## 1. Introduction

The numerical modeling of turbulent flows has advanced steadily since the advent of the large-scale digital computer in the 1960s. In smaller-scale meteorological applications today (domain sizes from 3000 to 1 km, say) one can identify two broad classes of such modeling: mesoscale modeling on the larger domains and large-eddy simulation (LES) on the smaller ones. Their fundamental difference is the value of *l*/Δ, the ratio of the energy-containing turbulence scale and the scale of the spatial filter used on the equations of motion. In traditional mesoscale modeling *l*/Δ is small, so none of the turbulence is resolved; in traditional LES it is large, so the energy- and flux-containing turbulence is resolved.

Until recently the *l*/Δ values and spatial domains of mesoscale modeling and LES were nonoverlapping; the horizontal area of a typical boundary layer LES domain could fit within the horizontal grid mesh square of a typical mesoscale model. Today's computers allow as many as 10^{9} numerical grid points in dynamical models (Gotoh and Fukayama 2001), so it is now possible to do very-fine-mesh mesoscale modeling with *l*/Δ ∼ 1. This is approaching the *l*/Δ range traditionally used in the relatively coarse-resolution LES of severe storms. Since neither LES nor mesoscale modeling was designed to operate in this *l*/Δ range, we shall term it the “terra incognita.”

## 2. Three-dimensional simulation of turbulent flow

### a. The foundations

Numerical simulation of high-Reynolds-number turbulent flows is possible only after the equations of motion have been spatially smoothed to remove the fine structure of their solution fields. Lilly's (1967) derivations of the smoothed equations and the equation for the Reynolds stress that results provided the basis of what is known today as large-eddy simulation, or LES.

*h,*denoting the averaging by an overbar:

*F*

*filtered*variable and

*G*the filter function. Following today's convention we shall designate the spatial scale of the filter function as Δ.

*τ*

_{ij}is a deviatoric (meaning its isotropic value has been subtracted) kinematic Reynolds stress defined as

*e*the kinetic energy per unit mass of the small-scale motions that have been removed by the filtering,

*τ*

_{ij}has zero trace (i.e.,

*τ*

_{ii =}0), which aids its modeling. We shall call

*τ*

_{ij}simply the subfilter-scale (SFS) stress and

*e*the SFS energy.

*τ*

_{ij}is derived. With the assumption of local isotropy its viscous dissipation term vanishes and Lilly's

*τ*

_{ij}equation reduces to

*τ*

_{ij}, an eddy-viscosity model:

*K*a scalar function of the filtered flow variables. He adopted Smagorinsky's (1963) form for

*K*:

*K*

*k*

^{2}

*D*

*k*a constant and

*D*= (

*D*

_{ij}

*D*

_{ij})

^{1/2}. Lilly chose Δ to lie in the inertial range of scales so that he could connect the eddy-viscosity closure (9), (10) with Kolmogorov's (1941) inertial-range form of the three-dimensional turbulent energy spectrum

*E*(

*κ*),

*E*

*κ*

*α*

^{2/3}

*κ*

^{−5/3}

*k*

*α*

^{−3/4}

*τ*

_{ij}conservation equation (8) under two simplifications. The first is the neglect of its time-change, advection, flux-divergence, and second production term. The second is modeling its pressure-destruction term as −

*τ*

_{ij}/

*T,*with

*T*a time scale; this is the simplest model that ensures its being a sink. In this limit (8) becomes a two-term balance that yields Lilly's closure (9),

### b. The mesoscale and LES limits of the filtered equations

Lilly's evolution equation (8) for SFS stress holds for any averaging that commutes with differentiation. That includes not only spatial averaging and its generalization by Leonard (1974) to spatial filtering, but also the ensemble averaging of classical turbulence analysis. In an unbounded, homogeneous field low-pass spatial filtering converges to ensemble averaging as Δ → ∞. (In a flow that is homogeneous in the horizontal but not the vertical, we restrict the averaging to the horizontal plane.) Thus, we can explore both types of averaging by allowing the filter scale Δ to vary, subject only to the restriction that it be much larger than the scale of the dissipative eddies so that the molecular-diffusion terms in the filtered equations are negligible.

