Modeling and Analysis of

Adjacent Grid Point Wind Speed

Profiles Within and Above a

Forest Canopy

ARL-MR-432 April 1999

Arnold Tunick

Approved for public release; distribution unlimited.
The findings in this report are not to be construed as an

official Department of the Army position unless so

designated by other authorized documents.

Citation of manufacturer’s or trade names does not

constitute an official endorsement or approval of the use

thereof.

Destroy this report when it is no longer needed. Do not

return it to the originator.
ARL-MR-432 April 1999

Army Research Laboratory

Adelphi, MD 20783-1197

Modeling and Analysis of

Adjacent Grid Point Wind

Speed Profiles Within and

Above a Forest Canopy

Arnold Tunick 

Information Science and Technology Directorate

Approved for public release; distribution unlimited.
Adjacent grid point profile data from the canopy coupled to the

surface layer (C-CSL) model are examined to illustrate the model’s

capability to represent effects of the surface boundary on wind flow.

Vertical cross sections of the wind field and contours of derived

momentum flux data are presented. Depictions of the vegetation

morphology and terrain elevation data are also given for the areas

studied.

The C-CSL model provided data for an analysis of the surface layer

wind flow within and above five different sections of vegetative

canopy. As a result, the modeled wind speed profiles appeared to be

in line with experimental observations. Momentum flux (Reynolds

stress) data were calculated from the wind speed profile gradients.

Within the canopy layer, the structure of the profiles of momentum

flux appeared to agree well in contrast to data from two other

turbulence closure models. In the layer above the forest canopy top,

the structure of the momentum flux profiles were in line with

experimental observations. In data-limited areas, this kind of

modeling can be used to support land-based operations where the

transport and diffusion of smoke, chemicals, or other toxic aerosols

Abstract

in complex terrain are a primary concern.

ii
iii

Contents

1. Introduction ........................................................................................................................................... 1

2. C-CSL Model and Coupled Wind Profile Equations ..................................................................... 2

3. Input Data: Meteorology, Terrain, and Vegetation......................................................................... 4

4. Analysis .................................................................................................................................................. 6

5. Summary and Conclusions ............................................................................................................... 12

References ................................................................................................................................................ 13

Distribution ............................................................................................................................................. 17

Report Documentation Page................................................................................................................. 21

Table

1. Vegetation and land-use elements....................................................................................................... 5

Figures

1. A contour map of terrain elevation data (in meters) for C-CSL model study. Horizontal

line segments indicate where C-CSL wind speed profile data were taken for analysis ........... 4

2. A chart of vegetation (forest) morphology for five cases of C-CSL model data studied............. 5

3. C-CSL model results for case 1: adjacent grid wind speed profiles and momentum

flux profiles .......................................................................................................................................... 7

4. C-CSL model results for case 2: adjacent grid wind speed profiles and momentum

flux profiles .......................................................................................................................................... 8

5. C-CSL model results for case 3: adjacent grid wind speed profiles and momentum

flux profiles .......................................................................................................................................... 9

6. C-CSL model results for case 4: adjacent grid wind speed profiles and momentum

flux profiles ........................................................................................................................................ 10

7. C-CSL model results for case 5: adjacent grid wind speed profiles and momentum

flux profiles .........................................................................................................................................11
1

1. Introduction

Modeling and analysis of wind speed profiles within and above cropcovered and forested areas over uneven terrain can reveal many interesting aspects of microscale canopy air flow. For example, abrupt changes in

canopy displacement height can generate locally intense wind shears at a

leading or trailing edge of a forest stand (Raynor, 1967; Meroney, 1968;

Shinn, 1968). Changes in the density and the type of vegetation can also

affect the degree to which aerodynamic drag is imparted to the wind flow

at the canopy-air interface (Cionco, 1965, 1978, 1979). Observations have

shown that aerodynamic drag is greatest through a relatively thin layer in

the upper part of a canopy (Stull, 1988; Shaw et al, 1988; Lee and Black,

1993) and that most of the downward momentum flux is absorbed at this

height rather than at the ground surface. Changes in elevation of the

underlying terrain can also affect the contour patterns of the surface layer

winds (Orgill and Shreck, 1985) as wind speeds may accelerate over

isolated hills and ridges and diverge (converge) ahead of (behind) significant terrain features.

