries may be polluted by the network field. Estimates of the rms vertical flow are provided by Chou et al. (1991) and Hathaway et al. (2002) who find speeds of about 30 m s^{–1} (the topology of the vertical flows is largely unknown). Even more difficult to measure are the related temperature fluctuations. Observers have searched for the thermal signature of a convective process, i.e., rising hot material at the cell centers and sinking cool material at the cell boundaries. Unfortunately, answers vary too widely (see Lin and Kuhn, 1992, and references therein).
Estimates of the pattern rotation rate of supergranulation can be obtained by correlation tracking techniques. Duvall Jr (1980) and Snodgrass and Ulrich (1990) used Doppler scans separated in time by = 24 hr. The results indicate that the equatorial rotation of the supergranulation pattern is faster than the spectroscopic rate (Snodgrass and Ulrich, 1990) by about 4% and faster than the magnetic features (Komm et al., 1993b) by about 2%. Duvall Jr (1980) also considered sameday scans with = 6 hr and found a slightly smaller pattern rotation rate than for = 24 hr.
It was suggested by Foukal (1972) that the difference between the rotation of magnetic features and the spectroscopic rate may be due to magnetic structures being rooted in deeper, more rapidly rotating layers. Supergranules must also sense increase rotation in the shear layer below the surface. However, Beck (2000) remarks that the pattern rotation of supergranulation measured from correlation tracking with = 24 hr is significantly faster than the rotation of the solar plasma measured by helioseismology at any depth in the interior. It is especialy puzzling that the rotation of the magnetic network is significantly less than that of the supergranular pattern, since small magnetic elements are believed to be advected by supergranular flows. Hathaway (1982) suggested that a faster supergranular rate may be a direct consequence of the interaction of convection and rotation.
As explained in the following sections, local heliososeismology has become a powerful tool to study the structure and evolution of supergranular flows. In fact, helioseismological measurements have transformed our knowledge of the dynamics of supergranulation.
In order to resolve structures at supergranular scales, Duvall Jr et al. (1996) measured pmode travel times at distances shorter than 10 Mm over an 8.5 hr time interval. Directional information was obtained by crosscorrelating a point on the surface with surrounding quadrants centered on the four cardinal directions. In a first order approximation, the southnorth and eastwest travel time differences were converted into an apparent horizontal vector flow field without inversion. Duvall Jr et al. (1997) found that the lineofsight projection of the inferred flow field is highly correlated with the mean MDI Dopplergram (correlation coefficient 0.74). This comparison was initialy used as a validation of the timedistance technique.
Three dimensional inversions of quietsun travel times have been presented by Duvall Jr et al. (1997), Kosovichev and Duvall Jr (1997), and Zhao and Kosovichev (2003a). Tests of the raybased inversion procedure of Zhao and Kosovichev (2003a) show that the horizontal components of the velocity can be infered with some confidence in the upper 5 Mm (and perhaps down to 10 Mm). The small vertical flows, on the other hand, cannot be inferred reliably near the surface due to significant “crosstalk” with the horizontal divergence signal.
Nearsurface horizontal flows have been measured at a depth of 1 Mm with fmode timedistance helioseismology (Duvall Jr and Gizon, 2000). In this case travel time differences are directly sensitive to horizontal flows because f modes propagate horizontaly. The fmode timedistance technique gives results that are comparable to correlation tracking of granulation (De Rosa et al., 2000). Shown in Figure 22 is an inversion of fmode travel times that employs 2D Born kernels (Gizon et al., 2000). The correlation coefficient between the estimated lineofsight velocity and the surface Doppler image is about 0.7.
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The holographic technique also measures flows at supergranular scales. As shown earlier in Figure 45, supergranules are easy to identify as regions of outflows for a 3 Mm focus depth (24 hr time average).
