Introduction
Scintillometry provides turbulent heat fluxes representative of much larger
scales than is possible with traditional point-based or small-area
measurements, such as eddy covariance (EC). The technique relates
fluctuations in the intensity of light (“scintillations”, observed as
shimmering or “heat-haze”) to the strength of turbulence in the atmosphere.
Scintillometry is suited to heterogeneous regions because the measurements
are spatially integrated, providing average values representative of the
area as a whole (Hoedjes et al., 2002; Meijninger et al., 2002b; Evans et
al., 2012). A transmitter unit provides the light source, a beam of
electromagnetic radiation, which is detected some distance (0.1–10 km) away
by the receiver. Variations in the received intensity result from
diffraction; as turbulent eddies move through the beam, their refractive
indices are determined by their densities which, in turn, can be related to
their temperature and moisture content (e.g. Meijninger, 2003). The
refractive index depends on the wavelength of radiation; in the optical or
near-infrared range temperature-induced fluctuations dominate the refractive
index fluctuations, whereas for longer wavelengths (millimetre or radiowave
regions) humidity fluctuations are more important. Scintillometers are most
sensitive to fluctuations occurring towards the centre of the path,
i.e. away from the transmitter and receiver and their mountings. Thus the
atmosphere above a city (or valley) can be sampled remotely – a major
advantage in areas where it would be impracticable to install equipment in situ.
Scintillometer measurements have been carried out at sites of varying
complexity, from tests of the technique under simple conditions (Hill and
Ochs, 1978; De Bruin et al., 1993) to studies investigating the complications
of non-ideal terrain, including heterogeneous land cover (Beyrich et al.,
2002; Meijninger et al., 2002a, 2006; Ezzahar et al., 2007)
and complex topography (Poggio et al., 2000; Evans, 2009; Evans et al.,
2012). On the whole, these studies have shown that scintillometers installed
above or close to the blending height can provide valuable area-averaged fluxes.
With careful selection of a suitable path, the scintillometry technique has
been successfully used in urban areas. Kanda et al. (2002), the first to
obtain the sensible heat flux in an urban setting, used two small aperture
scintillometers installed at different heights on a 250 m path over a dense
residential area of Tokyo. Other small aperture studies include measurements
in Basel (Roth et al., 2006) and London (Pauscher, 2010).
Large aperture scintillometers are increasingly being used over longer urban
paths (Lagouarde et al., 2006; Gouvea and Grimmond, 2010; Mestayer et al.,
2011; Wood et al., 2013; Zieliński et al., 2013). These infrared (or
optical) scintillometers must rely on the residual of the energy balance if
the latent heat flux is to be estimated. However, the complexity of the
energy balance (Oke, 1987) means this is usually not attempted in urban
areas. In particular the significant storage heat flux
(Offerle et al., 2005) and contribution from anthropogenic activities
(Klysik, 1996; Allen et al., 2011) are both very difficult to measure.
Sensitivity to both humidity and temperature fluctuations can be achieved
with a two-wavelength scintillometer system. In this case, the structure
parameter of humidity can be obtained in addition to the structure parameter
of temperature, from which both the sensible heat flux (QH) and latent
heat flux (QE) can be found (Hill et al., 1988; Andreas, 1989). Several
studies have reported successful estimates of QE using the
two-wavelength method (Meijninger et al. 2002a, 2006;
Evans, 2009; Evans et al., 2010). This technique requires that a value of the
temperature–humidity correlation coefficient, rTq, be assumed. Often
rTq is taken to be ±1, indicating perfect correlation, as in
Green et al. (2001) and Meijninger et al. (2002a), but other values
have also been used: Kohsiek and Herben (1983) used
rTq = 0.87; Evans (2009) used rTq = 0.8; and Meijninger
et al. (2006) used measured rTq from a nearby EC station with values
between -0.5 and 0.9. Previous studies measuring rTq with fast-response
sensors suggest daytime values tend to be smaller than 1, typically around 0.8.
For example: 0.75 at a flat, homogeneous site (Kohsiek,
1982); 0.76 over sandy soil with patchy vegetation (Andreas et al., 1998); 0.70–0.95 for unstable conditions
over heterogeneous farmland (Meijninger et al., 2002a). Nocturnal values
of rTq down to -1 are rarely seen (Andreas et al., 1998; Beyrich et
al., 2005; Meijninger et al., 2006).
Some studies have suggested that rTq varies with stability (Li et al.,
2011; Nordbo et al., 2013), although others have shown no clear relation
(De Bruin et al., 1993; Roth, 1993). Explanation of
|rTq| ≠ 1 is often related to surface heterogeneity (Roth,
1993; Andreas et al., 1998; Lüdi et al., 2005) but low values have been
obtained over homogeneous surfaces too (Kohsiek, 1982; De Bruin et al.
1993). In almost all previous studies, measurements of rTq were made
using point sensors.
Lüdi et al. (2005) outlined a method to obtain path-averaged
values of rTq using a two-wavelength scintillometer system. This
“bichromatic-correlation” method is an extension of the two-wavelength
technique and involves correlating the signals from each scintillometer,
thus enabling determination of the combined temperature–humidity
fluctuations and rTq. The bichromatic-correlation method, applied for
the first time during the LITFASS-2003 campaign, gave promising results
(Beyrich et al., 2005; Lüdi et al., 2005). An overview of the second
study, during LITFASS-2009, is given in Beyrich et al. (2012).
Aside from enabling more accurate structure parameters and fluxes to be
obtained from scintillometry, improved knowledge of rTq has wider
applications. Correlations between scalars are thought to be useful
indicators for the violation of Monin–Obukhov similarity theory (MOST)
(Hill, 1989; Andreas et al., 1998). Correlations are also
relevant to the understanding and modelling of turbulent transport processes
through physical quantities such as eddy diffusivities.
The objectives of this research are to measure structure parameters and
obtain large-area sensible and latent heat fluxes for a suburban area. In
this two-part study, a 94 GHz millimetre-wave scintillometer was deployed
alongside an infrared scintillometer over the town of Swindon, UK. This is
the first use of such a system in the urban environment. In Part 1,
structure parameters from the two-wavelength system are compared to
structure parameters calculated from an EC system and measured values of
rTq are discussed. In Part 2 (Ward et al., 2015), the sensible and
latent heat fluxes are determined and analysed. These
spatially integrated observations represent the behaviour of the suburban
surface over an area of 5–10 km2 and constitute by far the longest
data set (14 months) that uses these techniques. The performance of the techniques
is assessed under a range of conditions, and their strengths and weaknesses are
examined. This paper offers insight into the behaviour of the structure
parameters and rTq at various timescales (daily, seasonal and
inter-annual), including how they respond to energy and water availability,
surface cover and changing meteorological conditions.