In this study, adjacent grid point profile data from the canopy coupled to

the surface layer (C-CSL) model (Cionco, 1965, 1985) are examined to

illustrate the model’s capability to represent effects of the surface boundary on wind flow. Vertical cross sections of the wind field and contours of

the derived momentum flux are presented for five case studies. Depictions of the vegetation morphology and terrain elevation data are also

given for the modeled area.

The C-CSL model is a diagnostic tool that can produce data for an analysis of the surface layer wind flow in and above a vegetative canopies. In

data- and information-limited areas, this kind of modeling can be used to

support land-based operations affected by the transport and diffusion of

smoke, chemicals, or other toxic aerosols (Ohmstede and Stenmark, 1980;

Hanna, 1981; Cionco, 1982).
2

2. C-CSL Model and Coupled Wind Profile Equations

The C-CSL model given by Cionco (1965, 1985) produces data for the

analysis of flow over complex terrain by first simulating the highresolution wind (HRW) field over an entire gridded area and then coupling the horizontal flow to the canopy flow at each grid point. The

C-CSL model is driven by the two-dimensional HRW model (Ball and

Johnson, 1978; Cionco, 1982, 1985), which calculates the wind flow over a

gridded area of 5 × 5 km with a spatial resolution of 100 m. The HRW

model is initialized with values for surface layer wind speed, wind

direction, temperature, pressure, and an estimate of buoyancy (or relative

stability) derived from a single upper air sounding. Detailed terrain

elevation, vegetation, and land-use information is also needed as input.

The HRW model defines an initial uniform field and then calculates

deformations in the wind field caused by changes in the terrain elevation

and discontinuities in the surface roughness and vegetation based on

conservation of momentum and continuity.

Wind speed profiles in the layer above the canopy top are written in form

described by Businger (1973) and Garratt (1994):

U(z) = u*

k ln z – d

zo – ψ m , (1)

where U = the mean total horizontal wind, z = height above ground level,

d = displacement height (≈ 0.7zc), zo = the roughness height (≈ 0.14zc),

zc = canopy height, u* = the friction velocity (i.e., surface stress), k is

von Kármán’s constant (= 0.4), and ψm = the buoyancy-stability function,

i.e., the deviation in the wind speed profile from the neutral stability case

(Paulson, 1970).

Wind speed profiles within the canopy layer are exponential in form as

described by Inoue (1963) and Cionco (1965):

U(z) = Uc exp α z

zc – 1 , (2)

where Uc = the mean total horizontal wind at the canopy top, zc = canopy

height, and α is the canopy flow index (Cionco, 1978). The canopy flow

index, α, represents a measure of the wind flow response to the canopy

element, for example, its height, density, or flexibility. A compilation of

values reported in Cionco (1978) suggests that the flow index has a range

from approximately 1.00 to 2.80 for corn, wheat, oats, and like crops and

from approximately 2.70 to 4.40 for forest canopies, such as oak, maple, or

spruce.
3

The calculation of the wind speed at the top of the canopy, Uc , is based

on a coupling ratio, Rc, a relationship proposed by Cionco (1979) that can

be expressed as

Rc = U0.25zc

U1.4zc

=

Uc exp α

0.25zc zc – 1

u *

k ln 1.4zc – d

zo – ψ m

, (3)

where U0.25zc and U1.4zc denote the mean wind speed at heights above

ground level in the canopy layer and in the ambient surface layer, respectively. Substituting for zo and d, the expression for Uc can be rewritten as

Uc = u *

k ln(5.0) Rc exp(0.75α) , (4)

where values (in percent) of the coupling ratio, Rc , depend mainly on

relative distance from either ambient flow upwind, flow in the canopy, or

air flow downwind from the canopy’s trailing edge (Cionco, 1979). Alternatively, the coupling ratio can be thought of as indicating the rate of

momentum transfer through the canopy. (It is not certain why the expression for Uc given by equation (4) does not include ψm, the buoyancystability function.)