A practical way to detect and display supergranulation is to plot the horizontal divergence of the flow field. Timedistance helioseismology applied to f modes is particularly well adapted to measuring the horizontal divergence of the velocity near the surface. Duvall Jr and Gizon (2000) measured the time it takes for solar f modes to propagate from any given point on the solar surface to a concentric annulus around that point. The difference in travel times between inward and outward propagating waves is a proxy for the local horizontal divergence. An example of the divergence signal (inward minus outward travel time) for one 8hour interval analyzed is shown in Figure 57 with magnetic field information overlaid (MDI fulldisk data). A white, or positive signal, corresponds to a horizontal outflow. From the size of the features present, their lifetime, and the presence of the magnetic field in the dark lanes regions, supergranulation is identified as the main contributor to the signal. This can also be seen by making a spatial power spectrum of the divergence signal: Power peaks near degree . The movie associated with Figure (57) shows that the magnetic features stay in the regions of horizontal convergence over the time interval of the observations (one week).
Local heliososeismology opens prospects for mapping the structure of supergranular flows below the surface. A major goal is to answer the longstanding question of how deep supergranular flows persist below the surface.

Duvall Jr et al. (1997) and Kosovichev and Duvall Jr (1997) presented threedimensional inversions of quietsun travel times using raytheoretical sensitivity kernels. Figure 58 shows a vertical cut through the velocity field; the horizontal sampling is 4.3 Mm and the vertical sampling is about 1 Mm. In the upper 5 Mm the flows are positively correlated with the direct surface observations. To assess the average structure of superganular flows with depth, Duvall Jr (1998) used the inversion results from Duvall Jr et al. (1997) to measure the correlation of horizontal flows between the surface and any given depth. As seen in Figure 59, the correlation drops to zero at a depth of 5 Mm and is negative in the range 5 – 8 Mm, suggesting the existence of a “return flow”. Since the correlation seems to disappear below 8 Mm, Duvall Jr (1998) concluded that the depth of supergranulation is 8 Mm. The same type of analysis was repeated by Zhao and Kosovichev (2003a) with more recent (and presumably more reliable) inversions of travel times. They found that the correlation of the horizontal divergence falls off away from the surface, changes sign at a depth of about 6 Mm, and is negative at depths in the range 6 – 14 Mm (see Figure 59). In particular, Zhao and Kosovichev (2003a) find a correlation coefficient of –0.5 at a depth of 10 Mm. Zhao and Kosovichev (2003a) concluded that supergranulation is about 15 Mm deep.

We note that inversion results heavily rely on the assumed travel time sensitivity kernels. While most inversions use raybased kernels, Jensen et al. (2000) and Birch and Kosovichev (2000) showed that finitewavelength effects must be taken into account. It may be that wavebased kernels could yield depth inversion results that are qualitatively different from the ones presented above.

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Braun and Lindsey (2003) used a 24 hr MDI data cube and seismic holography to make maps of convective flows at focus depths from 3 Mm to 37 Mm. The horizontaldivergence maps are shown in the left panels of Figure 60. The behavior of the divergence maps with depth can be studied by making scatter plots of the divergence values observed at a given depth versus the nearsurface values. The slope of a line fit to the scatter is observed to change sign at 10 Mm and to remain negative in the range 10 – 37 Mm. Although it is tempting to interpret the reversal of the horizontal divergence signal at focus depths greater than 10 Mm, Braun and Lindsey (2003) suspect that this reversal could be due to the increasing contribution of oppositely directed surface flows as the pupil increases in size with focus depth. To test this hypothesis, they considered a control experiment in which the observed flows at 3 Mm depth are used to estimate the surface contribution to the seismic measurements at greater focus depths. The divergence maps of the control experiment, which are shown in the right panels of Figure 60, ressemble the observed divergence maps (left panels). The good correspondence between the two suggests that the observations at depths are mostly due to leakage from the surface (the “showerglass effect”). Modelling efforts by Braun et al. (2004) have confirmed that the change of sign of the correlation below 10 Mm is a predominantly surface contamination of the velocity signal from neighboring supergranules. However, the fact that the control data do not reproduce exactly the observations may indicate that there is at least some sensitivity to velocities at depth.
