%0 Chart or Table
%A Graf, Alexander
%A Marcon, Lediane
%A Schmidt, Marius
%A Kummer, Sirgit
%A Peichl, Matthias
%A Larmanou, Eric
%A Boschetti, Fabio
%T High-frequency based VTT estimates for FeaViTa 2025
%I Zenodo
%M FZJ-2026-01411
%D 2025
%X 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
%F PUB:(DE-HGF)32
%9 Dataset
%R 10.5281/ZENODO.17209765
%U https://juser.fz-juelich.de/record/1053071