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@MISC{Graf:1053071,
author = {Graf, Alexander and Marcon, Lediane and Schmidt, Marius and
Kummer, Sirgit and Peichl, Matthias and Larmanou, Eric and
Boschetti, Fabio},
title = {{H}igh-frequency based {VTT} estimates for {F}ea{V}i{T}a
2025},
publisher = {Zenodo},
reportid = {FZJ-2026-01411},
year = {2025},
abstract = {Title: High-frequency VTT estimates for FeaViTa 2025
Authors: Graf, Alexander (Project manager), Marcon-Henge,
Lediane (Project member), Schmidt, Marius (Data collector),
Kummer, Sirgit (Data collector), Peichl, Matthias (Project
partner), Larmanou, Eric (Data collector), Boschetti, Fabio
(Data collector) CO2 mixing ratios estimates using the
first implementation of the suggested new Virtual Tall Tower
(VTT) approach, including diagnostic data (??) for the ITMS
Module B2 project FeaViTa. Greenhouse gas measurements, such
as CO₂, from existing eddy-covariance (EC) flux stations
(typically 2–50 m a.g.l.) can be used to estimate gas
concentrations at tall tower (TT) heights (approximately 100
m a.g.l. and higher). In this framework, an EC station
effectively becomes a virtual tall tower (VTT). This dataset
contains the 3rd data deliverable of the ITMS
(https://www.itms-germany.de/) project FeaViTa, following up
the deliverables: FeaViTa 2024 measurements (DOI
10.5281/zenodo.14561379) Classic VTT estimates for FeaViTa
2025 (DOI 10.5281/zenodo.16899569) A minor inconsistency
towards the latter dataset is that through an update of the
external ICOS L2 product for the Svartberget tower, CO2
reference data at 35 m and 150 m published with this dataset
have slightly changed (for the period of interest for our
project by less than 0.03 ppm). We decided to include the
newer Svartberget L2 product version in this dataset, since
it is more likely to be the long-term future reference for
this station. Error statistics of the classic VTT approach
changed for less than 0.015 ppm (bias), 0.06 ppm (RMSE) and
0.003 (R2) when comparing the new vs. old reference data
version. Two EC–TT pairs were used for the VTT
calculations. One pair is located in Jülich, Germany (ICOS
TT site JUE + ICOS-associated ecosystem EC site DE-RuS; the
ICOSclass 1 site RuS is not yet included because of the
delay in official final flux data production). The second
pair is located in Svartberget, Sweden (ICOS TT site SVB +
ICOS ecosystem EC site SE-Svb), where both measurements are
taken at the same location. EC (ecosystem) and TT
(atmospheric) station data are available for download from
the ICOS portal (https://data.icos-cp.eu/portal/) or from
our own dataset (https://zenodo.org/records/14561380). VTT
calculations were performed using all 2024 data for the
Svartberget station, and periods of calibrated measurements
in 2024– July 2025 for the Jülich Associate station. The
high-frequency approach requires raw (in our case, 20 s-1)
fast-response EC station data of CO2 concentration (as dry
mixing ratio or together with the thermodynamic state
variables needed to convert it), temperature, and optionally
vertical wind and humidity. In its most basic form, building
on the rationale explored in Graf et al. (2010), the instant
minimum fast-response (sonic or corrected) temperature
within an averaging interval (e.g. 30 minutes or 1 hour) is
used to identify the most likely occurrence of air from the
well-mixed part of the (convective) planetary boundary layer
(BL) being seen by the EC instruments. In alternative
versions, maximum temperature can be used to generate
tentative additional estimates for stable (downward sensible
heat flux) periods; the minimum or maximum can be replaced
by quantiles in an attempt to increase statistical
robustness; and quantiles or averages of vertical wind,
humidity and CO2 (the latter two depending again on flux
direction) can be used to pre-filter for expected BL air.
The (instantaneous, average or median) CO2 concentration of
these instants is then the VTT (BL concentration) estimate
on the calibration scale of the EC gas analyzer. Since
typical EC gas analyzers do not have the long-term stability
needed to make CO2 concentrations traceable to the standards
of atmospheric concentration measurement networks, an
additional (typically slow-response and ideally frequently
auto-calibrated) CO2 measurement at the EC location is
needed. In case of Svartberget, this reference is provided
by the atmospheric network measurement at the 35 m EC level
and available full-year, in case of Jülich dedicated
campaign measurements with an Li810 or Li8100 analyzer were
performed (all reference requirements and steps also apply
to the classic VTT dataset). Four High-frequency VTT
formulations were implemented in this dataset, all after
exclusion of outliers and spikes and correction for time
lags in the raw data and all on one-hour periods matching
the averaging interval of the tall tower data: 1.