Theory
Structure parameters describe the intensity of turbulent fluctuations in the
atmosphere. The structure parameter for a variable, y, is defined (Tatarski,
1961);
Cy2=[y(x+δ)-y(x)]2‾δ2/3,
where δ is the spatial separation between two points and y(x) is the
value of the variable at location x. The cross-structure parameter between
two variables is defined analogously; for example the cross-structure
parameter between temperature, T, and specific humidity, q, is written as follows:
CTq=[T(x+δ)-T(x)][q(x+δ)-q(x)]‾δ2/3.
Obtaining structure parameters from scintillometry
The refractive index structure parameter (Cn2) is fundamental to
scintillometry. For each wavelength, λ, it can be written (Hill et al., 1980) as
follows:
Cn2=AT2T2CT2+2ATAqTqCTq+Aq2q2Cq2,
where CT2 is the structure parameter of temperature,
Cq2 the structure parameter of specific humidity, CTq the
temperature–humidity cross-structure parameter and AT and Aq are the
structure parameter coefficients for temperature and specific humidity,
respectively, given in Ward et al. (2013b) as
At and Aq (see their Table 2). These coefficients contain the
wavelength dependence of Cn2, whereas CT2, Cq2
and CTq are properties of the atmosphere. Each Cn2 measurement
is made up of a combination of the three unknowns: CT2,
Cq2 and CTq.
As Cn2 from large aperture optical or near-infrared scintillometers
is almost entirely made up of temperature fluctuations (CT2), the
(usually small) contributions from CTq and Cq2 can be
approximated using the Bowen ratio, β (Wesely, 1976; Moene, 2003):
Cn2≈AT2T2CT21+AqqTATcpLvβ-12≈AT2T2CT21+0.03β-12,
where cp is the specific heat capacity of air at constant pressure,
Lv is the latent heat of vaporisation and the value 0.03 is for typical
atmospheric conditions (T = 300 K, pressure (p) = 105 Pa). The
required Bowen ratio may be found by using the available energy as an input
to the iteration to obtain the sensible heat flux (e.g. Green and Hayashi,
1998; Meijninger et al., 2002b; Solignac et al., 2009). Calculation of the
latent heat flux must rely on the energy balance (Ezzahar et al., 2009;
Guyot et al., 2009; Evans et al., 2012; Samain et al., 2012b). This is the
single-wavelength scintillometry method.
As demonstrated by Hill et al. (1988) and Andreas (1989), a two-wavelength scintillometer system enables
retrieval of both CT2 and Cq2 via simultaneous equations
(Eq. (3) for each wavelength). The two-wavelength method has the
significant advantage of providing both sensible and latent heat fluxes
without resorting to the energy balance, but a value for the
temperature–humidity correlation coefficient rTq must be assumed in the
substitution CTq = rTq(CT2Cq2)1/2.
The bichromatic-correlation method uses the same combination of optical and
millimetre wavelength scintillometers as for the two-wavelength method, but
additionally exploits the correlation between optical and millimetre-wave
signals to obtain a third equation for the cross-structure parameter,
Cn1n2 (Lüdi et al. 2005):
Cn1n2=AT1AT2T2CT2+AT1Aq2+AT2Aq1TqCTq+Aq1Aq2q2Cq2,
where the subscripts 1 and 2 refer to the different wavelengths. In this
study, λ1 denotes optical (specifically 880 × 10-9 m)
and λ2 millimetre (3.2 × 10-3 m)
wavelengths. Thus all three unknown meteorological structure parameters
(CT2, Cq2 and CTq) can be found from the three
measured refractive index structure parameters by inverting the matrix
equation (Lüdi et al., 2005):
Cn1n1Cn2n2Cn1n2=MCT2CTqCq2,
where the inverse matrix M-1 is given by
M-1=T2q2AT1Aq2-AT2Aq12×Aq22q2Aq12q2-2Aq1Aq2q2-AT2Aq2Tq-AT1Aq1TqAT1Aq2+AT2Aq1TqAT22T2AT12T2-2AT1AT2T2.
As mentioned above, the structure parameter coefficients AT and
Aq should be those formulated using specific humidity.
For the bichromatic-correlation method, the value of CTq can therefore
be used to effectively measure the temperature–humidity correlation
coefficient:
rTq=CTqCT2Cq2.
If MOST assumptions (i.e. T - q similarity) are satisfied, the Bowen ratio can
be calculated from the structure parameters (Andreas, 1990; Lüdi et al., 2005)
β=sgnCTqcpLvCT2Cq2.
When the cross-structure parameter has not been measured, the structure
parameters CT2 and Cq2 can be calculated from the
two-wavelength equations, after Hill et al. (1988):
CT2=Aq22Cn1n1+Aq12Cn2n2+2rTqAq1Aq2S2λCn1n1Cn2n2AT1Aq2-AT2Aq12T-2,Cq2=AT22Cn1n1+AT12Cn2n2+2rTqAT1AT2S2λCn1n1Cn2n2AT1Aq2-AT2Aq12q-2,
where S2λ is ±1. This choice of sign is an inherent
ambiguity of the two-wavelength method and represents two possible solutions
to Cn2n2 (Hill et al., 1988; Hill, 1997). For low β, when
humidity fluctuations dominate Cn2n2 then S2λ = +1,
whereas S2λ = -1 is required at larger β. The sign of
S2λ is not known a priori but must be assumed. Often the two solutions
for β indicate which is the most likely solution for the atmospheric
conditions and site characteristics (Hill, 1997).
When expressed as a function of β, a minimum in Cn2n2 is revealed
due to the coefficients AT and Aq having opposite signs at
millimetre wavelengths. The contribution of CTq to Cn2n2 (middle
term in Eq. 3) is negative when CTq > 0, so for moderate
β the terms in Equation 3 can cancel out leaving Cn2n2 close to
zero (Hill et al., 1988; Otto et al., 1996). In practice, zero Cn2n2
will not be observed because the instrument has a finite noise floor (and
rTq ≠ 1). Instead the scintillation signal may be close to, or
below, the detection limit of the instrument, resulting in reduced
sensitivity around the region of minimum Cn2n2 and a tendency for the
derived β to be biased away from (below, for S2λ = +1)
the value at which minimum Cn2n2 occurs. The problematic region is
expected to occur for β ≈ 2–3 (Leijnse et al., 2007; Ward et al., 2013b).
Obtaining structure parameters from eddy covariance
Conversion between spatial and temporal domains enables calculation of
structure parameters from point measurements, such as those from EC
instrumentation. The spatial structure function, Dyy_x,
can be written (e.g. Stull, 1988) as follows:
Dyy_x(δ)=[y(x+δ)-y(x)]2‾.
Analogously the temporal structure function, Dyy_t, is given by
Dyy_t(τ)=[y(t+τ)-y(t)]2‾,
where τ is the temporal separation and y(t) is the value of the variable
at time t. Bosveld (1999) gives the conversion between temporal and
spatial structure functions using the horizontal wind vector, U, and the
variances of the three wind components, σu,v,w2:
Dyy_x(δ)=Dyy_t(δ/U)1-19σu2U2+13σv2U2+13σw2U2.
