Quality control of eddy-covariance measurements, Workshop at Castle
Thurnau, November 15-17.

**Presiding;Thomas Foken, Universität Bayreuth**

**Co-sponsored; CARBOEUROFLUX/CARBOEUROPE, afo-2000/VERTIKO, and
EVA-GRIPS**

**Annotated review; H. Loescher, Oregon State University**

This workshop focused on instrument calibration and classification, and
data quality control criteria. The criteria were applied and assessed
across several research studies. The workshop also included discussions
on the current eddy covariance technique, as well as some innovative
approaches to emerging issues, for example; u\* corrections, nighttime
advection, and using LES to address the spatial heterogeneity of energy
balance. A new technique linked a QA/QC software package with source
area models to determine which locations within the source area
contribute to differing qualities of data was also introduced. Much of
these results are to be considered as preliminary, and are presented
here to invoke thought and feedback. Many presentations are available
at;

<http://www.geo.uni-bayreuth.de/mikrometeorologie/QC_Workshop/>

**Foken**-***overview-***introduced a classification scheme to determine
the level of standard for radiation and turbulence sensors. Further
discussion and recommendations regarding this classification scheme
followed throughout the meeting. Classification of radiation sensors is
based on time constant, offset, resolution, stability, and non-cosine,
temperature and spectral response (Kasten 1985, Brock 2001). Likewise,
turbulence sensors are classified into three categories (Foken and
Oncley 1995),

Table 1. Proposed classification of sonic anemometers.

+-----------------------------------+-----------------------------------+
| **Classification**                | **sensor**                        |
+-----------------------------------+-----------------------------------+
| Fundamental                       | Kaijo-Denki typ-A                 |
|                                   |                                   |
|                                   | CSAT3                             |
|                                   |                                   |
|                                   | NUWprobe (NCAR)                   |
|                                   |                                   |
|                                   | Solent HS                         |
+-----------------------------------+-----------------------------------+
| General flux use                  | Kaijo-Denki typ-B                 |
|                                   |                                   |
|                                   | Solent windmaster, R2, R3         |
|                                   |                                   |
|                                   | METEK USA1                        |
|                                   |                                   |
|                                   | Young 81000                       |
+-----------------------------------+-----------------------------------+
| Normal use                        | 2-D anemometers                   |
+-----------------------------------+-----------------------------------+

**Liebethal**-***comparisons of radiation sensors during EBEX-2000 and
VERTIKO measurement campaigns***-Classification of primary radiation
sensors are based on their ability to internally correct for within dome
heating, i.e. short-wave sensors Kipp-Zonen CM21 and Eppely PSP.
Differences between longwave sensors were associated with solar heating
effects. Net radiometers (Kipp-Zonen CNR1, Schultze, and REBS Q7.1)
generally overestimated nighttime R~n~ by \~10-25 w m^-2^, and
underestimated daytime R~n~ by 1-80 w m^-2^. Recommendations encouraged
in-house calibration of sensors to a primary standard that includes
checking calibration coefficients and body temperature corrections over
different conditions.

**Vogt**-***comparisons of radiation sensors***- The authors utilized a
variety of upward- and downward facing radiations sensors for an
intercomparison. Their findings include; that clouds can introduce 5-18
sec time constants in longwave radiation and data acquisition should
accommodate this, longwave data should be excluded in early morning
conditions when dew exists, differences in transmissivity exists between
domes of same and dissimilar manufacturers, and leakage of shortwave
radiation into longwave sensors exists and should be quantified.