It would appear that local helioseismology has not yet provided a definitive answer regarding the depth of supergranulation.
Since the typical lifetime of supergranules is significantly less than the solar rotation period, the influence of rotation on supergranular convection is expected to be small. Solar rotation effects in supergranules were illustrated by Hathaway (1982) in nonlinear numerical simulations. The Coriolis force causes divergent and convergent horizontal flows to be associated with vertical components of vorticity of opposite signs (on average). In the northern hemisphere, cells rotate clockwise where the horizontal divergence is positive, while they rotate counterclockwise in the convergent flow towards the sinks. The sense of circulation is reversed in the southern hemisphere.
In general, it is not straightforward to predict the statistical properties of the vorticity in rotating turbulent convection: Vorticity production is due to the effect of the Coriolis force and to vortex stretching and tilting mechanisms. The importance of the Coriolis force is characterized by an inverse Rossby number, or Coriolis number, defined by
where is a characteristic correlation time and is the equatorial solar angular velocity. The choice implies for supergranulation. At latitude , quasilinear theory (Rüdiger et al., 1999) predicts that the vertical vorticity of the velocity (denoted below by ‘curl’) and the horizontal divergence of the horizontal velocity (denoted by ‘div’) are correlated according to where the brackets denote an expectation value or longitudinal average. The latitudinal variations are given by the function and are due to the projection of the local angular velocity vector onto the radial direction.

The effect of rotation on supergranules was first observed by Duvall Jr and Gizon (2000) and Gizon and Duvall Jr (2003)^{2}. They computed the vertical vorticity and horizontal divergence from vector flow maps obtained with fmode timedistance helioseismology (1 Mm deep, 8 hr time invervals). The vertical largescale vorticity due to differential rotation and meridional circulation was removed to study the vorticity at supergranular scales. Panel (a) of Figure 62 shows the correlation coefficient between div and curl at latitude ,
where the angle brackets denote a spatial average over the area of a 10° longitudinal strip centered around . In the north, positive (negative) divergence is correlated with clockwise (anticlockwise) vorticity; the correlation changes sign in the south. Thus, away from the equator, the number of righthanded cyclones is not equal to the number of left handed cyclones. The sign and the latitudinal variation of are both characteristic of the effect of the Coriolis force on the flows. Panel (b) of Figure 62 shows horizontal averages of the vertical vorticity versus , where the averages and are restricted to the regions of positive and negative divergence, respectively. Note that there is a small difference between the total area covered by regions of positive and negative divergence (Duvall Jr and Gizon, 2000). A nearly perfect linear relationship between and is observed, with . This is again consistent with the interpretation as a Coriolis effect.Observations may be summarized by a measurement of the average correlation between the horizontal divergence and the vertical vorticity (Gizon and Duvall Jr, 2003):
Since for , the naive prediction from Equation (91) is one order of magnitude smaller than the measurements (Equation 94). Recent numerical simulations of rotating convection by Egorov et al. (2004) give values of near the top of the convection zone that appear to be close to the observations. They show that a good agreement with the data is obtained for a reasonable Coriolis number (their definition of the Coriolis number is slightly different than ours; Egorov, 2004, private communication). Egorov et al. (2004) predict that the amplitude of the curl–div correlation should decrease fast with depth.As mentioned above, the pattern rotation rate of supergranulation derived from correlation tracking of Doppler scans appears to be significantly faster than the spectroscopic rate (for a 24 hr time lag). Correlation tracking algorithms can also be applied to images of supergranular flows derived from local helioseismology, such as the horizontal divergence signal. Unlike raw Doppler images, the divergence signal has uniform sensitivity across the solar disk and is subject to few systematic errors.