Thus temporal structure functions (Eq. 12) can be calculated from EC
measurements, converted to spatial structure functions (Eq. 13) and
then structure parameters (Cy2 = Dyy_x(δ)δ-2/3 defines Cy2 for δ in the
inertial subrange). Unlike fluxes, structure parameters are strongly height dependent.
Aerial photograph (2009, GeoPerspectives©) of the study area
showing the locations of the two-wavelength scintillometer path (BLS–MWS),
eddy covariance station (EC) and two meteorological stations (METsub,
METroof). The location of Swindon within the British Isles is shown
(top right panel).
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The instrumental setup. For the scintillometers the mean height of the beam
above the land surface (zm) is given (for the effective measurement
height (zef) see Table 2); for METroof
the heights above the roof surface are given. Roughness length, z0, and
displacement height, zd, were not calculated for the rooftop site.
Tx denotes transmitter, Rx receiver.
Instrumentation
Height [m]
Location
Path
z0
zd
length
[m]
[m]
[m]
Two-wavelength
44.3
51∘36′33.9′′ N 1∘47′38.6′′ W (Tx)
5492
0.7
4.9
scintillometer system
51∘33′38.1′′ N 1∘46′55.3′′ W (Rx)
EC station
12.5
51∘35′4.6′′ N 1∘47′53.2′′ W
–
0.5
3.5
METsub
10.6 (WXT)
51∘35′4.6′′ N 1∘47′53.2′′ W
–
0.5
3.5
10.1 (NR01)
METroof
2.0 (WXT)
51∘34′0.3′′ N 1∘47′5.3′′ W
–
–
–
1.1 (NR01)
Experimental details
Instrumental setup and site description
A millimetre-wave scintillometer (MWS) (Evans, 2009), designed and
built by the Centre for Ecology and Hydrology (CEH) and Rutherford Appleton
Laboratory (RAL), was used in combination with a commercially available
large aperture infrared scintillometer, the BLS900 (Scintec, Rottenburg, Germany).
Both scintillometers were installed on a 5.5 km path over the town
of Swindon, UK. The path is orientated approximately north–south
(170∘) and extends from the edge of the settlement to the town
centre (Fig. 1). An EC system, consisting of a sonic anemometer (R3, Gill
Instruments, Lymington, UK) and open-path infrared gas analyser (IRGA) (LI-7500,
LI-COR Biosciences, Lincoln, USA) mounted at 12.5 m above ground level (a.g.l.),
was positioned near the centre of the path. The EC site was equipped with an automatic weather station
(WXT 510/520, Vaisala, Finland) which provides the additional input data
required to process the scintillometer data; temperature, relative humidity
(RH), pressure and wind speed (U) are measured at a height of 10.6 m. A
four-component radiometer (NR01, Hukseflux, The Netherlands) was installed
at 10.1 m on the same mast and a tipping bucket rain gauge (0.2 mm tip,
Casella CEL, Bedford, UK) near the base of the mast. These were located in
the garden of a residential property approximately 3 km north of the town
centre, where the surrounding land use is predominantly residential,
consisting of 1–2 storey houses with gardens. Full details are given in Ward
et al. (2013a). In addition to the meteorological
instrumentation at the EC site (METsub), a second weather station was
established on the rooftop of a modern office building close to the town
centre (METroof). The setup is summarised in Table 1. To provide a combined data set of
continuous input variables required for scintillometry processing, T, RH, p
and U from METroof were linearly adjusted to gap-fill METsub
(required for < 1 % of T, RH and p and < 2 % of U data),
based on regressions with concurrent data from METsub (9 May 2011–31 December 2012).
Northern Swindon is typical of suburban areas in the UK. The area has a
relatively large proportion of vegetation and there is a large nature
reserve just north of the centre of the study area, which lies directly
underneath the scintillometer path. The town centre at the south of the
study area has the highest density of buildings and roads
(Fig. 1). Industrial areas to the east and
southwest with little vegetation contribute to measurement source areas
under stable conditions (see Part 2). Despite the variety of land-cover
types, many neighbourhoods appear fairly homogeneous at a scale of a few
hundred metres. The area shown in Fig. 1 has land
cover that is 14 % buildings, 31 % impervious, 53 % vegetation, 1 %
water and 2 % pervious. Land cover, topography and building and tree
heights were derived from a spatial database for Swindon (5 m resolution,
see Ward et al. (2013a) for more information).
Instrument and site-dependent characteristics of the scintillometers and
paired scintillometer system.
Instrument characteristics
Site-dependent characteristics
Scintillometer
Wavelength
Aperture
Fresnel
Effective
Scaling (S)
[m]
diameter
zone
height
factor
[m]
[m]
[m]
BLS (Cn1n1)
880 × 10-9
0.145
–
45.0
–
MWS (Cn2n2)
3.2 × 10-3
0.25
4
42.8
0.952
BLS–MWS (Cn1n2)
–
–
–
43.1
0.958
Cross-section of the land surface (metres above sea level) and height of
obstacles (buildings and trees) along the BLS–MWS path. Tx denotes
transmitter, Rx receiver.
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To obtain representative measurements it is important to be high enough
above the surface that quantities are sufficiently well-blended (i.e. high
enough that turbulent mixing averages out the influence of surface
heterogeneity), although recent studies have shown successful use of
scintillometers even below the blending height (Meijninger et al., 2002b;
Ezzahar et al., 2007). Based on the average height of the roughness elements
(i.e. buildings and trees) the blending height is estimated at about 15–30 m
for the BLS–MWS source area (Pasquill, 1974; Garratt,
1978). The scintillometer transmitters, mounted on custom-built brackets,
were installed at 28 m a.g.l. on a television transmitter mast. The
receivers of the BLS–MWS system were mounted at 26 m a.g.l. on a rooftop in
Swindon town centre. The resulting path is slanted (Fig. 2).
The effective heights of the scintillometers are given in
Table 2, calculated according to the
stability independent approximation (Eq. (15) of Hartogensis et al., 2003). These estimates include adjustment to account
for the curvature of the earth and displacement height, zd
(Table 1). Displacement heights were calculated
from the mean height of the roughness elements, zH, within a distance of
±1000 m perpendicular to the scintillometer path (and +500 m in the
direction parallel to the path), using the rule-of-thumb zd = 0.7 zH
(Garratt, 1992; Grimmond and Oke, 1999). In the case of the EC
station, zH was calculated within 500 m of the EC mast. The difference
in BLS and MWS path-weighting functions (Fig. 3)
means that the BLS, MWS and combined BLS–MWS covariance measurements are
representative of different heights even though the BLS and MWS beams
essentially traverse the same path (Evans and De Bruin, 2011).