The mean downward flux, – u′ w′ , of horizontal momentum (or Reynolds

stress) can be expressed as

– u′ w′ = Km ∂U

∂z = u*

2 , (5)

where Km = u* kz/φm is a diffusion coefficient for the surface layer, u* is

the friction velocity (which also refers to the surface stress tensor), and φm

is the nondimensional wind shear (Businger, 1973).
4

3. Input Data: Meteorology, Terrain, and Vegetation

The meteorological data for this study were taken from data collected as

part of the meteorology and diffusion over nonuniform areas

(MADONA) multinational field campaign (Cionco et al, 1995). Surface

weather conditions during the field study (15 to 23 September 1992) were

generally damp and cool, with temperatures ranging between 13 to 18 °C,

under mostly cloud-covered skies. The MADONA experiment was held

at the Ministry of Defense Chemical and Biological Defense Establishment (CBDE), Porton Down, United Kingdom. The test area (i.e., the

C-CSL model area for the present study) consisted primarily of rolling

grassy hills and forested ridges. The CBDE terrain elevation data are

given in figure 1. The terrain data show a ridge that runs southwest to

northeast with higher elevation at each end and a total maximum elevation change of approximately 100 m. Also, five separate line segments are

drawn on figure 1 to indicate where C-CSL wind speed profile data were

taken for analysis. These segments, as opposed to other subsets of the

study area, were chosen mainly because of their surface vegetation

(forest) morphology. A chart of the vegetation morphology for each of five

case studies (to be discussed in sect. 4, Analysis) is given in figure 2.

Vegetation and land-use elements are also described in table 1.

Figure 1. A contour

map of terrain

elevation data (in

meters) for C-CSL

model study.

Horizontal line

segments indicate

where C-CSL wind

speed profile data

were taken for

analysis.

5

10

15

20

25

30

35

40

45

50

5 10 15 20 25 30 35 40 45 50

East-west grid (× 100 m)

North-south grid (

× 100 m) 

180

160

140

120

100

80

60

(m)

( 4 )

( 5 )

( 2 )

( 3 )

( 1 )
5

35 36 37 38 39 40 41 42 43 44 45 46 47 48

0

10

20

Height (m)

Case 5

East-west grid (× 100 m)

35 36 37 38 39 40 41 42 43 44 45 46 47 48

0

10

20

17 18 19 20 21 22 23 24 25 26 27 28 29 30

0

10

20

Case 3

Case 4

28 29 30 31 32 33 34 35 36 37 38 39 40 41

0

10

20

Case 2

14 15 16 17 18 19 20 21 22 23 24 25 26 27

0

10

20

Case 1

Figure 2. A chart of

vegetation (forest)

morphology for five

cases of C-CSL model

data studied.

Element type Height (m)

Full density conifers 12.0

Full density deciduous 8.0

Farmland or crops 0.1 to 1.0

Grasslands 0.6

Table 1. Vegetation

and land-use

elements.
6

4. Analysis

The graphs in figures 3 to 7 show contours plotted through (a) modeled

wind speed and (b) momentum flux data. Five cases are presented. Case 1

(fig. 3) shows an example of the modeled surface layer flow over a symmetric forest stand. Case 2 (fig. 4) is similar to case 1, although the wind

speeds above the canopy are not as strong and the forest stand is broader

and more even. Case 3 (fig. 5) shows an example of undisturbed wind

flow over open cropland as it approaches the windward (leading) edge of

a forest canopy. Case 4 (fig. 6) shows the modeled wind speed and

momentum flux profile data at a canopy’s leeward (trailing) edge. Last,

case 5 (fig. 7) shows an example of the modeled wind flow for patches of

forest and clearings, where the clearings extend to a horizontal distance

much greater than the height of the individual tree elements.

The modeled data show (in figs. 3(a) and 4(a), in particular) that abrupt

changes at the surface boundary result in large upward and downward

deflections of the contours and significantly reduced wind speeds

through the leading edges of the canopy. Within and above the center

portion of canopy, the wind speed contours tended to either level off or

slope downward slightly. The modeled data also show (in each case) a

slowing of the wind flow, followed by reaccelerated winds, at or near the

trailing edges of the canopy (see figs. 3(a), 4(a), and 6(a)). Barr (1971)

refers to this slowing as a “wave-type” deflection, which appears as a rise

in the wind speed contour line as the canopy’s trailing edge is approached. Barr claims this feature of canopy flow has been evidenced in

the experimental data collected by Stearns (1964) and the wind tunnel

simulations reported by Kawatani and Meroney (1968).