**Mauder**-***comparison of sonic anemometers and humidity sensors
during EBEX-2000 and EVA-GRIPS experiment***.- For the EBEX-2000
campaign, 5 sonic anemometers (3 fundamental, 2 general flux) were
compared over irrigated cotton at a height of 4.7 m on different towers
\~ 10-15 m apart. Planar fit rotation scheme (Wilczak *et al.* 2001) and
path length correction (Moore, 1986) were used. Data was screened based
on Foken and Wichura 1986. The intercomparison utilized a single
reference instrument CSAT3 and used parameters
$\overline{u},\sigma_{w}^{2},u^{},\overline{w'T'}$to compare the other
3d anemometers. Intercepts did not differ from 0, and slopes were ± 3 %
with R^2^ \> 0.97 for each of the parameters from (another) CSAT3,
Solent HS and NUW(NCAR) anemometers. Regressions with the USA-1 probe
had a slope of 1.05, 0.93, 1.02, 0.85 for
$\overline{u},\sigma_{w}^{2},u^{},\overline{w'T'}$, respectively. The
Young 81000 anemometer tended to overestimate $\sigma_{w}^{2}$ by \~ 6%,
and only 88% of the variability in u\* was accounted for.

For the EVA-GRIPS- campaign, 3 CSAT3 and 4 USA-1 anemometers were used
for the intercomparison over 0.1 m grass canopy measured at 3.25 m \~ 10
m between towers. CSAT3 measured higher $\sigma_{w}^{2}$ by ca. 4-22 %
than did USA-1. Similarly, CSAT3 probes tended to measure \~ 10-20%
higher buoyancy flux ($\overline{w'T'}$). METEK anemometers account for
flow distortion with an internal 'head correction' switched on. The
calculation and effect of this correction is unknown, and should be open
for discussion. The Solent R2 and R3 probes are considered
omni-directional but limited evidence suggests flow distortion can occur
around stanchions that support the head.

Recommendationd for humidity sensors include, Licor IRGAs have less
inherent signal noise than kypton hygrometers, and the humidity sensor
for standard calibrations should be a dewpoint chilled mirror able to
run at 10 hrz.

**Lebs**-***comparisons of scintillometers and humidity
sensors***-Kypton hygrometers have greater random noise than Licor IRGAs
in measured water vapor. Moreover, variance among different kypton
hygrometers (of the same manufacturer) can differ by \~ 10 %. Pathlength
effects (Moore 1986) do not explain the apparent systematic bias seen
between sensors. While Licor IRGA comparatively measures lower absolute
concentrations in water vapor (\~ 11%), fluxes are generally the same
due to large non-correlated variance to w by the krypton hygrometers.

**Anthoni*-open and close path comparison-*** The authors compared the
response of the Li-cor closed-path 6262 and open-path sensors over
Pondersoa pine sites in Oregon. Anthoni et al. applied the high
frequency loss correction to the data collected from the closed-path
sensor as outlined by Goulden et al. (1996), and applied the WPL
corrections to the open-path data. The NOAA open path seemed to measure
higher fluxes at night than the Licor 6262 closed path. Concentration
measured by Licor 7500 and 6262 generally fall on a 1:1 line, but the
amount of variability accounted for was not reported. NEE estimated by
the closed path was \~ 20% **higher** than that measured by the open
path at night. In contrast, NEE estimated by the closed-path was \~ 20%
**lower** than that measured by the open path (7500) during the daytime.
NEE estimates from the closed-path remained \~ 35% **lower** when
integrating daily totals of NEE during summer periods with long
daylengths. Moreover, nighttime estimates from the closed path were
higher than the sum of integrated chamber measurements (from the
Metolius site), suggesting the overestimation of R~e~ by the closed path
system. Under near neutral conditions, open path sensors have difficulty
in measuring low frequency contributions. Whereas, closed path sensors
may miss high frequency components. The WPL corrections can be large, \~
100 % under certain conditions, and there is no significant difference
if applying this correction to 10 hz data or to the 30-min averages.

**Liebethal**-***Spectra and WPL correction***- an overview of the WPL
approach, and defined as a 1d flux unit conversion however, the authors
urge all sites to examine the entire control volume and to consider both
vertical and horizontal advection. They also recommend that
manufacturers report the frequency response and time constants of their
instrumentation.