We shall call the case *l* ≫ Δ, with *l* the integral scale of the turbulence, the “LES limit.” Here the energy and flux-containing turbulence is contained in the filtered equation of motion (4), as sketched in Fig. 1. The “mesoscale limit” *l* ≪ Δ is reached in mesoscale modeling. [Adding a Coriolis term to Eq. (4) presents no complications, since that term is linear in velocity, so we shall not indicate it explicitly. In general, there is also a buoyancy term, but it does not change the central issues, and we shall neglect it as well.] In mesoscale modeling the grid-mesh element is typically much smaller in the vertical direction than in the horizontal in order to resolve some structure in the boundary layer. But, since resolving three-dimensional turbulence requires a grid mesh that is smaller than *l* in all three directions, even with fine vertical resolution essentially none of the turbulence is resolved in the mesoscale limit. The turbulence resides in the SFS fields, as also sketched in spectral terms in Fig. 1.

Equation (8) is the evolution equation for the SFS stress *τ*_{ij} in LES, in mesoscale modeling, and for applications over the range of scales in between. Even though in high-resolution LES the turbulent kinetic energy and fluxes are carried almost entirely by the filtered motion, the *τ*_{ij} term in (4) remains important. It is essential to the transfer of kinetic energy and scalar variance from the filtered to subfilter scales (Wyngaard 2002). Thus, reliable models of *τ*_{ij} and *e* are required also in the LES limit.

## 3. The flux conservation equations as guides for SFS modeling

### a. A conserved scalar

*c*in a constant-density flow is

*f*

_{i}the SFS flux of the scalar.

*c*

*χ*the rate of destruction of

*c*

^{2}

*f*

_{i}. In a constant-density flow it can be written as

*f*

_{i}by the filtered velocity; the third term is the divergence of the SFS flux of

*f*

_{i}. The first term on the right is the rate of production of

*f*

_{i}through the amplification and rotation of scalar flux by the filtered velocity gradient; it is the counterpart of the “stretching and tilting” term in the vorticity equation. The second term on the right represents the rate of production through the interaction of an SFS stress and the filtered scalar gradient. The third term is the gradient of a pressure covariance, and the final term represents the interaction of pressure and

*c*gradients. The neglected molecular term is quite small in large Reynolds number turbulence (Wyngaard et al. 1971).

*f*

_{i}around in space, and the first two terms on the right are source terms. Thus, the role of principal sink for scalar flux falls to the pressure,

*c*-gradient term. The simplest model for it is −

*f*

_{i}/

*T,*with

*T*a time scale of the SFS turbulence. Making all but one of the assumptions that Lilly used to simplify Eq. (8) for

*τ*

_{ij}—that is, dropping the time-change, advection, and flux-divergence terms but retaining all the production terms—yields the closure

*gradient-diffusion*term.

*l*≪ Δwe can interpret the overbar as an ensemble average, which we shall denote by angle brackets. Then with the classical decomposition into ensemble-mean plus fluctuating parts,

*c*= 〈

*c*〉 +

*c*′ =

*C*+

*c*′,

*u*

_{i}= 〈

*u*

_{i}〉 +

*u*

^{′}

_{i}

*U*

_{i}+

*u*

^{′}

_{i}

*f*

_{i}= 〈

*c*′

*u*

^{′}

_{i}

*R*

_{ij}= 〈

*u*

^{′}

_{i}

*u*

^{′}

_{j}

*T*∼

*l*/

*e*

^{1/2}, a time scale of the energy-containing turbulence. The horizontal flux 〈

*c*′

*u*

^{′}

_{1}

*l*≫ Δ the time scale

*T*is of the order of Δ/

*e*

^{1/2}, a time scale of inertial-range turbulence. In LES practice only the gradient-diffusion contribution to Eq. (19) is typically retained, however. Deardorff (1973) rationalized this by assuming the isotropic forms

*f*

_{j}= 0,

*R*

_{ij}= 2

*eδ*

_{ij}/3 on the right side of Eq. (19), thereby eliminating all but the gradient-diffusion term and yielding Eq. (21) with

*K*∼

*e*

^{1/2}Δ.With this standard eddy-diffusivity model for

*f*

_{i}the rate of transfer of squared scalar from filtered to SFS scales, the last term in Eq. (17), is positive definite:

*f*

_{j}and

*R*

_{ij}in Eq. (19) in the LES limit since these are locally averaged quantities, not ensemble means, and are not subject to the constraints of isotropy. The assumption is also inappropriate in the mesoscale limit, where

*f*

_{j}and

*R*

_{ij}are energy-containing-range properties and necessarily anisotropic. Thus, the simplest consistent model of SFS scalar flux that seems applicable across the scale range is Eq. (19). With that fuller model of