The data presented in figures 3(b) through 7(b) show the behavior of the

derived momentum flux within and above the five sections of forest

canopy studied. The momentum flux (Reynolds stress) data were calculated from the modeled wind speed profile gradients. In the lower portion of the canopy, the profiles of flux data are shown to decrease rapidly

toward the ground. Line graphs of these data appear to agree well in

contrast to data from the higher-order turbulence closure models of air

flow within a forest canopy reported by Wilson and Shaw (1977) and

Patton et al (1994). Above the canopy, the derived momentum flux data

are shown to remain fairly uniform and even increase slightly with

height. This result agrees with the observations reported by Shaw et al

(1988) that indicated a constant stress (constant flux) layer of about 25 m

above the top of the forest. In contrast, data reported in Lee and Black

(1993) indicated a reduction in the Reynolds stress in the layer between

1.0 and 1.38 times the height of the top of the canopy. However, Lee and

Black suggested that the reduction (flux divergence) that they had observed may have been associated with the topography of their experimental site.
7

Figure 3. C-CSL model results for case 1: (a) adjacent grid wind speed (m/s) profiles and

(b) adjacent grid momentum flux (m2/s2) profiles.

1.34

2.81

3.47

3.89

4.21

1.38

2.89

3.57

4.00

4.33

1.45

3.04

3.75

4.21

4.55

1.52

3.19

3.94

4.42

4.78

4.00

1.60

3.35

4.004.14

4.00

4.65

5.03

0.41

0.59

0.82

1.26

1.81

2.34

2.73

3.05

4.003.31

0.62

1.25

1.00

1.79

2.52

3.84

5.63

6.60

7.41 7.00

8.10

8.71

0.63

1.27

1.82

2.56

3.91

5.69

6.66

7.47

8.17

8.78

1.24

1.77

2.49

3.79

5.50

7.11

7.00

8.32

9.29

10.09

1.61

3.38

4.18

4.00

4.70

5.08

1.52

3.18

3.93

4.41

4.77

7.00

1.43

2.99

3.70

4.15

4.49

1.40

2.94

3.63

4.08 4.00

4.41

1.38

2.90

3.58

4.03

4.35

14 15 16 17 18 19 20 21 22 23 24 25 26 27

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

0.29

0.29

0.29

0.28

0.54

0.32

0.32

0.31

0.31

0.59

0.35

0.35

0.35

0.34

0.65

0.37

0.37

0.36

0.35

0.68

0.42

0.41

0.41

0.40

0.76

0.35

0.331.00

0.31

0.26

1.32

0.50

0.08

0.02

0.00

2.64

2.48

2.27

2.00

7.00

11.55

3.94 4.00

0.641.00

0.14

0.04

2.85

2.68

2.47

2.19

12.42

4.42

0.72

0.16

0.04

2.96

2.79

2.54

2.15 4.00

11.05

4.01

0.65

0.15

0.04

0.41

0.40

0.39 1.00

0.38

0.73

0.36

0.36

0.35

0.35

0.66

0.33

1.00

0.33

0.33

0.32

0.61

0.32

0.31

0.31

0.30

0.58

0.32

0.31

0.31

0.30

0.58

14 15 16 17 18 19 20 21 22 23 24 25 26 27

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

East-west grid (× 100 m)

East-west grid (× 100 m)

Height (m) Height (m)

(a)

(b)
8

Figure 4. C-CSL model results for case 2: (a) adjacent grid wind speed (m/s) profiles and

(b) adjacent grid momentum flux (m2/s2) profiles.

1.49

3.12

3.85

4.33

4.68

1.46

3.07

3.79

4.26

4.61

1.41

2.96

3.66

4.11

4.44

4.00

0.16

0.33

0.47

0.66

1.00

1.43

1.77

2.04

2.25

2.43

0.43

0.86

1.23

1.73

2.64

3.79

4.30

4.00

4.74

5.13

5.47

0.43

0.87

1.24

1.74

2.65

3.85

4.37

4.83

5.23

5.58

0.43

0.87

1.24

1.75

2.66

3.86

4.38

4.84

5.23

5.59

0.42

0.86

1.00

1.23

1.73

2.63

3.79

4.00

4.30

4.75

5.13

5.48

0.40

0.81

1.16

1.62

2.48

3.55

4.03

4.45

4.81

5.13

0.38

0.77

1.11

1.56

2.38

3.41

3.88

4.28

4.62

4.93

0.35

0.71

1.001.02

1.43

2.18

3.14

3.90

4.48

4.00

4.97

5.37

1.13

2.37

2.92

3.27

3.53

1.11

2.33

2.88

3.23

3.48

4.00

1.15

2.41

2.98

3.34

3.61

28 29 30 31 32 33 34 35 36 37 38 39 40 41

East-west grid (× 100 m)