**Mauder**-***rotations, planar fit and u\* correction*** --Authors
question if the planar fit approach is appropriate for both night and
day flow regimes? Or should separate datasets be used to calculate
rotations to estimate flow during the day and night. Is planar fit
appropriate for all wind directions? Some EUROFLUX sites still triple
rotation assuming that
$\overline{v} = 0,\overline{w} = 0,\text{and}\overline{w'v'} = 0$ the
30-min averaging time (Aubinet *et al.* 2000). During nighttime and low
windspeed conditions in EBEX-2000 (over short canopy cotton, flat
terrain), large rotation angles can occur using 2d rotation schemes. In
contrast, planar fit method maintains correction angles close to zero
shunting more turbulence into the w spectra. Hence, using the 2d
rotation approach under low windspeed conditions, an artificial
correlation can occur between horizontal and vertical wind components
and can double u\* when compared to estimates using the planar fit
method. Changing the method of rotation may change the u\* threshold.
Sites converting over to the planar rotation should re-calculate their
u\* threshold values.

**Beyrich**--***transformation buoyancy flux to sensible heat flux***-as
estimated by Kaimal and Gaynor 1991, if no crosswind corrections for
temperature are included;

$H = (M_{a}\frac{C_{p}}{V})(\overline{w'T_{s}'} - (3\text{.}\text{210}^{- 4} \ast T_{k} \ast \overline{w'q'}))$
Eq. 1

or with crosswind corrections

$H = (M_{a}\frac{C_{p}}{V})(\overline{w'T_{s}'} - (3\text{.}\text{210}^{- 4} \ast T_{k} \ast \overline{w'q'}) + 2\hat{u}u^{'}\frac{w^{'}}{\text{403}})$
Eq. 2

or as estimated by Schotanus *et al.* 1983 accouning for crosswind
effects

$H = (M_{a}\frac{C_{p}}{V})(\overline{w'T_{s}'} - (0\text{.}\text{51} \ast T_{k} \ast \overline{w'q'})) + 2\frac{\overline{T}\overline{u}}{\overline{c^{2}}}\overline{w'u'}$
Eq 3.

The middle term in eqs. 1-3 is assumed to equal

$0\text{.}\text{51} \ast T_{k} \ast \overline{w'q'} \simeq 0\text{.}\text{51}\frac{C_{p}\overline{T}}{L\beta}$
where $\frac{1}{\beta} = \frac{\Delta q}{\Delta\theta}$ Eq 4.

This in turn, assumes equal transfer functions for heat and water vapor,
i.e., K~H~ = K~q~

For sonic anemometers that measure temperature along the vertical axis,
Lui et al. 2001 suggests,

$H = (M_{a}\frac{C_{p}}{V})(\overline{w'T_{s}'} - (0\text{.}\text{51} \ast T_{k} \ast \overline{w'q'})) + 2\frac{\overline{T}}{\overline{c^{2}}}(\overline{u}\overline{u'w'}A + \overline{v}\overline{v'w'}B)$
Eq 5.

And for sonic anemomenters that measure temperature along orthogonal
axis (i.e., METEK USA1), A = B = 0.75, such that;

$H = (M_{a}\frac{C_{p}}{V})(\overline{w'T_{s}'} - (0\text{.}\text{51} \ast T_{k} \ast \overline{w'q'})) + 1\text{.}5\frac{\overline{T}}{\overline{c^{2}}}\overline{u}u_{}^{2}$
Eq 6.

where, M~a~ is mol wt of dry air, C~p~ is the specific heat of dry air,
V is the molar volume of air such that;

V = (R\*T~k~)/P, and R is the ideal gas constant, T~k~ is air
temperature, and P is atmospheric pressure.

T~k~ = T~s~/(1 + 3.210^-4^\*q) Eq. 7

Crosswind effects increase curvilinearly with increases in u/u\*, seem
to have a greater effect with increases in surface roughness, and better
fit the observed data. Overall the Schotanus transformation can account
for ± 20 % of the flux. Other factors that may influence heat flux at
high u/u\* values include, vibrations in the sonic, vertical and
horizontal flux divergences, and an oasis effect-stable stratification
during daytime hours.