*f*

_{i}the rate of transfer of squared scalar becomes

*f*

_{i}can be formally written as

*K*

_{ij}an eddy diffusivity that depends on

*T,*∂

*u*

_{i}/∂

*x*

_{j}, and

*R*

_{ij}. Unlike the usual eddy diffusivity, which is a scalar quantity,

*K*

_{ij}is a second-order tensor. Rather than solving Eq. (19) for

*K*

_{ij}, however, it could be more convenient to carry the time-dependent form,

*T*is of order

*l*/

*e*

^{1/2}and Δ/

*e*

^{1/2}in the mesoscale and LES limits, respectively. Its proportionality to Δ/

*e*

^{1/2}in the LES limit can be determined in the general way laid out by Lilly (1967). Its proportionality to

*l*/

*e*

^{1/2}away from the LES limit would probably be different in unstable and stable stratification, as is typically found in other closures.

### b. Stress

*u*

_{i}and rearranging:

*p** =

*p*/

*ρ*+ 2

*e*/3 is a modified kinematic pressure. The right side of (27) represents the rate of energy transfer between filtered and subfilter scales. With the eddy-diffusivity closure (9) for

*τ*

_{ij}, this energy-transfer term becomes

*K*closure (9) is commonly used in LES, where this one-way energy transfer is computationally advantageous. However, the equilibrium values of

*τ*

_{ij}components implied by (9) can be unphysical in the mesoscale limit. In the neutral atmospheric surface layer, for example, (9) yields 〈

*u*′

^{2}〉 = 〈

*υ*′

^{2}〉 = 〈

*w*′

^{2}〉 = 2

*e*/3, which agrees poorly with observations.

*τ*

_{ij}uses the quasi-steady, homogeneous form of its conservation equation (8):

*T*again a time scale of the SFS turbulence. This yields the closure

*T*can be made to agree well with observations.

### c. Relation to previous work

In Deardorff's early applications of what we now call LES to turbulent channel flow (Deardorff 1970) and to the planetary boundary layer (Deardorff 1972), the SFS model was the eddy-diffusivity closure of Eq. (9) with Smagorinsky's form, Eq. (10), for the eddy diffusivity *K.* Computational difficulties forced him to treat the stably stratified capping inversion in the latter work as a rigid lid. In his next study he developed a new SFS model based on the SFS flux conservation equations and was able to treat the capping inversion directly (Deardorff 1973). Findikakis and Street (1979) later reduced these SFS flux conservation equations to an “algebraic model,” much as we have outlined here, specifically for application of LES to stratified flows. Schmidt and Schumann (1989) used such an algebraic SFS model in LES of the convective boundary layer.

The SFS flux conservation equations improved the simulations, Deardorff (1973) said, although they increased the computation time by about a factor of 2.5. He used them also in his simulations of the Wangara boundary layer experiment (Deardorff 1974a,b). But later, in simulating cloudy boundary layers, he returned to the *K* model (Deardorff 1980), but now taking *K* ∼ Δ*e*^{1/2} and using an adjustment to allow smaller SFS length scales in stable stratification. The model included a conservation equation for the SFS turbulent kinetic energy *e,* which Schumann (1975) had earlier used in simulations of turbulent duct flow. SFS models of this *K*–*e* type are now standard in LES codes for atmospheric applications. Moeng et al. (1996), for example, reported an intercomparison of 10 atmospheric LES codes, all of which used versions of this model.

The engineering fluid-mechanics community, which coined the name *large-eddy simulation* after Deardorff's (1970) channel-flow study appeared, has had a rather different experience with LES. They applied it to a variety of flows and confirmed the soundness of its conceptual basis, but they also compared LES with direct numerical simulations of low Reynolds number turbulent flows (Clark et al. 1979). They found that the eddy-diffusivity SFS model represents the interaction between resolved and subfilter-scale turbulence well on average but poorly in detail. There ensued, and continues today, a long series of attempts to improve SFS modeling for engineering applications. A milestone here was the *dynamic model* (Germano et al. 1991), which attempts to compute the coefficient in the eddy-diffusivity SFS model (9) dynamically as the calculation progresses. Meneveau and Katz (2000) discussed this and related approaches to SFS modeling for LES. Piomelli and Balaras (2002) reviewed recent progress in LES of boundary layer flows, focusing on the use of separate SFS models adjacent to the surface.