For the two-wavelength scintillometer system the BLS and MWS beams must be
close together (Lüdi et al., 2005). The separation between
beams was minimised (< 0.35 m), and the relative positions of the BLS
and MWS were reversed at each end of the path so that the beams crossed near
the centre of the path. It was necessary to shield cables at the
scintillometer sites to protect against electrical interference.
Scintillometer data can sometimes be affected by vibrations of the mounting
structures (Von Randow et al., 2008; Beyrich et al., 2012). However,
mounting brackets were designed with this in mind and the scintillometer
spectra show little evidence of any vibrational contamination.
The data presented here are for the complete months when the BLS–MWS system
was functioning: July–December 2011 and May–December 2012. From January 2012
to April 2012 the MWS was not operational due to a fault.
Path-weighting functions for the infrared scintillometer (BLS disk), the MWS
and the BLS–MWS combination, normalised so that the total area under each
curve equals one.
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Data collection, processing and quality control
Scintillometry
In this study, Cn1n1 is used to denote the refractive index structure
parameter from the BLS, to distinguish from Cn2n2 (MWS) and Cn1n2
(BLS–MWS cross term). The BLS900 is a dual-beam scintillometer with two
transmitter disks (only one disk is used here for combination with the MWS).
The signal intensity of each BLS disk was sampled and stored at 500 Hz (raw
data) and statistics including the mean and standard deviation of signal
intensity were provided at 30 s intervals by the Scintec software (SRun v1-07).
Additionally, the signal intensities of both BLS disks and of the
MWS were sampled at 100 Hz by a CR5000 datalogger (Campbell Scientific Ltd.,
Loughborough, UK). These data were processed using code written in R (The
R Foundation for Statistical Computing). Data were subjected to initial
quality control involving the removal of dropouts (when the BLS makes a
background measurement) and despiking. The BLS and MWS signals were bandpass
filtered to remove contributions below 0.06 Hz and above 20 Hz for the
calculation of Cn2n2 and Cn1n2. The low-frequency cut-off reduces
the influence of absorption fluctuations. At 10 min intervals, the variances,
covariance and mean values of the signals were calculated, from which the
log-amplitude variances (σχ12, σχ22) and covariance (σχ1χ2) were obtained
(Tatarski, 1961). To convert between the log-amplitude (co)variances and
refractive index (cross-)structure parameters the following equations were used:
Cn1n1=4.48D7/3L-3σχ12,Cn2n2=8.33k-7/6L-11/6σχ22,
Cn1n2=8.93k-7/6L-11/6σχ1χ2,
where D refers to the aperture diameter of the infrared scintillometer, k to
the wave number (2π/λ) of the millimetre-wave scintillometer
and L to the path length (Tables 1 and 2). These equations can be derived from the
full forms of the log-amplitude (co)variances, which express the path
weighting of the instrument, the aperture averaging by the finite size of
transmitter and receiver, the turbulence spectrum (assumed to be the
Kolmogorov spectrum, e.g. Monin and Yaglom, 1971) and the separation of
the beams if applicable (Eqs. A1 and A2, Appendix A). Note that Eq. (14c) is specific to the setup described here (beam
separation, path length and instrument characteristics) and could be
expressed in terms of D rather than k. Equation (14b) was obtained using the
full formula (Eq. A2) instead of the small aperture approximation (in
which the Bessel functions accounting for aperture averaging are excluded)
and is also specific to this setup. The approximation can result in an
inaccuracy even for long paths (Appendix A).
Equation (14a) for the infrared scintillometer is a standard result and was
first demonstrated by Wang et al. (1978).
Quality-control procedures rejected data during periods of low signal
strength (usually caused by rain or fog). BLS data were rejected when the
received signal intensity dropped below 0.5 of the daily maximum value. For
the MWS a threshold of 0.33 of the daily maximum intensity was used, and MWS
data were also removed when the BLS signal intensity was below the
0.5 threshold indicating obscuration along the BLS–MWS path. The data points
directly adjacent to those failing the signal strength checks were also
removed. Rain was recorded at the EC site for 10 % of the data set and fog
often occurred, particularly during autumn and winter mornings (based on
observations during site visits). Values of Cn2 outside reasonable
thresholds were excluded (nine Cn1n1 values and one Cn1n2 value).
The resulting data available for analysis constitute 79 % of the total
possible 10 min values (N = 61 776).
Kleissl et al. (2010) suggest an empirical threshold for the
onset of saturation for infrared large aperture scintillometers of
Cn1n1 > 0.074 D5/3λ1/3L-8/3.
Approximately 21 % of the BLS data were above this threshold of
3.2 × 10-15 m-2/3. BLS data were corrected for
saturation using a look-up table of numerical values based on the modulation
transfer function of Clifford et al. (1974). The correction
increased Cn1n1 by around 5 % overall (∼ 10 % during summer
daytimes). Where the estimated correction was larger than 25 %, data were
removed instead (46 values in total). MWS data were well below the
saturation threshold of 5.0 × 10-11 m-2/3
(Clifford et al., 1974) and were not corrected. The BLS–MWS
covariance was not corrected as a methodology is yet to be determined
(Beyrich et al., 2012). There is therefore increased uncertainty
in these measurements due to the extent of currently applicable theory.
To use the refractive index structure parameters in Eq. (6) (or Eq. 10a–b),
Cn1n1, Cn2n2 and Cn1n2 must be representative of the
same height. The S factors (Evans and De Bruin, 2011) given in
Table 2 were applied to Cn2n2 from the MWS and
Cn1n2 from the BLS–MWS to scale them to the same effective height as
Cn1n1 from the BLS. These factors account for the difference in
effective heights between the three Cn2 measurements resulting from
the combination of different weighting functions and changing beam elevation
along the path (Sect. 3.1). The S factors are
relatively close to unity as the height differences are reasonably small for
this setup. The approximation of using stability independent S factors was
made here (incorporating stability would give values ranging from 1.0 under
very stable conditions to 0.9 for free convection). Calculation of the
meteorological structure parameters proceeds as described in Sect. 2.
Data were processed using each of the three techniques in order to
investigate their respective merits, although the focus is on the
two-wavelength and bichromatic-correlation methods. No Bowen ratio
correction was applied for the single-wavelength method (Eq. 4), given
the uncertainties in estimating the available energy in urban environments.
The impact is estimated to be a 6 % overestimation in CT2 (based
on β from the two-wavelength method). The positions of twice-daily
minima in Cn1n1 were used to indicate stability transitions
(Samain et al., 2012a) and assign positive or negative
rTq for the two-wavelength method. For the two-wavelength method
rTq = ±0.8 was assumed and the solution corresponding to
S2λ = +1 was chosen. To distinguish between the methods
applied (single-wavelength, two-wavelength, bichromatic-correlation), the
subscripts “1λ”, “2λ” and “bc” are used.
