Beside their intermittent nature, coherent structures have a sharp localization in the time domain which is evident in the step-like changes in the scalar time series. Therefore wavelets as time-localized base functions are an appropriate tool to detect and extract the
‘fingerprints’ of coherent eddy motion in these time series. The wavelet transform in Eq. (4) is a convolution between the scalar time series X(t) and a wavelet gp,a,b(t) for each scale a. It decomposes the signal X(t) into a theoretically infinite series of dilated (scale a) and translated (time localization b) versions of a base or
‘mother wavelet’ gp,1,0(t) (see, e.g., Grossmann et al., 1989; Torrence and Compo, 1998). The resulting wavelet coefficients Tp(a, b) contain the degree of similarity between X(t) and gp,a,b(t) for each value of a and b. The power coefficient p just influences the amplitude of the wavelet.
For a detailed description of the mathematical requirements that have to be met by a wavelet function, in addition to its limited temporal extent, see, e.g., Young (1993). In the present paper the ‘Mexican Hat’ (MHAT) wavelet Eq. (5) was used for all calculations with p set to 1 following Collineau and Brunet (1993a). As a second derivative of a Gaussian function, the MHAT wavelet has some characteristics that are of great benefit for the purpose here.
The coefficients Tp(a, b) of the axis-symmetric MHAT wavelet show zero-crossing (changing sign) at a time b0 when a step-like change (jump) occurs in X(t), avoiding the definition of an empirical threshold (necessary
if point symmetric wavelets like the HAAR wavelet are used which peak at jumps in X(t)) (Collineau and Brunet, 1993a). The orientation of the zero-crossing depends on the jump direction in the time series. Because the extreme jumps in scalar time series are frequently connected to coherent structures, an objective method is offered to determine the time period τs separating the consecutive structures. The value a0, corresponding to the peak of the wavelet variance spectrum Wp(a), is an appropriate scale to determine an optimum detection function from Tp(a, b), which is neither picking up small-scale fluctuations of X(t) nor missing a lot of real ramp structures (Collineau and Brunet, 1993a).
Wp(a) is defined as
the square modulus of the wavelet coefficients integrated over all translations b.
Its maximum occurs at the scale where covariance between the wavelet and the dominating structures in X(t) is the highest. It was found that a0 (times a wavelet dependent form factor which is π 2 for MHAT) corresponds to the time duration D of dominant ramps in the analyzed time series (see, e.g., Collineau and Brunet, 1993b) (note that D here is the duration of the whole ramp and therefore 2 times the duration defined by Collineau et al. (1993b)). The mean duration D of the ramps within a measuring period is a fraction of the average separation time τs, due to quiescent periods embedded from time to
time in between consecutive events (Fig.
1 (a)).
Large-scale events penetrating the main part of the canopy are spatially coherent and therefore visible in the simultaneously acquired time series from different measuring levels throughout the forest (Gao et al., 1989;
1992). In order to reduce the number of erroneous detections caused by background turbulence, a two-level detection scheme was used, including data from the uppermost measuring height (1.33 h) and the stem space of the forest (0.28 h). This scheme to determine the time interval τs consists of the following steps. After calculating the wavelet transform of the scalar time series measured at zm1 = 1.33 h and zm2 = 0.28 h, the wavelet variance spectrum at each height provides a scale a0(zmi) corresponding to its peak. With these values individual detection functions Tp(a0(zmi) , b) can be determined. The mean time delay τdel between the measurements caused by the vertical distance is considered by calculating the time shift of maximum lag cross-correlation Xz 1z 2( del) was only accepted as valid if the detection
function at 0.28 h also changed its sign within a tolerance time interval of ±10 s. The mean separation interval between coherent events τs
is then the averaging time Ta (here 1800 s) divided by the number of detections.
Fig. 3 shows 10-min examples of temperature time series which were mainly used for the detection scheme together with the individual detection functions Tp(a0(zmi), b). An additional measuring level at 1.05 h available at that time is also displayed. Please note that due to the mean thermal stratification, the heat flux was negative in the lower canopy so that inverted ramps are displayed in T′ at 0.28 h. It is obvious that not all events that were detected at both upper heights, are reaching down to the level in the stem space of the canopy (e.g., at t ≈ 370 s). One can also see that at the expense of an excellent localization in the frequency domain, the time localization of MHAT shows a higher uncertainty than point symmetrical wavelets do. Owing to the uncertainty principle, an arbitrarily high precision in both domains cannot be achieved (see Kumar and Foufoula-Georgiou, 1994). However it was shown by Collineau and Brunet (1993a;
1993b) that despite the higher uncertainty, good agreement was achieved between τs values obtained with MHAT and point symmetric wavelets.