The mesoscale modeling community has evidently done less experimentation with their SFS models. It seems that virtually all mesoscale models use the *K* closure, often with a conservation equation for the SFS turbulent kinetic energy.

### d. Analysis of HATS observations

The Horizontal Array Turbulence Study (HATS; Sullivan et al. 2003) provided data useful for studying the behavior of the SFS flux conservation equations in the atmospheric surface layer. HATS employed the anemometer-array technique developed by Tong et al. (1998, 1999)—a pair of horizontal, crosswind arrays of sonic anemometers mounted parallel to the surface. Filtering in time (with Taylor's hypothesis to convert to streamwise spatial filtering) and in the crosswind direction allows the turbulence fields to be decomposed into filtered and SFS parts. The data presented here were gathered in daytime conditions with slightly to moderately unstable conditions in runs ranging from 25 to 50 min long. Four array geometries were used in order to obtain a range of *l*/Δ values. We chose *l* as the streamwise integral scale of the vertical velocity field, which the Kansas observations (Kaimal et al. 1972) showed is to a good approximation 5*z* (*z* is the distance above the surface) under the conditions of our HATS runs; Δ is the scale of the filter in the horizontal plane.

We will briefly discuss some results for the SFS scalar-flux equation (18) with the scalar taken as potential temperature. In the LES limit each quantity in the flux-production term, which is expanded in Eq. (20), fluctuates about its ensemble-mean value. Figure 2 shows, for each component of scalar flux, the ratio of the rms (about the mean) value of its full, six-term production rate and the rms value of its gradient-diffusion part. For each of the SFS flux components this ratio is appreciably larger than 1.0, indicating that the fluctuating production term for SFS flux is not dominated by its gradient-diffusion part. Thus it appears that the simplest reasonable model of SFS scalar flux in the near-neutral surface layer is not the commonly used gradient-diffusion form (21), but rather the more general form (19).

The dashed line in Fig. 2 is the result of a simplified analytical calculation in the LES limit. The calculation approximates the *f*_{i} in the production term by its gradient-diffusion form, assumes that the fourth moments that result when the expression is squared and averaged are related to second moments as for a Gaussian process, uses second-moment conservation equations to relate the third moments that result to second moments, and uses isotropic forms for second moments. As Fig. 2 shows, the numerical result of this calculation agrees well with the HATS data in the LES region, *l*/Δ ≃ 10.

We tested the more general SFS model (19) by comparing the SFS fluxes and scalar variance transfer rates it yields with those observed in HATS. In so doing we ignored the buoyancy term in the *f*_{3} equation, whose net mean effect averaged about 20% of the mean value of the production term. In (19) we chose *T* = *Ce*^{1/2}/Δ, with *C* = 0.3. We used the time series of *e* and the gradients of filtered velocity and temperature observed in HATS to drive the model in its unsteady form, Eq. (26). In general, the advection term is also active in this equation, but it was not measured in the HATS experiment. Under horizontally homogeneous conditions it averages to zero, however, so its neglect here is presumably not serious. Figure 3 compares all three components of the run-averaged SFS temperature fluxes measured in HATS against the predictions of Eq. (26). The agreement is quite good.

Figure 4 shows a comparison of the run-averaged values of the rate of temperature variance transfer, −2*f*_{i}∂*θ**x*_{i}, from the model (26) and the HATS observations. Again the agreement is quite good. Figure 5 shows the fraction of the values of the rate of temperature variance transfer that are positive—that is, represent transfer from large scales to smaller—for the HATS observations and the model (26). Again the agreement is fairly good.

The ratio of the rms value of the six-term production rate of *f*_{i} and the rms value of ∂*f*_{i}/∂*t* from HATS is plotted in Fig. 6. The rms time derivative is considerably larger than the rms production rate, particularly at the larger values of *l*/Δ. This indicates that the advection term in the full conservation equation (18) for *f*_{i} is the principal contributor to fluctuations in ∂*f*_{i}/∂*t.* Since the rms vertical advection is generally smaller than the rms production rate (Fig. 7), we conclude that horizontal advection is the primary source of these fluctuations. This suggests that without knowledge of the horizontal advection terms more detailed evaluations of SFS models, for example comparisons of measured and modeled time series, might not be possible.