Example power spectral density (PSD) and frequency (f) spectra for (a, b)
the BLS and (c, d) unfiltered MWS for 14:30–15:00 UTC on 2 July 2011.
Smoothed spectra (data divided into 100 bins) are shown in black. The dashed
lines represent theoretically predicted slopes of -12/3 and -8/3 for the BLS
and MWS, respectively.
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Eddy covariance
Structure parameters were also derived from 20 Hz raw EC data. Sonic and
IRGA data were time aligned by seeking maximum covariance between variables.
Initial quality control incorporated threshold checks, outlier detection and
despiking. A fixed temporal separation of τ = 1 s was decided upon
after investigation into suitable values in the inertial subrange.
Calculation of CT2, Cq2 and CTq included the Schotanus
et al. (1983) correction for sonic temperature as discussed
in Braam (2008) and Braam et al. (2012). No corrections
were made for spectral losses caused by the spatial separation of the sonic and
IRGA and their finite path lengths, which can be expected to result in
underestimations of about 5–7 % (Hill, 1991; Hartogensis et al., 2002).
The resulting 30 min structure parameters were quality controlled in
accordance with the corresponding EC fluxes. Data were removed during times
of known instrument malfunction, when the IRGA diagnostic indicated
obstruction of the optical path, when rainfall could adversely affect the
measurements and if values exceeded physically reasonable ranges.
To facilitate comparison between EC and scintillometer data sets, the EC
structure parameters were scaled to match the height of the scintillometer
data using MOST functions (with the constants suggested by
Andreas (1988) and assuming identical height scaling
of temperature and humidity, see Eq. 3a and b in Part 2).
Since the source areas are different between EC and BLS–MWS systems, the
structure parameters obtained are not expected to be in perfect agreement.
The BLS–MWS footprint is generally more vegetated than the EC footprint
(56 % versus 44 %). However, comparisons are useful for a number of
reasons. As EC is a widely used technique, it permits evaluation of the
scintillometer system to a certain extent, through comparison of trends and
patterns of behaviour even if absolute values differ. Both systems have
merits and limitations: scintillometers are spatially representative; EC is
a more direct measurement but (open-path IRGAs) cannot provide data after
rainfall and there are issues with energy closure (Foken, 2008). Analysis
of both approaches can therefore provide a more complete picture of the
environment studied.
Results and discussion
Structure parameters
Refractive index structure parameters
The refractive index structure parameters measured by the BLS (Cn1n1)
and MWS (Cn2n2) and the cross-structure parameter from the covariance of
the BLS–MWS signals (Cn1n2) follow clear diurnal cycles. Data for an
example day are plotted in Fig. 5. Whilst
Cn1n1 and Cn2n2 remain positive, the cross-structure parameter can
be positive or negative depending on whether the infrared and
millimetre-wave signals are correlated or anti-correlated. Cn1n2 tends
to be negative during the day and positive at night. The sign change occurs
at the morning and evening stability transitions. Typically Cn1n1 passes
through sharp minima at these times, whereas the diurnal course of
Cn2n2 is flatter and wider, without clearly defined minima. These
findings are in broad agreement with data from the LITFASS campaigns
(Beyrich et al., 2005, 2012; Lüdi et al., 2005).
Structure parameters of the refractive index as measured by (a) the BLS and
(b) the MWS, and (c) the cross-structure parameter as measured by the
BLS–MWS combination for an example day (22 August 2012).
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When averaged by month (Fig. 6), the diurnal
patterns are enhanced and seasonal trends are revealed. The amplitudes of
the diurnal cycles are largest in summer, except Cn1n1 which peaks
slightly earlier in the year. Cn1n1 is closely related to
CT2 and, in turn, the sensible heat flux. Hence the peak in
Cn1n1 in late spring reflects the annual cycle of QH: approaching
maximum insolation in mid-summer, the total energy input is large but
evapotranspiration rates are limited by phenological development as maximal
leaf area is not reached until later in the year. During winter, low
radiative input means the midday maximum in Cn1n1 is small; larger
values are observed at night. For millimetre wavelengths, Cn2n2 tends to
remain low throughout the night. The diurnal cycle in Cn1n2 is
maintained across all months with changes in the position of the
zero crossings determined mainly by atmospheric stability (usually the sign
of QH, related to available energy and day length). In December
Cn1n2 is negative only for a very short period during the middle of the
day. The effect of day length is also seen in the positions of Cn1n1
minima, which move closer together as radiative energy input decreases from
summer to winter. Using the positions of Cn1n1 minima to indicate the
stability transition times generally worked well for this data set, although
performance was poorer in winter when stability changes tend to be less well
defined and conditions may remain close to neutral throughout the day.
Following the method described in Samain et al. (2012a),
additional restrictions were imposed on the times of morning (here after
04:00 UTC) and evening (before 21:00 UTC) transitions. In a few cases,
cumulative daily net radiation was also used to distinguish Cn1n1 minima
that were due to sudden cloud cover during daytime from Cn1n1 minima
that were due to a change of stability.
Meteorological structure parameters
There are clear parallels between the refractive index structure parameters
(Fig. 6) and the meteorological structure parameters (Fig. 7). Cn1n1 is dominated by
CT2 with a small contribution from CTq (of about 5 %, which
decreases as β increases; Green et al., 2001). The cross-structure
parameter Cn1n2 consists mostly of the CTq term and a contribution
from CT2. When Cn1n2 is negative CTq is positive. The
dominant term in Cn2n2 depends on the Bowen ratio: Cq2 usually
dominates at low β, but the CTq term is also important and forms a
negative contribution to Cn2n2 during daytime (when CTq is positive).
Comparison of eddy covariance and scintillometry techniques
In Fig. 8, meteorological structure parameters
for individual days are compared to EC. As structure parameters are strongly
height dependent, the EC values have been scaled to match the height of the
scintillometry results (zef = 45.0 m, Sect. 3.2.2). Scintillometer and EC values of
CT2 and Cq2 are not expected to agree exactly due to the
differences in source area and land-cover composition; however, these
independent measurements are often remarkably consistent. The correlation
between the scintillometer and EC data sets gives confidence that the
scintillometer setup is responding reasonably to changes in boundary layer
conditions and measuring within the surface layer. The square of the
correlation coefficient (r2) between the two-wavelength scintillometry
and height-scaled EC data is 0.72 and 0.60 for CT2 and
Cq2, respectively. Considering daytime only, r2 increases to 0.86
for CT2, but remains about the same for Cq2 at 0.56.
These results suggest βEC is larger than
βBLS–MWS, but the fact that the estimates of
CT2 are closely matched while Cq_BLS–MWS2 is
larger than Cq_EC2 is indicative
of a more complex situation (see Part 2).