**Letzel**- ***LES study of the energy balance closure problem with eddy
covariance technique***- LES model PALM (Raasch and Schröter 2001) was
used to assess 1) the spatial representativeness of sensible and latent
heat fluxes from a single point measurement (e.g. tower), and 2) the
systematic underestimation of surface energy balance closure by eddy
covariance. Model dimensions were 4 x 4 x 2 km with 250 x 250 x 250 m
grid cells, where each intersection was a virtual tower resulting in 200
virtual towers. Comparisons were made between LES method using spatial
Reynolds decomposition and the 'virtual tower' method using temporal
Reynolds decomposition, such that;

$F_{s} = w_{s}^{'}T_{s}^{'} + \underset{= 0}{\overset{\lbrack w\rbrack}{\underbrace{}}}\lbrack T\rbrack + w_{s}^{'}\lbrack T\rbrack + \underset{= 0}{\overset{\lbrack w\rbrack}{\underbrace{}}}\left\lbrack T_{s} \right\rbrack = \overline{w_{s}^{'}T_{s}^{'}} + \overline{\underset{= 0}{\overset{w_{s}^{'}}{\underbrace{}}}\lbrack T\rbrack}$
no large scale vertical motion

$F_{t} = w_{t}^{'}T_{t_{s}^{'}}^{'} + \overline{\text{wT}} + \underset{= 0}{\overset{w_{t}^{'}}{\underbrace{}}}\overline{T} + \overline{w}\underset{= 0}{\overset{T_{t}^{'}}{\underbrace{}}} = \overline{w_{t}^{'}T_{t}^{'}} + \overline{\text{wT}}$=EC
flux and mean flow contribution

Without modeling geostrophic winds, the distribution of the probability
function was negatively skewed against the difference between these two
methodologies (LES vs virtual tower) over a 1 h averaging period. The
differences between these two methods were expressed as a ratio.
Lengthening the averaging period to 3 h reduced the magnitude of the
probability function and the skewness. Though the distribution remained
slightly skewed. Modeling ambient surface winds from 1 to 4 m s^-1^
increased the probability function and reduced the skewness, i.e.,
deceases the imbalance closer to the canopy.

Letzel *et. al*. found that, i) 10-20 towers were needed to reduce the
uncertainty to within 10%, ii) imbalance exists even for homogeneous CBL
resulting in limitations for single point measurements, iii) longer
averaging time slightly reduces the imbalance, iv) higher ambient winds
strongly reduces the imbalance, v) the imbalance increases with height.
Even though these approach is produce robust results and provides future
directions from EC research, it lacks the use of actual turbulent times
series and no high frequency turbulent characteristics.

**Foken**-***QA/QC for micromet and flux measurements-***an overall
approach to assessing QA/QC in a dataset and links the assessment to a
program that flags data dependent on the level of quality. The QA/QC
assessment begins with defining the appropriate tests and criteria as
outlined in Moncrieff *et al.* 1997, Aubinet *et al.* 2000, Foken *et
al.* 1997, Müller *et al.* 1995. Criteria for wind statistics are found
in DeGaetano 1997, Radiation in Gilgen *et al*. 1994, profile systems in
Handorf 1996, Turbulent tests in Foken and Wichura 1996, and addition
raw data, steady state and turbulence tests can be found in Mahrt 1991,
Vickers and Mahrt 1997, Kaimal and Finnigan 1994, and Højstrup 1993.
Limitations to quality control tools include the inability to identify
exact causes for uncertainty. Further tests are needed to identify
canopy coupling/decoupling, and interpretion of more complex QC analyses
(wavelet, spectra etc..) needs advanced micromet experience and cannot
be automated QC by programs.