### e. Implications for numerical modeling

The SFS turbulence closures typically used in mesoscale models and LES today are of the same form— a scalar eddy-diffusivity model with *K* ∼ *e*^{1/2}*l*^{s}, with *l*^{s} taken as *l* in mesoscale models and Δ in LES. We showed from the HATS data, however, that the simplified conservation equation for SFS scalar flux implies a tensor eddy diffusivity, not a scalar one (Fig. 2).

In high-resolution LES, where *l* ≫ Δ, the SFS model carries little turbulent flux; its principal role is extracting energy and scalar variance from the filtered scales. The scalar diffusivity SFS model is quite effective in this transfer role and it seems doubtful that the additional production terms included in the tensor-diffusivity model (19) would have any strong effects. This is consistent with the early LES experience of Deardorff, who returned to the *K*-closure SFS model after several years of experience with SFS flux conservation equations, and the more recent experience of Schmidt and Schumann (1989), who commented that (except in inversion layers) their algebraic SFS model is of “minor importance” in high-resolution LES. Mason (1994) also mentioned the insensitivity of current LES to the SFS model, except near boundaries or in statically stable regions. Juneja and Brasseur (1999) discussed the deficiencies of the standard SFS model in reproducing near-surface structure. Thus, it seems that high-resolution LES in atmospheric applications would not benefit significantly from our more general SFS model except possibly very near surfaces and in stably stratified regions.

In coarse-resolution mesoscale modeling turbulent fluxes are carried entirely by the SFS model. Here the SFS models are typically developed, calibrated, and evaluated for *l* ≪ Δ, often in one-dimensional form (Ayotte et al. 1996). In effect they produce ensemble-mean fluxes, which in the atmospheric boundary layer are maintained by mean gradients in the vertical. The standard scalar eddy-diffusivity model can be tuned to represent these fluxes fairly well. Although the eddy diffusivity for conserved scalars in the convective boundary layer is not well behaved (Wyngaard and Weil 1991), a “nonlocal term” can be added (Stevens 2000) to accommodate much of this behavior as well. Thus, the tensor eddy-diffusivity model (19) is perhaps also not necessary in coarse-resolution mesoscale modeling.

But in LES near the surface, in most LES of severe storms (Bryan et al. 2003), and in very high resolution mesoscale modeling, *l* is typically of the order of Δ. In such applications, which lie outside the original design ranges of both mesoscale modeling and LES, the SFS model carries appreciable flux in an environment in which all three components of resolved gradients can be significant. In this “terra incognita” the tensor eddy-diffusivity SFS model could impact both LES and mesoscale modeling.

## 4. The terra incognita: Dynamical modeling with *l* ∼ Δ

### a. A unified closure concept

*τ*

_{ij}through a scalar eddy viscosity

*K*∼

*e*

^{1/2}

*l*

^{s}and assume the filtered flow is statistically homogeneous in the horizontal and has velocity and length scales

*U*and

*L.*The effective Reynolds number Re of the filtered flow is therefore of order

In the mesoscale limit *e*^{1/2} and *l*^{s} are *u* and *l,* the scales of the turbulence, which in turn are of the order of (but less than) *U* and *L,* the scales of the filtered flow. The Reynolds number of the filtered flow is then *O*(1) and most likely below the value required for transition to turbulence. Thus, mesoscale model output fields are nonturbulent.

*l*

^{s}is the filter scale Δ, which lies in the inertial subrange, so we can write

*u*

^{3}/

*l*(Tennekes and Lumley 1972) this yields

*l*/Δ ≫ 1, we expect LES output fields to be turbulent. The transition to turbulence evidently occurs in the scale region

*l*∼ Δ.

*l*in the mesoscale limit and Δ in the LES limit. As suggested by Cuxart et al. (2000), it is natural to use a single closure with its length scale transitioning between

*l*on the mesoscale side and Δ on the LES side (Fig. 8):

*l*

^{s}

*l,*

*l*

*l*

^{s}

*l.*

### b. The roles of buoyancy and turbulent transport

The experience with second-order closure (Zeman 1982) suggests that the conservation equations for SFS flux and energy described in section 3 need buoyancy and turbulent-transport (third-moment flux divergence) terms to be optimally useful in geophysical applications. Including buoyancy is straightforward: through the Boussinesq approximation, for example, the equation of motion gains a buoyant acceleration term *g*Θ/Θ_{0}, with *g* the acceleration of gravity, Θ_{0} a background potential temperature profile, and Θ a deviation from this profile. This generates buoyant-production terms in Eqs. (8) for SFS stress *τ*_{ij} and (18) for SFS scalar flux *f*_{i}. These are known to be quite important when *l* ≪ Δ—that is, in mesoscale and ensemble-mean modeling (Zeman 1982).