-0.5 0.0 0.5
0 100 200 300 400 500 600
-1 0 1
-1 0 1
-1 0 1 T' 1.33h (K)
-0.5 0.0 0.5
T' 1.05h (K)
-1.0 0.0 1.0
T' 0.28h (K)
time (s)
Tp(a0(1.33h),b) Tp(a0(1.05h),b) Tp(a0(0.28h),b)
Fig. 3. Temperature time series measured simultaneously at three heights (black curves). The gray curves are the corresponding wavelet transforms Tp(a0(zmi), b) at the peak scales a0 of the wavelet variance spectra Wp(a). The wavelet transforms Tp(a0(zmi), b) are displayed in normalized form. The vertical gray shaded rectangles mark the tolerance windows of the two-level detection scheme for structures simultaneously detected at 1.33 h and 0.28 h (details in the text).
Its good frequency localization makes the MHAT wavelet an appropriate choice to extract the ramp information from the measured scalar time series. At each layer included in the jump detection procedure, the ratio between the mean structure duration D and the time separation interval τs between the structures was found to be around 0.94. Similar fractions (keeping in mind the factor 2 by definition of D, as mentioned above) were found by Collineau and Brunet (1993b) and Lu and Fitzjarrald (1994) applying several different wavelets. D ≈ τs was used to derive a corresponding wavelet scale af (or duration Df ) for the separation interval τs obtained from the
two-level detection. The scale
( )
2 −1≈τs π
af is then assumed to be
characteristic for the structures penetrating deep into the canopy. In the scale domain the wavelet transform of the scalar time series is weighted (Eq. (8)) with a one-decade wide rectangular filter window (Eq. (9)) centered around af with af = aSaL (comparable to the Butterworth filter width applied by Paw U et al. (1995)). The wavelet coefficients Tp(a,b) are set to zero for scales a < aS and a > aL before the inverse wavelet transform (Eq. (10)) is applied on Tpf(a,b).
( )
p( ) (
S L)
An example of the filter procedure can be seen in Fig. 4 (c). Displayed are a 1-s block-averaged ozone time series before (X(t)) and after (Xf(t)) the application of the filter. It can be seen that high-frequency noise and fluctuations are removed by the scheme. Fig.
4 (a) and (b) show the corresponding wavelet variance spectrum Wp(a) (with the filter window) and the wavelet transform Tp(a,b), respectively.
In order to make the obtained filter principally transferable to other time periods (e.g., when only one measuring height is available) or to make the results comparable for other canopies, a functional relation to environmental parameters is necessary. The frequency of coherent structures occurrence 1/τs at the vegetation–atmosphere interface was found to scale with horizontal wind shear at z = h (Paw U et al., 1992; Raupach et al., 1989).
Raupach et al. (1989; 1996) proposed a plane mixing-layer analogy to describe canopy turbulence characteristics like the development of coherent eddies. A necessary condition for
the occurrence of hydrodynamic instabilities (the origin of coherent structure development) is an inflection in the vertical wind profile of the mean velocity. The spatial separation of the structures in flow direction was found to be a function of a canopy related shear length scale (see also Brunet and Irvine, 2000; Katul et al., 1998).
Owing to the lack of a continuously measured horizontal wind velocity at the canopy height, a suggestion of Chen et al.
(1997b) was taken up, that u*/h can be used analogously to a mean wind shear measure like
) (h
u /h. The separation time interval τs between coherent structures is assumed to scale with h/u*:
Here m is an empirical coefficient. For friction velocities below u*L = 0.1 m s-1, a constant value for τs was assumed due to the pole at u* = 0 m s-1. This parameterization was used in the filtering scheme and applied on 1-s block-averaged scalar time series. The value of
[
dXS(zm)/dt]
+/− in Eq. (1) was then calculated by averaging solely the instantaneous positive or negative temporal changes in Xf(t) depending on the flux direction determined by third-order structure functions (Chen et al., 1997a).Fig. 4. Wavelet transform Tp(a,b) (b) of an ozone time series ((c); gray curve). The white areas display scales of high correlation. Integrating Tp(a,b) over time results in the variance spectrum Wp (a) which shows a distinct maximum at scale a0. The gray-shaded area in (a) marks the width (between aS and aL) and af the center of the applied scale filter. The filter procedure results here in the time series (black curve (c)), which is used in the surface renewal approach.
3 Experiment
3.1 Site and Experimental Period