East-west grid (× 100 m)

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

0.35

0.35

0.34

0.34

0.64

0.34

0.34

0.33

0.32

0.62

0.32

0.31

0.31

0.30

0.57

0.23

0.22

0.20

0.18

0.90

0.36

0.06

0.01

0.00

1.31

1.24

1.16

1.05

4.00 1.00

5.86

2.30

0.37

0.08

0.02

1.27

1.21

1.12

1.01

5.81

2.12

0.34

0.08

0.02

1.29

1.22

1.14

1.03

5.88

2.17

0.35

0.08

0.02

1.29

1.22

1.14

1.04

5.81

2.23

0.36

0.08

0.02

1.15

1.09

1.02

0.93 1.00

5.15

4.00

4.00

2.02

0.33

0.07

0.02

1.05

1.001.00

0.94

0.85

4.74

1.84

1.00 1.00

0.30

0.07

0.02

1.00

0.95

0.87

0.76

3.98

1.49

1.00

0.24

0.05

0.01

0.23

0.23

0.23

0.23

0.44

0.22

0.22

0.22

0.22

0.41

0.23

0.23

0.22

0.22

0.42

28 29 30 31 32 33 34 35 36 37 38 39 40 41

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

Height (m) Height (m)

(a)

(b)
9

Figure 5. C-CSL model results for case 3: (a) adjacent grid wind speed (m/s) profiles and

(b) adjacent grid momentum flux (m2/s2) profiles.

1.33

2.78

3.43

3.86

4.17

1.33

2.78

3.44

3.86

4.17

1.31

2.76

3.40

3.82

4.14

1.28

2.67

3.30

3.70

4.00

4.00

4.00

4.00

1.37

2.88

3.55

3.99

4.31

1.43

2.99

3.69

4.15

4.48

0.37 0.53

0.74

1.13 1.61

2.08

2.43

2.70

2.93

0.57

1.15 1.00

1.64

2.31

3.52

5.14

5.76

6.30

6.79

7.22

0.58

1.17

1.68

2.36

3.59

5.25

5.74

6.18

6.59

6.96

0.57

1.15

1.65

2.32

3.53

4.00

4.00

5.16

5.78

6.33

6.81

7.25

0.49

0.99

1.42

1.99

3.04

4.38

5.12

5.74

6.27

6.73

0.83

1.19 1.67

2.55 3.67

4.77

5.59

6.23

6.77

0.77

1.95

2.39

2.66

2.86

0.78

1.99

2.44

2.72

2.93

17 18 19 20 21 22 23 24 25 26 27 28 29 30

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

0.29

0.29

0.28

0.28

0.53

0.29

0.29

0.29

0.28

0.54

0.28

0.27

0.27

0.26

0.50

0.28

1.00

0.28

0.28

0.27

0.52

0.32

0.32

0.31

0.31

0.59

0.33

0.33

0.33

0.32

0.61

0.30

4.00

1.00

0.29

0.27

0.23

1.13

0.45

0.07

0.02 0.00

2.12

2.01

1.87

1.70

10.05

3.52

0.57

0.13

0.03

2.09

1.99

1.87

1.72

4.007.00

10.44

3.624.00

4.00

0.59

0.13

0.03

2.14

2.03

1.89

1.71

10.14

3.56

0.58

1.00

0.13

0.03

1.82

1.73

1.60

1.43

7.85

2.99

0.49

0.11

0.03

1.47

1.40

1.29

1.10 1.00

1.00

5.49

2.10 0.34

0.08

0.02

0.13

0.13

0.13

0.16

0.14

0.13

0.13

0.13

0.15

0.14

17 18 19 20 21 22 23 24 25 26 27 28 29 30

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

East-west grid (× 100 m)

East-west grid (× 100 m)

Height (m) Height (m)

(a)

(b)
10

Figure 6. C-CSL model results for case 4: (a) adjacent grid wind speed (m/s) profiles and

(b) adjacent grid momentum flux (m2/s2) profiles.

