DOI: 10.5281/zenodo.16899569
title: Classic VTT estimates for FeaViTa 2025

description:  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.