Median diurnal cycles and inter-quartile ranges (grey shading) of the structure
parameters of the refractive index as measured by (a) the BLS and (b) the
MWS, and (c) the cross-structure parameter as measured by the BLS–MWS
combination, separated by month. Yellow shading indicates periods when
Q* > 0 W m-2.
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Median diurnal cycles and inter-quartile ranges (shading) of the
meteorological structure parameters (a) CT2, (b) Cq2 and
(c) CTq calculated using the bichromatic (bc) and two-wavelength
(2λ) techniques. In (c) CTq for the two-wavelength technique
is given by ±0.8 (CT2 Cq2)1/2.
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Structure parameters of temperature and humidity and the
temperature–humidity correlation coefficient for selected days, derived from
eddy covariance measurements (EC) and from the BLS–MWS system using the
single-wavelength (1λ), two-wavelength (2λ) and
bichromatic-correlation (bc) methods. EC structure parameters are shown for
the EC measurement height (thin line) and scaled to the effective height of
the scintillometry results (thick line). Unstable times according to the EC
data (LOb < 0) are indicated by grey dots. Single-wavelength
and two-wavelength data are for 10 min intervals; EC and
bichromatic-correlation data are for 30 min intervals.
![]()
Mean daytime (incoming shortwave radiation K↓ > 5 W m-2)
(a, b) structure parameters and (c) Bowen ratio calculated
according to Eq. (9) for each month calculated for the BLS–MWS system
(bichromatic-correlation and two-wavelength approaches) and eddy covariance
data scaled to match the BLS–MWS height. For the two-wavelength results
rTq_2λ = ±0.8; error bars in (a) and (b)
indicate the effect of assuming rTq values of ±0.5 and
±1.0. Light grey bars (a, b) indicate means calculated when both
CT2 and Cq2 from all three methods (bc, 2λ and
EC) are available.
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Structure parameters from the BLS–MWS and EC systems exhibit the same trends
(but have different magnitudes) over the course of the year
(Fig. 9). CT2 is largest during late
spring, whilst Cq2 peaks in July and August. There is variability
between years attributed to drier conditions in 2011 than 2012 (July–August
rainfall was 110 mm in 2011, 184 mm in 2012). In July–August 2011,
CT2 was larger and Cq2 smaller than in the same months in
2012. In general, 2012 was much wetter than 2011, which explains consistently
lower β in 2012 (Fig. 9c). The BLS–MWS
gives lower β compared to EC (during summer daytime
βBLS–MWS ≈ 0.5 and βEC ≈ 1.0), which is
in accordance with the BLS–MWS footprint being more vegetated, but similar
seasonal behaviour is seen. In winter β becomes negative as a result
of reduced radiative input, and the difference between βBLS–MWS
and βEC decreases. In terms of fluxes, evapotranspiration
continues throughout winter because moisture is readily available, whereas
energy is limited so QH is directed towards the surface for much of the
daytime, only becoming positive for a short time around midday
(Ward et al., 2013a). Although it is more straightforward to
consider this behaviour in terms of the fluxes (discussed further in Part 2),
the same conclusions could be inferred from the diurnal course of
rTq (Fig. 10) and positions of Cn1n1 minima (Fig. 6a).
The performance of EC and scintillometry differs with atmospheric
conditions. EC data from open-path gas analysers cannot be used if the
instrument windows are wet, such as during and after rainfall
(Heusinkveld et al., 2008). Consequently, water vapour
measurements from open-path systems significantly under-represent these
times and may result in an appreciable underestimation of mean QE
(Ramamurthy and Bou-Zeid, 2014) and Cq2. Mean values
for CT2 calculated using daytime data only when all quantities are
available concurrently (i.e. CT2 and Cq2 from EC,
two-wavelength and bichromatic-correlation methods – grey bars,
Fig. 9) are larger compared to mean values
calculated using all available daytime data (coloured bars) due to the
exclusion of periods during and directly following rain (when
CT2 is typically relatively low QE is relatively high). In
September 2012 the opposite effect is seen because the EC data were limited
by dirty IRGA windows when the weather was dry and sunny; hence it is
generally high CT2 values that are eliminated in this case. During
summer, monthly mean Cq_BLS–MWS2 is reduced
slightly for the concurrent data set (grey bars) as times of high QE when
water and energy are plentiful have been excluded. The overall results are
not substantially changed for the restricted subset:
CT_BLS–MWS2 and
CT_EC2 are still similar, whilst
Cq_BLS–MWS2 still exceeds
Cq_EC2, although the difference between
Cq_BLS–MWS2 and
Cq_EC2 is reduced slightly. The spectral
correction (Sect. 3.3.2) would increase the EC
structure parameters by 5–7 %, which would further reduce the discrepancy
between EC and BLS–MWS values.
As for most previous two-wavelength campaigns, typical Bowen ratios for this
path are expected to lie below the problematic region of minimum
Cn2n2 (Sect. 2.1). Other urban studies
suggest daytime β of around 1.0–1.5 for suburban sites, lower when
precipitation is frequent (Grimmond and Oke, 1995)
and strongly dependent on the amount of vegetation (Grimmond and Oke,
2002; Christen and Vogt, 2004). However, although average β remains
below 1.5 (Fig. 9c), changing surface conditions
can drive down the evapotranspiration on the timescale of a few days
(e.g. drying of impervious surfaces; Ward et al., 2013a, or at
agricultural sites, senescing crops; Evans et al.,
2012), producing substantial excursions from the average β. Such
excursions are seen in this data set, particularly for βEC,
whereas β2λ seems to be limited to values ≤ 1.3.
There are two potential issues here: (a) the two-wavelength sign ambiguity
and (b) the region of reduced sensitivity around the Cn2n2 minimum
(Sect. 2.1). Selecting S2λ = +1
automatically restricts β2λ to values below that at which
the terms in Equation 3 cancel out (β2λ_min = 2.0–2.6
for this data set). For a few cases (when β is
large), the “true” structure parameters should be obtained from the
alternative solution (S2λ = -1). Using the
bichromatic-correlation results to identify times of high β suggests
the impact is small (βbc > β2λ_min
for a very small proportion of the data (< 0.3 % of daytime values)). A more significant issue appears to be reduced
measurement capability for β2λ > 1.3. As the
true β increases, if Cn2n2 does not decrease as much as expected
from theory, β2λ will likely be underestimated, and the
corresponding Cq2 (and QE) could be overestimated. It is
thought that noise in the setup (instrumental or unwanted intensity
fluctuations from absorption, for example) may constrain the measured value
of β2λ to a greater extent than suggested in the literature.
This region of reduced sensitivity of Cn2n2 also compromises the
performance of the bichromatic method, as measured Cn2n2 will be mostly
made up of noise contributions relative to the near-zero true value of
Cn2n2. It follows that the correlation between BLS and MWS signals in
this region is expected to be dominated by common instrumental or
atmospheric effects such as absorption, or any electrical interference,
mounting vibrations or obscuration along the path. Although these effects
were not found to be problematic generally, they could become significant
when the refraction signal diminishes at moderate β.