**Göckede**-Linked the Foken QA/QC software package with source area
models to determine which locations within the source area contribute to
flagged data. Source weight function and roughness length were
determined for 30-min averages to match EC data. 2-d land use
classification and topography was determined from remote sensed data.
Two source area models were used; FSAM analytical model (Schimd *et al.*
1997, Schmid 1994) and Forward Lagrangian Model (Rannik *et al.* 2000).
Relative contributions to the source area derived from the Lagrangian
model were of greater magnitude closer to the tower as those from FSAM,
i.e smaller footprint. Both models preformed well, but need to be
interpreted differently,

Table 2. Some assumptions and characteristics of the two source area
models.

  ----------------------------------- -----------------------------------
  Schmid 1994                         Rannik *et al.* 2000

  Analytical model                    Lagrangian stochastic model

  Homogeneous turbulence              Inhomogeneous turbulence

  No along-wind dispersion            Includes along-wind dispersion

  No canopy                           Considers canopy effects

  Sources are at the ground           Vertical source distribution
  ----------------------------------- -----------------------------------

**Montagnani**-Estimated advection with 3 profile towers measuring
CO~2~, windspeed and temperature from an open-canopy coniferous forest
on a \~ 10° slope. Fetch to the south was large and homogeneous, fetch
to the north was fragmented and patchy. Daytime horizontal flow was
upslope from the south, nighttime flows were downslope from the north.
During the daytime hours advection was negligible, using the approach
outlined in Aubinet *et al.* 2000. Spatial heterogeneity of CO~2~
concentrations play a key role in determining horizontal advection.
CO~2~ concentrations generally increased downslope, and on average,
horizontal advection flux exported carbon downslope. Vertical motions
also advected carbon out of the system. The sum of vertical and
horizontal advection indicates the potential of large amounts of carbon
being exported from this system. Nighttime turbulent fluxes were often
negligible compared to advection fluxes. Stable nights temperature
inversions occur, density gradients develop and catabatic flows develop.
Horizontal advection upslope was \~ 3.5 times greater than downslope
components. Independent measures of respiration (scaled chamber
measurements) were consistently higher than the combination of eddy
corvariance, storage and advection fluxes.

**Cessatti**-results from the same site Montagnani reported from,
Cessatti and colleagues estimated vertical advection using two
approaches. If

$V_{\text{adv}} = \int_{0}^{z_{r}}{\overline{w}\frac{\partial\rho_{c}}{\partial z}}$
Eq. 8

then, approach 1 is defined by,

$V1_{\text{adv}} = {\overline{w}}_{r}\left( {\overline{\rho}}_{r} - \frac{1}{z_{r}}\int_{0}^{z_{r}}{\overline{\rho}(z)\partial z} \right)$
Eq. 9

and approach 2 defined by,

$V2_{\text{adv}} = {\overline{w}}_{r}{\overline{\rho}}_{r} - \int_{0}^{z_{r}}{\frac{\partial\overline{w}}{\partial z}\overline{\rho}(z)\partial z}$
Eq. 10

Both approaches utilize planar fit rotation scheme. Approach 1 assumes a
linear decrease in $\overline{w}$with a decrease in height. In contrast,
approach 2 makes no assumption in the shape of the vertical gradient of
$\overline{w}$. Estimates using approach 2 where \~ 30 % less than those
assuming a linear gradient in $\overline{w}$, i.e., approach 1.

Table 3. Preliminary results showing the advection effects on carbon
balance from the Renon site, Italy. Units are in g C m^-2^ d^-1^.

  ----------------- ----------------- ----------------- -----------------
  **Flux            **upslope**       **downslope**     **Total**
  components**                                          

  Eddy              -2.938            -2.938            **-2.938**

  Storage           -0.045            -0.045            **-0.045**

  H_adv             2.015             0.583             **1.299**

  V2_adv            1.197             1.523             **1.302**

  **Total**         **0.229**         **-0.877**        **-0.382**
  ----------------- ----------------- ----------------- -----------------

**[References Cited]{.underline}**

Aubinet, M., Grelle, A., Irbom, A., Rannik, U, Moncrieff, J., *et al.*
2000. Estimates of the annual net carbon and water exchange of forests:
the EUROFLUX methodology. *Adv. Ecol. Res*. **30**:113-175.