Gizon and Duvall Jr (2003) used a 60day sequence of 90° × 90° divergence maps obtained with fmode timedistance helioseismology (0.24° spatial sampling). The original MDI Doppler velocity images were tracked at the Carrington rate to remove the main component of rotation. Small regions, apodized by a Gaussian surface with a full width at half maximum of 3.84° and separated by a timelag , were spatially crosscorrelated. For the peak of maximum correlation is easy to identify, and the spatial displacement at correlation maximum gives an apparent pattern velocity , where the subscript LCT stands for ’local correlation tracking’. Figure 63 shows that the apparent motion of the supergranulation pattern depends on . In particular, the pattern rotation rate increases rapidly with . The apparent meridional motion is poleward for with a very small amplitude ( 5 m s^{–1}), while, suprisingly, it is slightly equatorward for greater time lags. The apparent rotation of supergranulation obtained by tracking the divergence maps confirms the original findings of Duvall Jr (1980) and Snodgrass and Ulrich (1990) obtained by tracking Dopplergrams.
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Beck and Duvall Jr (2001) showed that the observed correlation function of the divergence signal switches sign at . The same analysis is repeated here with the 60day sequence used by Gizon and Duvall Jr (2003). Figure 64 shows a still at from a movie of the temporal evolution of the spatial correlation, with varying from zero to 5.5 d (equatorial region, 6 hr time steps, Carrington tracking rate). The spatial crosscorrelation decays and oscillates as a function of time. For , the peak of maximum correlation is well defined and is surrounded by a ring of negative correlation due to adjacent supergranules. For a peak of negative correlation has appeared (east), which is surrounded by a ring of positive correlation. When the analysis is repeated away from the equator, one finds that the peak of negative correlation forms poleward of the east direction. For the main correlation peak is again positive and is surrounded by a ring of negative correlation.
Earlier observations of solar convection assumed that supergranulation can be characterized by an autocorrelation function that exhibits a simple exponential decay in time (Harvey, 1985; Kuhn et al., 2000). It is now obvious that supergranulation does not follow such a simple model. The oscillation period of the correlation function, of the order of 6 d, suggests an underlying longrange order. As shown below, these puzzling observations are easier to describe in Fourier space (Section 5.3.5).
Another interesting method of analysis to study the evolution of the supergranulation pattern is described by Lisle et al. (2004). They used horizontaldivergence maps obtained from local correlation tracking of granules (1 min time lag). Lisle et al. (2004) constructed temporal averages (8 d) of the divergence maps for different tracking velocities . The spatial variations of a 15° × 15° section of the timeaveraged divergence map were characterized by the rms values in the eastwest () and southnorth () directions. Lisle et al. (2004) found that at the equator the ratio is maximum at a tracking velocity which is 110 m s^{–1} above the Carrington velocity, i.e., faster than any rotation rate measured so far. They suggested that a ratio greater than 1 is an indication that supergranules are preferentially aligned in a northsouth direction. Junwei Zhao has recently repeated their analysis using timedistance divergence maps. Figure 66 and the companion Movie ?? shows a 15° × 15° timeaveraged divergence map for various tracking velocities. Figure 66 shows the ratio at the equator as a function of tracking velocity, averaged over all available data. The maximum occurs at a tracking rate 123 m s^{–1} above the Carrington velocity. It is evident that there is another local maximum 8 m s^{–1} below the Carrington velocity. This local maximum could only be hinted at from the plots of Lisle et al. (2004) due to lack of averaging. We will come back to this observation near the end of the next section.
The 3D power spectrum of the divergence signal was studied first by Gizon et al. (2003). The same analysis was extended by Gizon and Duvall Jr (2004) to cover the period from 1996 to 2002. They considered series of MDI fulldisk Dopplergrams from the Dynamics campaigns (two to three months each year). Dopplergrams were tracked at the Carrington angular velocity to remove the main component of rotation. Every 12 hr, a 120° × 120° map of the horizontal divergence of the nearsurface flows was obtained using fmode timedistance helioseismology. At a given target latitude a longitudinal section of the data was extracted, 10° wide in latitude. Gizon and Duvall Jr (2004) rearranged the data in a frame of reference with angular velocity (rotation rate of small magnetic features; Komm et al., 1993b). This choice of reference is convenient, although arbitrary. The position vector is denoted by in the neighborhood of latitude , where coordinate is prograde and is northward, with a spatial sampling of 2.92 Mm in both coordinates.