0.58

1.18

1.69

2.37

3.62

5.24

5.73

6.17

6.57

6.94

0.62

1.25

1.79

2.51

3.83

5.57

6.09

6.56

6.99

7.38

0.60

1.21

1.73

2.44

3.71

5.41

5.92

6.37

6.79

7.17

0.51

1.03 1.00 1.00

1.47

2.07

3.15

4.51

4.92

5.30

5.64

5.95

0.42

0.85

1.21

1.70

2.60

3.68

4.024.00

4.00

4.33

4.60

4.85

0.38

0.77

1.10

1.55

2.36

3.29

3.59

3.86

4.10

4.32

0.37

0.74

1.06

1.49

2.27

3.23

3.77

4.22

4.60

4.94

0.95

1.99

2.44

2.74

2.95

1.09

2.29

2.82

3.16

3.42

1.13

2.38

2.93

3.29

3.56

4.00 4.00

1.18

2.48

3.05

3.43

3.70

1.21

2.54

3.13

3.51

3.79

1.31

2.60

3.21

3.61

3.90

1.40

2.79

3.45

3.88

4.20 4.00

35 36 37 38 39 40 41 42 43 44 45 46 47 48

East-west grid (× 100 m)

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

2.15

2.05

1.93

1.79

10.61

3.87

0.63

0.14

0.04

2.40

2.28

2.15

1.99 4.00

7.00

11.89

4.24

0.69

0.15

0.04

2.25

2.14

2.01

1.86

11.17

3.94

0.64

0.14

0.04

1.67

1.60

1.51

1.41

8.00

7.00

4.00 3.17

1.00 0.51

0.12

0.03

1.16

1.11

1.05

0.98

5.39

4.00

2.29

0.37

0.08

0.02

0.97

0.94 1.00

0.89

0.84

4.32

2.05

0.33

0.08

0.02

1.07 1.00

1.00

1.00

1.02

0.96

0.86

4.52

1.89

0.31

0.07

0.02

0.18

0.18

0.18

0.18

0.35

0.21

0.21

0.21

0.21

0.40

0.22

0.22

0.22

0.22

0.41

0.24

0.24

0.24

0.24

0.45

0.26

0.26

0.26

0.26

0.49

0.28

0.28

0.28

0.26

0.59

0.31

0.31

0.30

0.29

0.64

35 36 37 38 39 40 41 42 43 44 45 46 47 48

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

Height (m)

(a)

East-west grid (× 100 m)

Height (m)

(b)
11

Figure 7. C-CSL model results for case 5: (a) adjacent grid wind speed (m/s) profiles and

(b) adjacent grid momentum flux (m2/s2) profiles.

0.42

0.84

1.21

1.70

2.59

3.74

4.39

4.93

5.39

5.79

0.18

0.36

0.51

0.72

1.09

1.56

1.94

2.23

2.47

2.67 4.00

0.36 0.51 0.72 1.10 1.00 1.58

2.19

2.62

2.94

3.20

1.42

2.98

3.67

4.13

4.46

1.37

2.88

3.56

4.00 4.00

4.32

0.17

0.34

0.48

0.68

1.03

1.48

1.83

2.10

2.33

2.52

4.00

0.41

0.83

1.18

1.66

2.54

3.68

4.18 4.00

4.62

5.00

5.34

0.42

0.84

1.001.21

1.70

2.59

3.74

4.39

4.93

5.38

5.78

0.80 1.14 1.60 2.44 3.50

4.00

4.86

5.80

6.51

7.09

1.26

2.51

3.09

3.48

3.76

1.21

2.30

2.83

3.19

3.45

0.28 0.40

0.57 0.86 1.231.00

1.68

1.99

2.23

2.42 4.00

4.00

4.00

0.68 0.98

1.38 2.10 3.01

4.10

4.87 4.00

5.46

5.94

1.19

2.36

2.91

3.27

3.54

35 36 37 38 39 40 41 42 43 44 45 46 47 48

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

1.30

1.23

1.14

1.01

5.60

2.08

0.34

0.08

0.02

0.26

0.25

0.23

0.20

1.04 1.00

0.41

0.07

0.01

0.00

0.29

0.27

0.25

0.21

0.98 0.38

0.06 0.01

0.33

0.33

0.33

0.32

0.61

0.30

0.30

0.30

0.29

0.55

0.24

0.23

0.21

0.18

0.94

0.37

0.06

0.01

0.00

1.16

1.10

1.02

0.92

5.31

1.93

0.31

0.07

0.02

1.31

1.24

1.15

1.02

5.63

2.10

0.34

0.08

0.02

1.43

1.37

1.26

1.07

4.91 1.93

0.31 0.07

0.26

0.26

0.26

0.25

0.55

0.23

0.23

0.22

0.21

0.52

0.19

0.18

0.17

0.15

0.67

0.28

0.04

0.01

1.071.00

1.00

1.00

1.00

1.00 1.00

1.00

1.00

1.00

1.03

0.95

0.81

3.77

1.49

0.24

0.05

0.24

0.24

0.24

0.23

0.50

35 36 37 38 39 40 41 42 43 44 45 46 47 48

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

East-west grid (× 100 m)