Comparison of two-wavelength and bichromatic-correlation methods
The three estimates of CT2 for the BLS–MWS path are similar (on
average within 6 %) whether the bichromatic-correlation, two-wavelength or
single-wavelength approach is used. Larger deviations are seen between
Cq_bc2 and Cq_2λ2 when measured rTq_bc differs from the value
assumed (±0.8) in the two-wavelength method (e.g. 21 August 2011,
Fig. 8). During daytime, Cq_2λ2 is slightly larger than
Cq_bc2 in 2011 but the opposite is true for
most of 2012 (Figs. 7b and 9b). This could be due to higher values of
rTq_bc in 2012 (see Fig. 10),
which result in smaller CT2 but larger Cq2 when
rTq > 0. The value of rTq has a greater impact on
Cq2 than CT2 (compare error bars in
Fig. 9a and b). Had rTq_2λ been assumed to be ±1.0 instead of ±0.8,
Cq_2λ2 would have been 7 % higher and
CT_2λ2 1 % lower.
During winter night-times, large differences are observed between
Cq_bc2 and Cq_2λ2 which
cannot be explained by differences in rTq. Negative
values of Cq_bc2 are frequently obtained
(Fig. 7b). These do not have a physical
interpretation but are indicative of measurement limitations and coincide
with large negative CTq_bc, which results from large
positive Cn1n2 (i.e. high correlation between BLS and MWS beams).
Particularly during these times, but also in general, the
bichromatic-correlation data show much greater variability than the
two-wavelength data. To reduce this variability, bichromatic-correlation
data are presented as 30 min averages (except in Fig. 7).
Monthly median diurnal cycles and inter-quartile ranges (shading) of the
temperature–humidity correlation coefficient calculated from the BLS–MWS
system via the bichromatic-correlation method and from EC.
![]()
Box plots of rTq from eddy covariance (EC) and the BLS–MWS
bichromatic-correlation method for (a) unstable and (b) stable conditions.
Crosses indicate the 10th and 90th percentiles, boxes enclose the
inter-quartile range (25th to 75th percentiles) and heavy lines indicate the medians.
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Temperature–humidity correlation
Measured rTq_bc follows similar seasonal and diurnal
trends to rTq_EC (Fig. 10).
During summer, there is a clear plateau of around 0.8 during daytime and the
transition times when rTq changes sign are of short duration compared to
the day length. In winter rTq remains below zero for most of the day but
peaks at positive values around midday, though the average midday rTq is
less than 1. The EC data rarely exceed the range -0.75 to 0.85, whereas
inspection of the time-series reveals that rTq_bc almost
always follows a typical diurnal course but individual points may vary about
the general trend (e.g. Fig. 8). Some unrealistic
values of |rTq_bc| > 1 are
observed, even when averaged to 30 min (Figs. 8 and 10). Lüdi et al. (2005) also
reported frequent occurrences of |rTq_bc| > 1. Since rTq
is a correlation coefficient, these values
(> 1) do not have a physical interpretation, but result from
uncertainties and thus indicate limitations of the measurement. The
uncertainty in Cn1n2 is around 20 % at best (Lüdi et
al., 2005). This inherent uncertainty associated with individual measurements
means the structure parameters from the bichromatic-correlation method show
greater variability than from the two-wavelength method (Sect. 5.1.4), particularly Cq2 and CTq which
have a greater dependence on Cn1n2 than CT2 does. For this
reason, bichromatic results are presented at 30 min, rather than 10 min as
for the two-wavelength method (Figs. 8–11). In addition, to calculate
rTq, the structure parameters must be combined (Eq. 8) and so
rTq amasses the uncertainties in each of the measurements.
Despite the high uncertainty, average rTq values provide information on
the typical temperature–humidity correlation along the scintillometer path.
This is an important quantity in its own right, as well as an indication of
MOST violations. The literature suggests rTq is typically expected to be
slightly less than +1 during the day, but negative and of smaller
magnitude at night (Kohsiek, 1982; Andreas et al., 1998; Meijninger et al.,
2002a; Beyrich et al., 2005). According to Lüdi et al. (2005)
the T - q anti-correlation observed at night is “less pronounced” than the
positive correlation during daytime; Meijninger et al. (2006) also found
rTq ranged between -0.5 and 0.9. Our measurements conducted in the
suburban environment do not indicate lower T - q correlation than over other
surfaces. Indeed, it is thought that rTq_EC likely
underestimates the true values (as the sonic and IRGA are spatially
separated). This supports the use of MOST, and gives confidence that the
measurements are made at sufficient height that the effects of surface
heterogeneity are well-blended. Furthermore, the two-wavelength assumption
rTq = +0.8 is seen to be reasonable for unstable conditions,
although probably rTq = -0.8 is too large during stable conditions.
Figure 10 suggests that these assumptions are less justified in winter.
In Fig. 11 values of rTq measured in this
study are separated into unstable and stable conditions (based on LOb
from EC data and the timing of stability transitions according to
Cn1n1 for the scintillometer data). During unstable conditions, measured
rTq is positive and around 0.6 to 0.9, whereas the stable values tend to
be smaller at around -0.3 to -0.5 and more variable. In winter rTq is
smaller and more variable (median rTq_EC decreases from
> 0.7 in summer to < 0.5 in December for unstable
conditions). Some studies have indicated a dependence of
temperature–humidity correlation on LOb, with larger deviations from
MOST as neutral conditions are approached (Li et al., 2011; Nordbo et al.,
2013), although other studies differ (De Bruin et al., 1993; Roth, 1993).
Part 2 further explores the scaling of temperature and humidity structure
parameters with stability.
Some instances of positive nocturnal rTq are observed when the fluxes have
the same signs, i.e. either unstable (QH > 0) conditions prevail,
or (more frequently) dewfall (QE < 0) is observed under stable conditions
(as on 21 August 2011 after 21:30 UTC, Fig. 8). In these cases the two-wavelength method
is limited as negative rTq must be assumed in the absence of other
information, whereas the bichromatic-correlation method often captures this
behaviour. In general, nocturnal structure parameters (and fluxes) tend to
be small so the absolute errors introduced by rTq_2λ of the wrong sign are fairly small, though they will always be biased in
the same direction and so will accumulate over time.
The bichromatic-correlation data suggest larger daytime rTq in 2012
compared to 2011, but this is not seen in the EC data. The contrast between
patches of hot, dry impervious areas and cool, wet vegetation may be reduced
by altogether wetter surfaces in 2012, which is thought to increase
correlation between temperature and humidity (Lamaud and Irvine, 2006;
Moene and Schüttemeyer, 2008; Ramamurthy and Bou-Zeid, 2014). On the other
hand, Lüdi et al. (2005) found lower rTq coincides with
lower β. Experiments designed to investigate the behaviour of
rTq (and the performance of the bichromatic-correlation technique) under
different conditions would be beneficial.