*l*is of the order of or greater than Δ as follows. We define

*θ*(Δ), the intensity scale of temperature fluctuations of spatial scale Δ, as the rms value of the fluctuating part of

*u*(Δ). Their Kolmogorov inertial-range scaling is (Tennekes and Lumley 1972)

*θ*and

*u.*That is, using ε ∼

*u*

^{3}/

*l,*

*χ*∼

*θ*

^{2}

*u*/

*l,*Eq. (37) yields for

*l*= Δ

*u*

*l*

*u,*

*θ*

*l*

*θ.*

*u*(Δ), and

*θ*(Δ), their ratio becomes a scale-dependent turbulent Richardson number:

*l*∼ Δ, this turbulent Richardson number is of the order of Ri

_{e}, that for the energy-containing range, which in atmospheric turbulence can be

*O*(1) in both stable and unstable stratification (Wyngaard 1992). If

*l*≫ Δ, Eq. (40) says the direct effects of buoyancy on the SFS turbulence budgets are small.

Figure 9 shows the ratio of rms value of the buoyancy term in the *f*_{3} equation and the rms value of the sum of its six production terms, as evaluated from the HATS data. The relative importance of buoyancy effects decreases with increasing *l*/Δ, much as predicted.

*II,*say) has the general form

*l*

^{s}∼

*l*and Δ in the mesoscale and LES limits, respectively. The most complex approach is to write the rate equation for

*III,*

### c. Caveat: An alternative averaging operator

One could use the ensemble average in mesoscale modeling; indeed, at small enough *l*/Δ it can be indistinguishable from a spatial average. But the difference is profound at *l*/Δ ∼ 1. In that *l*/Δ range spatial-averaged fields become turbulent, while ensemble-averaged fields are nonturbulent. Unless mesoscale modelers are inadvertently obtaining high-resolution but nonturbulent solutions through use of an incorrect turbulence closure (e.g., ensemble-averaged rather than spatially averaged), this is perhaps not an important issue in meteorology.

Ensemble-averaged models have traditionally been used to predict the dispersion of pollutants because a principal concern has been longer-term public health issues associated with releases of toxic effluents from continuous sources—for example, factory stacks. But to predict the dispersion of toxics from instantaneous releases (such as in terrorist attacks) one needs more than the ensemble-averaged result; one needs also the likely behavior in individual realizations (National Research Council 2003). The latter requires spatial-averaged models.

## 5. Summary and conclusions

Lilly's (1967) conservation equation for SFS stress, and that derived later for SFS scalar flux, provide a foundation for SFS modeling for both mesoscale model and LES applications. These equations have several production terms, one of which produces the commonly assumed downgradient diffusion model of SFS flux. Our analyses of data from the HATS surface-layer array experiment indicate that the other SFS flux production terms can also be important. If so, this implies that the simplest SFS model consistent with the SFS flux conservation equations has a tensor rather than scalar eddy diffusivity. We suggest that the tensor nature of this diffusivity is probably not important in high-resolution LES or in low-resolution mesoscale modeling. It could be important in the terra incognita, *l* ∼ Δ, which occurs in both very fine mesh mesoscale modeling and coarse-mesh LES.

## Acknowledgments

The author is grateful to Tom Horst, Peter Sullivan, Chenning Tong, George Young, and many colleagues in Penn State's Center for High-Resolution Atmospheric Regional Modeling (CHARM) for valuable discussions that stimulated the development of the ideas in this paper; to Jingyung Wang for expertly, cheerfully, and tirelessly carrying out the HATS analyses; and to the referees for helpful comments. This work was supported in part by the National Science Foundation under Grant ATM-0222421.