Height (m)

(a)

East-west grid (× 100 m)

Height (m)

(b)
12

5. Summary and Conclusions

Adjacent grid point profile data from the C-CSL model were examined.

Cross-section analyses of the wind fields and contours of derived momentum flux data were presented for five different segments of forest

canopy to illustrate the model’s capability to represent effects of the

surface boundary on wind flow.

The wind speed cross sections showed large deflections in the contours at

the leading edges of the forest canopies and greatly reduced wind speeds

through the remainder of the canopy layer. At the trailing edges of the

canopy, the modeled data showed a slowing of the wind flow, followed

by reaccelerated winds. These model results appear to be in line with

experimental observations.

Momentum flux (Reynolds stress) data were calculated from the modeled

wind speed profile gradients. Within the canopy layer, the structure of the

profiles of momentum flux appeared to agree well in contrast to data

from two other turbulence closure models. In the layer above the forest

canopy top, the structure of the momentum flux profiles were in line with

experimental observations.
13

References

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Temperature Analysis for EPAMS, Contractor Report ASL-CR-78-0048-1,

U.S. Army Atmospheric Sciences Laboratory, WSMR, NM.

Barr, S. (1971). “A modeling study of several aspects of canopy flow,” Mon.

Weather Rev. 99, pp 485–493.

Businger, J. A. (1973). “Turbulent transfer in the atmospheric surface layer,”

Workshop on Micrometeorology, D. A. Haugen, ed., Am. Meteorol. Soc.,

p 392.

Cionco, R. M. (1965). “A mathematical model for air flow in a vegetative

canopy,” J. Appl. Meteorol. 4, pp 517–522.

Cionco, R. M. (1978). “Canopy index values for various canopy densities,”

Boundary-Layer Meteorol. 15, pp 81–93.

Cionco, R. M. (1979). “A summary of analysis of canopy flow coupling for a

variety of canopy types,” 14th Conf. on Agric. and Forest Meteorol. and

4th Conf. on Biometeorol., 2–6 April, Minneapolis, MN, Am. Meteorol. Soc.,

Boston, MA, pp 105–106.

Cionco, R. M. (1982). “A meteorological approach to chemical defense over

complex terrain with vegetation,” Proc. Workshop on the Parameterization of

Mixed Layer Diffusion, R. M. Cionco, ed., U.S. Army Atmospheric Sciences

Laboratory, WSMR, NM.

Cionco, R. M. (1985). “Modeling wind fields and surface layer profiles over

complex terrain and within vegetative canopies,” Forest-Atmosphere

Interactions, B. A. Hutchinson and B. B. Hicks, eds., D. Reidel Publishing

Co., Dordrecht, Holland.

Cionco, R. M., W. aufm Kampe, C. Biltoft, J. H. Byers, C. G. Collins, T. J. Higgs,

A.R.T. Hin, P.-E. Johansson, C. D. Jones, H. Jorgensen, J. F. Kimber,

T. Mikkelsen, K. Nyren, D. J. Ride, R. Robson, J. M. Santabarbara,

J. Streicher, S. Thykier-Nielsen, H. van Raden, and H. Weber (1995). “An

overview of MADONA: A multinational field study of high resolution

meteorology and diffusion over complex terrain,” Proc. 11th Symp.

Boundary Layers and Turb., 27–31 March, Charlotte, NC, Am. Meteorol.

Soc., Boston, MA.

Garratt, J. R. (1994). The atmospheric boundary layer, Cambridge University Press,

p 316.

Hanna, S. R. (1981). “Applications in air pollution modeling,” Atmospheric

Turbulence and Air Pollution Modeling, F.T.M. Nieuwstadt and H. van Dop,

eds., D. Reidel Publishing Co., Dordrecht, Holland.
14

Inoue, E. (1963). “On the turbulent structure of airflow within crop canopies,” J.