The divergence signal was decomposed into its harmonic components through FFT, where is the angular frequency and is the horizontal wavevector. The power spectrum of the horizontal divergence signal, denoted by , was computed for each target latitude with 5° steps. Gizon et al. (2003) discovered that, in the range with , the power spectrum of the solar divergence signal can be described accurately by the following parametric model:
where and The functions , , , , and , described below, were determined from fits to the data. The fits took into account the convolution of the solar power spectrum with the power in the observation window. The representation of in terms of is convenient as it implies , i.e., the divergence signal is real.At fixed , the power spectrum of the horizontal divergence can be described by the sum of two Lorentz functions with independent amplitudes and and with central frequencies and respectively. The function depends on the direction of and can be parametrized as follows:
where the azimuth is the direction of the wavector such that , e.g., when points prograde and when points north). The component includes instrumental astigmatism: a purely spatial distortion which can not easily be separated from a real solar signal. The component, however, must be of solar origin. The power anisotropy may be defined by the ratio , while was called the azimuth of maximum power. The background noise , which is small, was assumed to be independent of frequency but affected by astigmatism. The half width at half maximum of the Lorentz profile corresponds to a characteristic folding lifetime . The frequency shift was interpreted to be a Doppler shift produced by a horizontal flow measured in the frame of reference (rotating at ), as is done in ringdiagram analysis (Section 4.2.2).Figure 67 shows a cyclindrical cut in the equatorial power spectrum at a constant typical of the supergranulation, and a model fit to the data according to Equations (95, 96, 97). For each azimuth , the power has two peaks at frequencies and . Observations show that the difference is independent of azimuth (Gizon et al., 2003). No Galilean transformation can cause these peaks to coalesce, at zero frequency or otherwise. This implies that supergranulation undergoes oscillations. In the plane , power is distributed along two parallel sinusoids at frequencies . The presumption by Rast et al. (2004) that power is distributed along two intersecting sinusoids must be rejected: It is inconsistent with the observations. As one does in helioseismological ring analysis (Schou and Bogart, 1998), it is natural to interpret the frequency shift as a Doppler shift produced by an advective flow (some average flow in the supergranulation layer). The weak dependence of the measured in the range is consistent with this interpretation. Furthermore the functional form of the power is preserved for different tracking rates (i.e., different latitudes). As shown in Figure 68, it is remarkable that the inferred rotation and meridional circulation are both similar to that of the small magnetic features (Komm et al., 1993b,c). This property is consistent with the view that magnetic fields are advected by supergranular flows. In addition, the torsional oscillations are seen with the same phase and amplitude as in global helioseismology. The temporal changes in the meridional circulations are also consistent with an inflows toward the mean latitude of activity (see Section 5.3.4).