In winter, and also during night-time, turbulence tends to be less
well-developed. This presents challenges to measurement theory. Measurements
are also more likely to be outside the surface layer when stable
stratification occurs and the boundary layer height is smaller. Due to the
rough surface, near-neutral conditions were far more common than stable and,
for the most part, there remains a close match between the diurnal behaviour
of EC and scintillometer measurements. On the few occasions when strongly
stable conditions were observed at the EC station, a comparison of time series
indicated periods when the BLS–MWS was possibly above the surface layer and
thus potentially affected by other processes not related to surface fluxes.
However, these were relatively rare occurrences.
In this study, the performance of the bichromatic-correlation method was
generally observed to be poor under conditions of low crosswind speeds (the
wind speed component perpendicular to the BLS–MWS path). At times of low or
near-zero crosswinds (< 2 m s-1), the retrieved quantities are
less robust, |rTq_bc| > 1 is
commonly observed and rTq_bc is highly variable
frequently changing sign (even during the day when both turbulent fluxes are
reasonably expected to be positive and EC data do not suggest otherwise).
The reason for this is unknown, but two possible explanations are considered
here. Firstly, Taylor's frozen turbulence hypothesis is assumed in order to
relate intensity fluctuations to Cn2 (Clifford, 1971; Wang et al.,
1981). When crosswind speeds are low, this assumption is less justified and
eddies decay as they are slowly blown through the beam. Correlation between
the received scintillation pattern from one sample to the next is reduced
compared to higher crosswind cases (Poggio et al., 2000). Correlation
between the scintillation signals of the BLS and MWS will likely show
greater variation too, depending on how the decay of eddies affects each
beam, which would result in variability in Cn1n2 that propagates through
to rTq_bc. Secondly, when wind speeds are low, turbulence
may be sporadic and not well-developed over the whole path length. Perhaps
sudden gusts or turbulence events cause spuriously high correlation between
BLS and MWS signals which outweighs the correlated scintillation signal the
technique aims to measure. Given the complexity of this suburban site it is
difficult to draw firm conclusions on this apparent effect of crosswind on
bichromatic scintillometry at this stage. Given that the position of the
spectrum is known to change with wind speed (Medeiros Filho et al., 1983;
Nieveen et al., 1998; Ward et al., 2011), and that Cn2 can be
underestimated if the filter excludes part of the scintillation signal under
very low wind speed conditions (Solignac et al., 2012),
the suitability of the bandpass filter was also re-examined. However, the
choice of filter frequency did not seem to explain the poor performance and
modifying the filter frequencies did not resolve the issues. Small changes
in filter frequency generally did not produce substantial changes to the
results, suggesting that the frequencies chosen are suitable for this
data set, but it is not critical that those exact values are used.
Conclusions
This study reports the first use of a two-wavelength scintillometer system
in an urban area. The behaviour of structure parameters and
temperature–humidity correlation is investigated at various timescales. By
examining the structure parameters themselves, more direct insight into the
performance of the measurement techniques is gained; assessment of fluxes
introduces additional uncertainties (e.g. similarity functions).
Furthermore, structure parameters are important quantities in their own
right. The spatial and temporal resolution of scintillometry observations
offers advantages for assimilation into, or evaluation of,
hydro-meteorological models. One approach for achieving this would be to
work with structure parameters directly, rather than fluxes (e.g. Wood et al., 2013).
The structure parameters presented here extend previous observations to a
different climatic region, different land-cover type and, most importantly,
across a much longer time period. Summertime behaviour is broadly in
agreement with other published trials (Beyrich et al., 2005; Lüdi et
al., 2005) but the long time-series presented here offers insight into
seasonal variations. Day length, or more specifically, atmospheric
stability, has a distinctive impact on the diurnal cycle of structure
parameters and rTq. Overall, the structure parameters obtained from the
BLS–MWS and EC systems exhibit remarkably similar tendencies. The Bowen
ratio calculated from the measured structure parameters decreases across the
two summer-to-winter periods studied here, and βBLS–MWS is
smaller than βEC, attributed partly to different source area
characteristics but also probable differences between the observational
techniques. Part 2 explores energy partitioning further via turbulent fluxes.
As well as extending the two-wavelength technique to a new environment,
several recently developed improvements have been implemented in the
processing. To obtain the structure parameter of refractive index from the
millimetre-wave scintillometer, the validity of the small aperture
approximation is considered and the more accurate full formula used instead.
To adjust for the difference in effective heights between the BLS and MWS,
the S factor approach of Evans and De Bruin (2011) was used. The
structure parameters use the specific humidity formulation outlined in Ward
et al. (2013b). The cross-correlation between
BLS and MWS signals enabled estimation of the temperature–humidity
correlation coefficient, thus extending the application of the
bichromatic-correlation method to the suburban surface.
The bichromatic-correlation method sometimes returns values outside the
physically meaningful range |rTq| ≤ 1. The inherent
variability of the cross-structure parameter Cn1n2 limits the accuracy
of any particular measurement, whereas the two-wavelength results are more
robust over shorter periods. On average, results closely follow the expected
diurnal cycle of correlated temperature and humidity during the day and
anti-correlated at night. Measured rTq is approximately 0.6–0.9 in
unstable conditions; stable values are more variable but tend to be smaller
in magnitude, averaging around -0.3 to -0.5. Observed rTq was
furthest from ±1 during winter and in near-neutral conditions.
Similar behaviour is seen in rTq_bc and
rTq_EC, including times when the assumed two-wavelength
value will be wrong.
Two-wavelength scintillometer systems have considerable potential to deliver
large-area measurements representative of complex environments. Limitations
of the two-wavelength method include the ambiguity due to two possible
solutions for β and the region of reduced sensitivity around the
Cn2n2 minimum. Research is needed; firstly, to better understand the
behaviour in this region and secondly, to investigate improvements to the
instrumentation, setup or post-processing to minimise the impact. Advantages
of the bichromatic-correlation method include additional information about
the atmospheric conditions (e.g. the relative sign of the heat fluxes and
to what extent MOST conditions are satisfied).
For the minimal extra hardware requirements, the recommendation is therefore
to measure the bichromatic correlation even if the additional information is
not used directly in data processing.
The large proportion of vegetation and cool, wet summers helped to maintain
a low Bowen ratio in this study. In drier periods or at more built-up sites,
the results may have been less useable. Future work should focus on the
performance of two-wavelength scintillometry under different conditions
(notably low crosswind, high Bowen ratio, near-neutral stability).
Theoretical development (e.g. modelling studies exploring the impact of
differences in footprint and effective height between instruments) combined
with careful experimental testing is needed. Comparison data (structure
parameters and fluxes) from different methods will be required for thorough
evaluation of these techniques.