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The ratio of the rms value of the full production rate of SFS scalar flux, Eq. (20), and the rms value of its gradient-diffusion term vs *l*/Δ. The dashed line in the LES limit (right) is based on a simplified analytical calculation (section 3d). Data are from the HATS experiment (Sullivan et al. 2003)

Citation: Journal of the Atmospheric Sciences 61, 14; 10.1175/1520-0469(2004)061<1816:TNMITT>2.0.CO;2

The ratio of the rms value of the full production rate of SFS scalar flux, Eq. (20), and the rms value of its gradient-diffusion term vs *l*/Δ. The dashed line in the LES limit (right) is based on a simplified analytical calculation (section 3d). Data are from the HATS experiment (Sullivan et al. 2003)

Citation: Journal of the Atmospheric Sciences 61, 14; 10.1175/1520-0469(2004)061<1816:TNMITT>2.0.CO;2

The ratio of the rms value of the full production rate of SFS scalar flux, Eq. (20), and the rms value of its gradient-diffusion term vs *l*/Δ. The dashed line in the LES limit (right) is based on a simplified analytical calculation (section 3d). Data are from the HATS experiment (Sullivan et al. 2003)

Citation: Journal of the Atmospheric Sciences 61, 14; 10.1175/1520-0469(2004)061<1816:TNMITT>2.0.CO;2

Run-averaged SFS scalar flux calculated with the model of Eq. (26) compared with the HATS observations

Run-averaged SFS scalar flux calculated with the model of Eq. (26) compared with the HATS observations

Run-averaged SFS scalar flux calculated with the model of Eq. (26) compared with the HATS observations

Run-averaged values of the rate of scalar variance transfer −2*f*_{i}(∂*θ**x*_{i}) compared with the HATS observations

Run-averaged values of the rate of scalar variance transfer −2*f*_{i}(∂*θ**x*_{i}) compared with the HATS observations

Run-averaged values of the rate of scalar variance transfer −2*f*_{i}(∂*θ**x*_{i}) compared with the HATS observations

Fraction of the instantaneous values of the rate of scalar variance transfer −2*f*_{i}(∂*θ**x*_{i}) that are positive, compared with the HATS observations

Fraction of the instantaneous values of the rate of scalar variance transfer −2*f*_{i}(∂*θ**x*_{i}) that are positive, compared with the HATS observations

Fraction of the instantaneous values of the rate of scalar variance transfer −2*f*_{i}(∂*θ**x*_{i}) that are positive, compared with the HATS observations

The ratio of the rms value of the full production rate of SFS scalar flux and the rms value of ∂*f*_{i}/∂*t* vs *l*/Δ. Data are from the HATS experiment

The ratio of the rms value of the full production rate of SFS scalar flux and the rms value of ∂*f*_{i}/∂*t* vs *l*/Δ. Data are from the HATS experiment

The ratio of the rms value of the full production rate of SFS scalar flux and the rms value of ∂*f*_{i}/∂*t* vs *l*/Δ. Data are from the HATS experiment

The ratio of the rms value of the vertical advection of scalar flux and the rms value of its full production rate, Eq. (20), vs *l*/Δ. Data are from the HATS experiment

The ratio of the rms value of the vertical advection of scalar flux and the rms value of its full production rate, Eq. (20), vs *l*/Δ. Data are from the HATS experiment

The ratio of the rms value of the vertical advection of scalar flux and the rms value of its full production rate, Eq. (20), vs *l*/Δ. Data are from the HATS experiment

The suggested behavior of the length scale *l*^{s} of the unresolved turbulence as a function of the filter scale Δ. On the LES side, 0 ≤ Δ ≤ *l,* *l*^{s} = Δ. On the mesoscale side, *l* ≤ Δ ≤ ∞, *l*^{s} = *l*

The suggested behavior of the length scale *l*^{s} of the unresolved turbulence as a function of the filter scale Δ. On the LES side, 0 ≤ Δ ≤ *l,* *l*^{s} = Δ. On the mesoscale side, *l* ≤ Δ ≤ ∞, *l*^{s} = *l*

The suggested behavior of the length scale *l*^{s} of the unresolved turbulence as a function of the filter scale Δ. On the LES side, 0 ≤ Δ ≤ *l,* *l*^{s} = Δ. On the mesoscale side, *l* ≤ Δ ≤ ∞, *l*^{s} = *l*

The ratio of the rms value of the rate of buoyant production of scalar flux and the rms value of its full production rate, Eq. (20), vs *l*/Δ. Data are from the HATS experiment

The ratio of the rms value of the rate of buoyant production of scalar flux and the rms value of its full production rate, Eq. (20), vs *l*/Δ. Data are from the HATS experiment

The ratio of the rms value of the rate of buoyant production of scalar flux and the rms value of its full production rate, Eq. (20), vs *l*/Δ. Data are from the HATS experiment