The dynamics of the supergranulation is best studied once the background flow has been removed. In a comoving frame, each spatial component oscillates at a characteristic frequency . There is a clear relationship between and the wavenumber , well described by a power law (see panel (a) of Figure 69):
This is a fundamental relationship as it is measured to be essentialy independent of , , and the phase of the solar cycle. The data are consistent with a spectrum of traveling waves with a dispersion relation . The azimuthally averaged power spectrum is shown in panel (b) of Figure 69. In the range , the peak of power away from zero temporal frequency is well resolved since the quality factor is larger than 1. Near we have . The variations of the oscillation frequency with latitude and time are less than 5% during the period 1996 – 2002 for . Since and the dominant size of supergranules are observed to be essentially independent of latitude, the general dynamics determining the time scale and the spatial scale of supergranulation is not affected by the Coriolis force associated with the large scale vorticity (rotation). However, there is a pronounced anisotropy in the azimuthal distribution of wave power at fixed . The power anisotropy is measured to be for . Furthermore, power is maximum in a direction equatorward of the prograde direction in both hemispheres: For observations show , and for . The pattern therefore senses the effect of rotation. A snapshot of the divergence field would not reveal this as the sum of the powers measured in opposite directions is nearly isotropic. We recall that the effect of rotation on supergranulation was detected in the vorticity field.The lifetime of supergranules is about 2.3 d at , i.e., a somewhat larger value than other estimates derived from the decay of the autocorrelation function. This is because the lifetime is not strictly given the decay of the correlation function at short time lags, but by the decay of the envelope of the correlation function, which oscillates. The lifetime decreases by about 20% at active latitudes (Gizon and Duvall Jr, 2004).
As mentioned earlier, estimates of supergranulation rotation obtained by tracking the Doppler pattern are systematically found to be higher than the rotation of the magnetic network (for large time lags, see panel (a) of Figure 63). This apparent superrotation of the pattern can now be understood as the result of the waves being predominantly prograde. The eastwest motion of the pattern is effectively a powerweighted average of the true rotation and the nonadvective phase speed for . Similarly, the excess of wave power toward the equator is reflected in the equatorward meridional motion of the pattern at large time lags (see panel (b) of Figure 63) even though the advective flow is poleward. Correlation tracking measurements must take into account the fact that the pattern propagates like a modulated traveling wave.
The measurement of the pattern rotation obtained by Lisle et al. (2004) is also a direct consequence of the power spectrum. It is straightforward to show that the ratio (see Section 5.3.4) can be expressed in terms of alone. Plugging in Equations (95, 96, 97), one finds that at the equator the variations of as a function of tracking velocity should display two peaks at , where is the dominant wave speed and is the advective velocity. The amplitudes of the peaks are approximately given by
using mean values and . The separation between the two peaks, , is precisely the observed value (Figure 66). The superrotation of the pattern obtained by Lisle et al. (2004), , is understandably faster than measured by other methods, and faster than which is close to the magnetic feature rate. Figure 66 is not only a direct consequence of the observed power spectrum, but it is additional evidence for the existence of prograde and a retrograde components (in a frame rotating at ) as well as excess power in the prograde direction. Astigmatism () would also affect the ratio , but it was neglected in Equation (100) for the sake of simplicity. It is worth noting that is not a sufficient condition to imply that supergranules are aligned in the northsouth direction, since depends on only (phases are needed for pattern characterization).It was a source of concern that the wavelike properties of supergranulation had not been seen previously in surface Doppler shifts. Schou (2003b) showed that the same phenomenon can be observed directly using MDI Dopplergrams, thereby confirming the observations of Gizon and Duvall Jr (2003). In addition to confirming those results, Schou (2003b) was able to extend the measurements for the rotation and meridional velocities, and , beyond 70° latitude. We note that, earlier, Beck and Schou (2000) had also used a spectral method to estimate the equatorial rotation of supergranulation from surface Doppler images; this method, however, was incorrect as it did not take account of the full complexity of the power spectrum.
All the evidence shows that supergranulation displays a high level of organization in space and time. Although no serious explanation has been proposed yet, it would seem that supergranulation is an example of travelingwave convection. The prograde excess of wave power is perhaps due to the influence of rotation (or rotational shear) that breaks the eastwest symmetry, allowing for new instabilities to propagate (see, e.g., Busse, 2003, 2004). We note that convection in oblique magnetic fields also exhibits solutions that take the form of traveling waves (Hurlburt et al., 1996). Future work should focus on measuring the evolution of the pattern at different depth in the interior and the phase relationship between the different Fourier components. More than forty years after its discovery, supergranulation remains a complete mystery.
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