Methods
Here, methods are only briefly described:
Gapfilling
The gapfilling of the eddy
covariance and meteorological data will be performed through methods
that are similar to Falge et al. (2001), but that consider both
the covariation of fluxes with meteorological variables and the
temporal autocorrelation of the fluxes (Reichstein et al. 2005): In this algorithm, three different conditions are identified:
1)
Only the data of direct interest are missing, but all meteorological
data are available,
2) Also air temperature or VPD is missing, but
radiation is available,
3) Also radiation data is missing.
In case 1),
the missing value is replaced by the average value under similar
meteorological conditions within a timewindow of 7 days. Similar
meteorological conditions are present when Rg, Tair and VPD do not
deviate by more than 50
W m^{2}, 2.5°C, and 5.0 hPa respectively. If no similar
meteorological conditions are present within the time window, the
averaging window is increased by 14 days.
In case 2) the same approach
is taken, but similar meteorological conditions can only be defined via
Rg deviation less than 50 W m^{2} and window size is not
increased.
In case 3) the missing value is replaced by the average
value at the same time of the day ( 1 hour), i.e. by the mean diurnal course. In this case
the window size starts with 0.5 days (i.e. similar to a linear interpolation from
available data at adjacent hours).
If after these steps the values
could not be filled, the procedure is repeated with increased window
sizes until the value can be filled. Both, the method, the window size,
and
the number and the standard deviation of values averaged is recorded
then,
so that for individual purposes appropriate data can be selected and
e.g.
uncertainties can estimated. Uncertainties are also calculated for actual measurements
by simulating a gap and applying the gapfilling procedure and are found in the column fs_unc
of the output file.
For convenience, the filled data is
further classified
into three categories (A, B, C) based on the method (1, 2, or 3) and
the
window size used (Table 1). The classification is based on the notion,
that
the estimation of the missing data improves with the knowledge on
meteorological
conditions and with the use of the temporal autocorrelation of the
variable
that favours smaller timewindows.
Table 1: Quality classification scheme for gapfilled values, according to method used and averaging time window. A: best; B: acceptable; C: dubious. The output file contains a variable fqc, with values 0: no gapfilled; 13: gapfilled category AC. 

Averagingtime window [days] 
Method 


1 
2 
3 
0.5 
n.a. 
n.a. 
A 
1.52.5 
n.a. 
n.a. 
B 
> 2.5 
n.a. 
n.a. 
C 
7 
A 
A 
n.a 
14 
A 
B 
n.a. 
> 28 
B 
C 
n.a. 
> 56 
C 
C 
n.a. 
Fluxpartitioning
Only original data (not gapfilled) are used for the fluxpartitioning, and all original data flagged with a quality indicator >1 (e.g., with nonturbulent conditions) are dismissed. Nighttime data was selected according to a global radiation threshold of 20 W m^{2} (night below that threshold), crosschecked against sunrise and sunset data derived from the local time and standard sungeometrical routines, and defined as ecosystem respiration (R_{eco}). Then the data set is split into consecutive periods of length x (days), and for each period it is checked where there are more than six data points available and whether the temperature range is more than 5 °C, since only under these conditions reasonable regressions of Reco versus temperature can be expected (x is a parameter of the algorithm and currently set to 10 days). For each of those periods where the criteria are met, the LloydandTaylor (1994) regression model

(Eq. 1) 
is fitted to the scatter of ecosystem respiration (R_{eco}) versus either soil or air temperature (T). While the regression parameter T_{0} is kept constant at 46.02°C as in Lloyd and Taylor (1994), the activationenergy kind of parameter (E_{0}), that essentially determines the temperature sensitivity was allowed to vary. The reference temperature (T_{ref}) was set to 10°C as in the original model.
For each period, the regression parameters and
statistics are kept in memory and evaluated after regressions for all
periods that have been performed. Only those periods where the standard
error of the estimates of the parameter E_{0} is less than 50%
and where estimates are within realistic bounds are accepted. The three
estimates of E_{0} with the
smallest standard error are then assumed to best represent the
shortterm temperature response of R_{eco} and are averaged
resulting in an E_{0,avg}
value for the data set. Subsequently, the respiration at reference
temperature
(R_{eco,ref}) is estimated from the night time data for
consecutive
intervals of y days using nonlinear regression of the R_{eco}
data versus temperature according to Eq. 1, where E_{0} is
fixed
to the E_{0,avg} value (y is a parameter of the
algorithm
and currently set to 4). The estimated value R_{eco,ref} is
then
assigned to the central timepoint of the period and linearly
interpolated between periods. Thus, for each half hour the parameters E_{0}
and R_{eco,ref} are available and are used to estimate R_{eco}
as a function of that one temperature (soil or air) that has been also
used to derive the parameters.
u*filtering
For the u*filtering the
data set (storage corrected flux is assumed) is split into 6
temperature classes of the same sample size (according
to quantiles) and for each temperature class the set is split into 20
u*classes. The threshold is defined as the u*class where the nighttime flux reaches more than 95% of the average
flux at the higher u*classes. The threshold is only accepted if for
the temperature class if temperature and u* are not or only weakly
correlated (r < 0.3). The final threshold is defined as the median
of the thresholds of the (upto) six temperature classed. This
procedure is applied to the subsets of four threemonths periods to
account for seasonal variation of vegetation structure. For each period
the u*threshold is reported, but the whole data set is filtered
according to the highest threshold found (conservative appraoch). In
cases where no u*threshold could be found it is set to 0.4 . A minimum
threshold is set to 0.1. Each halfhourly value of NEE with the
corresponding u*below the threshold and each succeeding NEE value are
removed.
Use of this service is quite simple.
 Go to the data input form: Here you provide the file containing the data (as ASCII file) and some basic information that are necessary to read and process the data. As you imagine with automatic processing, it is absolutely important that the data format and the information you provide is exact and follows our minimum template
 There are several options to choose from, e.g., uncertainties, u*filtering and different methods for the fluxpartitioning.
After you press the SUBMIT button the file is uploaded, the gapfilling, and, if selected, the fluxpartitioning are performed. After a certain time depending on size of the data set, number of gaps, and server load and performance (215 min, maybe enough for getting a coffee) you are directed to the results page. Meanwhile, the log file of the processing is forwarded to your browser output and it refreshes automatically once the processing is finished.
 If you follow the link (view whole output directory), you will find additional graphs and a zipped version of all the output files.