“W50T0”: After filtering for vertical wind values lower
than or equal to their hourly median (approximately focusing
on downdrafts), identifying the minimum (positive covariance
of temperature and vertical wind) or maximum (negative)
sonic-derived air temperature and applying the difference
between this instant’s dry air CO2 mixing ratio and the
period average (both from the EC) to the reference CO2
measurement at EC level. Results are almost identical to
those of focusing on negative (centered) vertical wind
speeds; the percentile-/median-based approach is preferred
here because the below alternative formulations can be
implemented by simply changing parameters (percentile
limits) of the same algorithm. 2. “T50W50”: As above
but using all values lower than the median of both
temperature and vertical wind speed, and then the median CO2
mixing ratio of all remaining records as the hourly VTT
estimate. For negative heat flux (e.g. typical nighttime)
situations, the upper instead of the lower half of
temperatures is used. 3. “W50T50C50H50”: The same
approach is applied to all variables potentially informing
about the vertical origin of the air, i.e. also CO2 mixing
ratio itself and that of H2O, always taking the lower half
of values for an upwards net flux of the respective
quantity, and the upper one for a downward flux. 4.
“W75C75H75T5”: Similar to the above, but the criterion
is relaxed $(75\%)$ for all other variables and restricted
$(5\%$ of the data) for temperature. The percentiles are
always computed from the whole original dataset if possible,
but if after filtering for one quantity the desired
percentile of the next one is not contained in the remaining
data anymore, it is newly computed from them. The filtering
is performed starting with the most relaxed toward the most
restricted criterion, and if percentiles are identical for
several quantities, their order in the short code of the
formulation is the filtering order. CSV files: o
$VTThifreq_Juelich_Associate.csv$ and o
$VTThifreq_Svartberget.csv:$ Hourly time series of CO2
averages (EC, calibration reference and TT), diagnostic data
and VTT estimates o $variable_names.csv:$ List of
variable names, units, descriptions, and data sources used
in the files. Python codes Below is a short description
of the provided Python codes. Scripts should be run in
numerical order. More detailed explanations are included as
comments within the code. o
$FeaViTa_1a_ReadHighResData.py:$ Read in EC raw data from
their original format (half-hourly files in ICOS standard
format for class-1 sites, daily custom format files for
Jülich associate sites) o
$FeaViTa_2_ConvertHighResData.py:$ Quality filter, determine
and shift time lag, Convert units, compute dependent
variables and diagnostic files to store the number of
outliers, crosscorrelations and shifts, while still
remaining in the 20 s-1 domain. o
$FeaViTa_3b_VTTHhiFreqPercentileVersion.py:$ The actual VTT
estimation described above, turning 20 s-1 data into hourly
timeseries o $FeaViTa_4_compareVTTtoTT.py:$ Calculates
the offset between EC and calibration measurements, applies
the offset to the calculated VTT to obtain final CO₂
concentrations at tall tower height, and computes comparison
statistics. Outputs a CSV file containing the VTT-corrected
CO₂ time series, and optionally, another CSV file with
main statistics (bias, root mean square error, and Pearson
R²) comparing estimated and measured CO₂ concentrations
can be exported. This script was already part of the classic
VTT data upload but is here stored in an updated version.
Also needed but not included, since identical to the classic
VTT upload version, are the scripts
$FeaViTa_1b3c_ReadTallTowerData.py,$
$FeaViTa_1d3_ReadCalAnalyzer.py,$ and
$FeaViTa_0a_maxcorrshift.$ Additional files o
$Svartberget_EC_HourlyHiFreqFiles_qc_2004a.zip,$ o
$Svartberget_EC_HourlyHiFreqFiles_qc_2004b.zip$ and o
$Juelich_AssociateEC_HourlyHiFreqFiles_qc.zip:$ 20 s-1 raw
data after quality filtering and lag correction, one
(in-zip) csv file per station and hour, named by the center
of the hour in UTC References: Graf, A. et al., 2010.
Boundedness of Turbulent Temperature Probability
Distributions, and their Relation to the Vertical Profile in
the Convective Boundary Layer. Bound.-Layer Meteor., 134(3):
459-486. https://doi.org/10.1007/s10546-009-9444-9},
cin = {IBG-3},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {2173 - Agro-biogeosystems: controls, feedbacks and impact
(POF4-217)},
pid = {G:(DE-HGF)POF4-2173},
typ = {PUB:(DE-HGF)32},
doi = {10.5281/ZENODO.17209765},
url = {https://juser.fz-juelich.de/record/1053071},
}
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