Flux Data Post-Processing and QA/QC

The following software and packages are generally designed for post-processing flux and meteorological data (i.e., half-hourly), generating value-added data products (e.g., gap-filled, partitioned gross primary production/ecosystem respiration, footprint), and facilitating further QA/QC.

MDI Meteo gap-filling tool

  • http://www.bgc-jena.mpg.de/~MDIwork/meteo/index.php
  • This meteorological gap-filling tool takes advantage of the re-analyzed meteorological fields provided by ECMWF. The local site conditions are related to the gridded spatial data from their ERA-Interim product. The mapping from the grid to point is performed by multiple linear regression with the nearest nine pixels.


  • http://tinyurl.com/GaFir-web
  • A user-friendly open-source package GaFiR was developed to fill data gaps in turbulent flux observations of evapotranspiration and carbon dioxide.




  • https://github.com/lsigut/openeddy
  • R package for eddy covariance data handling, quality checking (similar to Mauder et al., 2013), processing, summarizing, and plotting. It aims to standardize automated quality checking and make data processing reproducible.
  • The vignette for the package is in development, but you can already try it out on an example dataset using the workflow files (1. QC, 2. Storage, 3. GF & FP, 4. Summary) (https://github.com/lsigut/EC_workflow)
  • Although most functions have general applications, the processing is tuned to EddyPro output files. With REddyProc, openeddy forms a complete processing chain that produces annual sums of fluxes with further variables, statistics, and visualization.


  • https://github.com/OzFlux/PyFluxPro
  • Developed in Python, but the package can be used via a simple GUI and the editing of text files.
  • PyFluxPro includes the functions of QAQC, corrections (e.g., calibration drifts), gap filling of meteorological and flux data, u* threshold detection (Barr’s method), GPP/RECO partitioning (daytime/nighttime approaches).
  • Met data gap-filling can be done using data from a paired flux tower, from automated weather stations (AWS) operated as part of the global observing network, from the ERA-Interim reanalysis data set and from high spatial and temporal numerical weather prediction (NWP) models where available. Flux data gap-filling (H, LE, and FC) is done using a robust neural network (Hsu et al., 2002).
  • Its predecessor (OzFluxQC) is described in a recent paper in the OzFlux Special Issue of Biogeosciences (https://www.biogeosciences.net/14/2903/2017/).


  • https://github.com/mcuntz/hesseflux
  • Python module for processing eddy covariance data, used at the ICOS ecosystem site FR-Hes.
  • The post-processing functionality for eddy flux data is similar to the R-package REddyProc and includes basically the steps described in Papale et al. (Biogeosciences 2006) plus some extensions such as the daytime method of flux partitioning (Lasslop et al., Global Change Biology 2010) and flux uncertainty estimates (Lasslop et al., Biogeosciences 2008).
  • Published under the MIT license, can be installed via pip: pip install hesseflux
  • Cited via Zenodo: https://doi.org/10.5281/zenodo.3831488
  • Documentation is available from the Read The Docs: https://hesseflux.readthedocs.io/en/latest/


  • https://github.com/AmeriFlux/ONEFlux
  • It was developed in Python version 2.7 (it should work with Python version 3.5 or later, but was not fully tested with these versions).
  • ONEFlux (Open Network-Enabled Flux processing pipeline) consolidates multiple computations to process half-hourly (or hourly) flux inputs in an automatic fashion, including friction velocity threshold estimation methods and filtering, gap-filling of micrometeorological and flux variables, partitioning of CO2 fluxes into ecosystem respiration and gross primary production, uncertainty estimates, and more.
  • The current version of the code is compatible with the code base used to create the FLUXNET2015 dataset. The steps implemented are detailed on the data processing description page [https://fluxnet.org//data/fluxnet2015-dataset/data-processing/].


  • https://www.geosci-model-dev.net/10/3379/2017/gmd-10-3379-2017.html
  • R package for post-processing FLUXNET2015 and La Thuile data for use in land surface modeling
  • It transforms the data into community standard NetCDF files that are directly readable by LSMs
  • It also provides multiple methods for gap-filling and quality controlling the data


  • http://www.sciencedirect.com/science/article/pii/S1364815211002064
  • R package developed to analyze air pollution measurement data but is also of more general use in the atmospheric sciences (e.g., creation of wind rose).
  • The package consists of many tools for importing and manipulating data and undertaking a wide range of analyses to enhance understanding of air pollution data.

Kljun footprint model

The list was initially compiled by Ladislav Šigut and Housen Chu and has been updated with inputs from the communities. We thank all the original software/package developers for sharing and maintaining such great services. For details of each software/package, please refer to the developing team through the external links. Please contact the website manager (ameriflux-web@lbl.gov) if you would like to propose any addition or update to the list.