Brock, FV, Richardson, SJ 2001. Meteorologial measurement systems.
Oxford University Press, NY, pp. 290.

DeGaetano, A.T., 1997. A quality control routine for hourly wind
observations. *J. Atmos. Oceanic Tech..* **14**: 308-317.

Foken, Th., Oncley, SP., 1995. Results of the workshop 'instrumental and
methodical problems of land surfacef lux measurements.' *Bull Am
Meteorol Soc.* **76**: 1191-1193

Foken, Th., Wichura, B. 1996. Tools for quality assessment of
surface-based flux measurements. *Agric. For. Meteorol*. **78**:83-105.

Foken, Th., Mangold, A., Rebamnn, C., Wichura, B., 2000.
Characterisation of a complex measuring site for flux measurements.
14^th^ Symposium on Boundary Layer and Turbulence, Aspen, CO, August
7-11, 2000. Am. Meteorol. Soc., Boston, 388-389.

Gilgen, H., Whitlock, C.H., Koch, F., Müller, G., Ohmura, A., Steiger,
D., Wheeler, R., 1994. Technical plan for BSRN data management. World
Radiation Monitoring Centre, Technical Report. **1**: 55 pp.

Handorf, D. 1996. Zusammenstellung von Qualitätstests zur anwendung für
die profilmessungen an den LITASS-monitoringstationen und auf dem
gemeinsamen messfeld. MOL2-INFO Nr. 9, Deutscher Wetterdienst,
Lindenberg, pp. 29.

Højstrup, J., 1993. A statistical data screeing procedure.
*Meas.Sci.Tech.* **4**:153-157.

Kaimal, J.C., Finnigan, J.J., 1994. Atmospheric Boundary layer Flows:
their structure and measurement. Oxford University Press, NY, pp 289.

Kasten 1985. Instruction and observation methods, Report No. 23, WMO,
Genf, pp.65-84.

Mahrt, L. 1991 Eddy asymmetry in the sheared heated boundary layer. *J.
Atmos. Sci.* **48**:472-492.

Moncreif, J.B., Massheder, J.M., DeBruin, H., Elbers, J. et al. 1997. A
system to measure surface fluxes of momentum, sensible heat, water vapor
and carbon dioxide. *J. Hydrol.* **188-189**:589-611.

Rannik, U., Aubinet, M., Kurbanmuradov, O., Sabelfeld, K. K., Markkanen,
T., Vesala, T. 2000. Footprint analysis for measurements over a
heterogeneous forest. *Boundary-Layer Meteorology.* **97**:137-166.

Schmid, H.P., 1997. Experimental design for flux measurements: matching
scales of observations and fluxes. *Agric. For. Meteorol.* **87**:
179-200.

Schmid, H.P. 1994. Source areas for scalars and scalar fluxes.
*Boundary-Layer Meteorology*. **67**:293-318.

Shearman, R.J., 1992. Quality assurance in the observation area of the
Meteorological office. *Meteorol. Mag.* **121**:212-216.

Thomas, C., Foken, Th., 2002. Re-evaluation of integral turbulence
characteristics and their parameterizations. 15^th^ Conference on
Turbulence and Boundary Layers, Wageningen, NL, July 15-19, 2002, *Am.
Meteorol. Soc.* 212-216.

Vickers, D., Mahrt, L. 1997. Quality control and flux sampling problems
for tower and aircraft data. *J. Atmos. Oceanic Tech.* **14**:512-526.

Wichura, B., Buchmann, N., Foken, Th., 2002. Carbon dioxide exchange
characteristics above a spruce forest. 25^th^ Symposium on Agricultural
and Forest Meteorology, Norfolk, VA, May 20-24, 2002.