Meteorol. Soc., Japan 41, p 317.

Lee, X., and T. A. Black (1993). “Atmospheric turbulence within and above a

Douglas-fir stand. Part I: Statistical properties of the velocity field,”

Boundary-Layer Meteorol. 64, pp 149–174.

Kawatani, T., and R. N. Meroney (1968). The Structure of Canopy Flow Field,

Technical Report, grant no. DA-AMC-28-043-65-G20, Fluid Dynamics and

Diffusion Laboratory, Colorado State University, Ft. Collins, CO, p 122.

Meroney, R. N. (1968). “Characteristics of wind and turbulence in and above

model forests,” J. Appl. Meteorol. 7, pp 780–788.

Ohmstede, W. D., and E. B. Stenmark (1980). “A model for characterizing

transport and diffusion of air pollution in the battlefield environment,”

Proceedings of AMS/APCA Second Joint Conference on Applications of Air

Pollution Meteorology, 24–27 March, New Orleans, LA, pp 416–423.

Orgill, M. M., and R. I. Schreck (1985). “An overview of the ASCOT multilaboratory field experiments in relation to drainage winds and ambient

flow,” Bull. Am. Meteorol. Soc. 66, pp 1263–1277.

Patton, E. G., R. H. Shaw, K. T. Pau U, and C.-H. Moeng (1994). “A comparison

of two large eddy simulations of turbulent flow above and within a forest

canopy,” Proceedings of the 21st Conference on Agricultural and Forest Meteorology, 7–11 March, San Diego, CA, Am. Meteorol. Soc., Boston, MA.

Paulson, C. A. (1970). “The mathematical representation of wind speed and

temperature profiles in the unstable atmospheric surface layer,” J. Appl.

Meteorol. 9, pp 57–61.

Raynor, G. S. (1967). “Effects of a forest on particulate dispersion,” Proc. of the

U.S. Atomic Energy Commission Meteorological Meeting, Chalk River

Nuclear Laboratories, Ontario, Canada, 11–14 September, Atomic Energy

of Canada, Ltd., Ontario, pp 581–588.

Shaw, R. H., G. den Hartog, and H. H. Neumann (1988). “Influence of foliar

density and thermal stability on profiles of Reynolds stress and turbulence intensity in a deciduous forest,” Boundary-Layer Meteorol. 45,

pp 391–409.

Shinn, J. H. (1968). “Air flow in the tree trunk region of several forests,”

National Meeting of the American Meteorological Society With the Pacific

Division, American Association for the Advancement of Science, 26–28

June, Logan, UT.

Stearns, C. R. (1964). “Wind profile modification experiments using fields of

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DA-36-039-AMC-00878, Dept. of Meteorol., University of Wisconsin,

Madison, p 115.
15

Stull, R. B. (1988). An Introduction to Boundary Layer Meteorology, Kluwer

Academic Publishers, p 666.

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flow,” J. Appl. Meteorol. 16, pp 1197–1205.
17

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NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89)

Prescribed by ANSI Std. Z39-18

298-102

Modeling and Analysis of Adjacent Grid Point Wind

Speed Profiles Within and Above a Forest Canopy

April 1999 Final 9/1/98 to 1/4/99

Adjacent grid point profile data from the canopy coupled to the surface layer (C-CSL) model are

examined to illustrate the model’s capability to represent effects of the surface boundary on wind

flow. Vertical cross sections of the wind field and contours of derived momentum flux data are

presented. Depictions of the vegetation morphology and terrain elevation data are also given for the

areas studied.

The C-CSL model provided data for an analysis of the surface layer wind flow within and above five

different sections of vegetative canopy. As a result, the modeled wind speed profiles appeared to be

in line with experimental observations. Momentum flux (Reynolds stress) data were calculated from

the wind speed profile gradients. Within the canopy layer, the structure of the profiles of

momentum flux appeared to agree well in contrast to data from two other turbulence closure

models. In the layer above the forest canopy top, the structure of the momentum flux profiles were

in line with experimental observations. In data-limited areas, this kind of modeling can be used to

support land-based operations where the transport and diffusion of smoke, chemicals, or other

toxic aerosols in complex terrain are a primary concern.

Vegetative canopy, wind flow, momentum flux, atmospheric surface layer,

micrometeorological model, aerodynamic roughness

Unclassified

ARL-MR-432

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6110253A11

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