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@MISC{MarconHenge:1053077,
author = {Marcon-Henge, Lediane and Graf, Alexander and Schmidt,
Marius and Kummer, Sirgit and Peichl, Matthias and Larmanou,
Eric and Boschetti, Fabio},
title = {{C}lassic {VTT} estimates for {F}ea{V}i{T}a 2025},
publisher = {Zenodo},
reportid = {FZJ-2026-01417},
year = {2025},
abstract = {CO2 mixing ratios estimates using classic Virtual Tall
Tower (VTT) approach, diagnostic data (fluxes and turbulence
parameters) 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). Here, the VTT approach described
by Haszpra et al. (2015) (referred to as the classic
approach) is applied to estimate CO₂ concentrations in the
mixing layer from EC measurements. 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. For the VTT classic
approach, the following parameters are required as input:
time series of EC fluxes, planetary boundary layer height,
air pressure, air temperature, relative humidity, and
sensible heat flux. Optional input data include tropospheric
CO₂ concentrations and CO₂ concentrations at the top of
the mixing layer. Tropospheric CO₂ concentrations can, for
example, be obtained from the NOAA Global Monitoring
Laboratory (e.g., Mauna Loa Observatory data:
https://gml.noaa.gov/ccgg/trends/data.html). Planetary
boundary layer height can be obtained from the ERA5 dataset
(available at
https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download).
Three VTT formulations were implemented and are labeled as
follows: 1. $_HaszpraModified:$ Based on the
formulation proposed by Haszpra et al. (2015, Eq. 4), with a
modification in the calculation of the top-down diffusion
flux due to entrainment . The top-down diffusion flux is
approximated as a fraction of the surface bottom-up flux
(0.2 x bottom-up flux), as proposed by Lilly (1968) and
Chemel and Staquet (2007). 2. $_Patton:$ Similar
to $_HaszpraModified,$ but using the bottom-up and top-down
gradient functions proposed by Patton et al. (2003).
3. $_Wang:$ Similar to $_HaszpraModified,$ but
using the bottom-up and top-down gradient functions proposed
by Wang et al. (2007). CSV files: o
$VTTclassic_Associate.csv:$ Time series of main variables
and calculated VTT for Associate EC station. o
$VTTclassic_Svartberget.csv:$ Time series of main variables
and calculated VTT for Svartberget EC station. o
$variable_names.xlsx: List$ of variable names, units,
descriptions, and data sources used in the VTTclassic 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 $FeaViTa_99a_getERA5data.py:$ Downloads ERA5
(Copernicus) hourly data for planetary boundary layer
height, used in VTT calculations. o
$FeaViTa_99b_MergeExportERA5data.py:$ Prepares downloaded
ERA5-Copernicus time series. Outputs CSV files of time
series for each location. o
$FeaViTa_1b3c_ReadTallTowerData.py:$ Imports tall tower data
after download from the ICOS Portal. Outputs a TT CSV file
containing CO₂ concentration time series. o
$FeaViTa_1c_ReadFluxesEC.py:$ Imports EC data (fluxes +
meteorological data) after download from the ICOS Portal.
Outputs an EC CSV file with the input time series required
for VTT calculations. o
$FeaViTa_1e_ReadFluxesAssociateEC:$ Imports processed EC
data from the Associate station (own EC station). Outputs an
EC CSV file with the input time series required for VTT
calculations. o $FeaViTa_1d3_ReadCalAnalyzer:$ Imports
calibration data for the EC station. Outputs CO₂
concentration time series used to calibrate EC measurements.
o $FeaViTa_3a_VTTclassicapproach.py:$ Calculates the VTT
using the formulations listed above, without applying
calibration to EC measurements. Outputs CSV files with time
series of main variables and calculated VTT from raw EC
data. o $FeaViTa_4_compareVTTtoTT:$ 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. Additional files Zip folder containing
the files listed bellow: o $FeaViTa_0a_maxcorrshift.py:$
Python function to check and identify potential time shifts
between time series. Can be used, if needed, with
$FeaViTa_4_compareVTTtoTT.$ o
$FeaViTa_1c3a_ReadTroposphericCO2.py:$ Imports background
hourly CO₂ concentrations after download from NOAA Global
Monitoring Laboratory (Mauna Loa Observatory). These
concentrations are used in the original formulation of
Haszpra et al. (2015). o $CA_RuS_30min.csv:$ Time series
of CO₂ concentrations for the calibration of the Selhausen
Class-I and Associate EC stations. References: Chemel,
C., $\&$ Staquet, C. (2007). A formulation of convective
entrainment in terms of mixing efficiency. Journal of Fluid
Mechanics, 580, 169-178. Haszpra, L., Barcza, Z., Haszpra,
T., Pátkai, Z., $\&$ Davis, K. J. (2015). How well do
tall-tower measurements characterize the CO 2 mole fraction
distribution in the planetary boundary layer?. Atmospheric
Measurement Techniques, 8(4), 1657-1671. Lilly, D. K.
(1968). Models of cloud‐topped mixed layers under a strong
inversion. Quarterly Journal of the Royal Meteorological
Society, 94(401), 292-309. Patton, E. G., Sullivan, P. P.,
$\&$ Davis, K. J. (2003). The influence of a forest canopy
on top‐down and bottom‐up diffusion in the planetary
boundary layer. Quarterly Journal of the Royal
Meteorological Society: A journal of the atmospheric
sciences, applied meteorology and physical
oceanography, 129(590), 1415-1434. Wang, W., Davis, K. J.,
Yi, C., Patton, E. G., Butler, M. P., Ricciuto, D. M., $\&$
Bakwin, P. S. (2007). A note on the top-down and bottom-up
gradient functions over a forested site. Boundary-layer
meteorology, 124(2), 305-314.},
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.16899570},
url = {https://juser.fz-juelich.de/record/1053077},
}
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