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Eddy Covariance Processing Software | Version 6
EddyPro® Software is a powerful application for processing eddy covariance data. It computes fluxes of water vapor (evapotranspiration), carbon dioxide, methane, other trace gases, and energy with the eddy covariance method.
EddyPro® in the Field
And at Your Desk
The SmartFlux™ 2 System brings EddyPro to your research site to provide fully processed flux results in real time.
EddyPro provides the full range of output and processing options for advanced data analysis.
EddyPro is an open source software application developed, maintained and supported by LI-COR Biosciences. It originates from ECO2S, the Eddy Covariance Community Software project, which was developed as part of the Infrastructure for Measurement of the European Carbon Cycle (IMECC-EU) research project. We gratefully acknowledge the IMECC consortium, the ECO2S development team, the University of Tuscia (Italy) and scientists around the world who assisted with development and testing of the original version of this software. Click here for more information about the ECO2S project.
Online help provides tutorials to help you learn EddyPro and to help you select the best settings for each project. It also provides a comprehensive explanation of data processing and a complete scientific reference.
EddyPro Forum Visit the EddyPro section on the LI-COR Environmental Forum
We invite you to join us for the conclusion of our 5-part series on the eddy covariance method. In this webinar we demonstrate EddyPro 3.0 eddy covariance data processing software, including:
• What's new in EddyPro 3.0
• Using advanced settings of EddyPro 3.0
• Where is EddyPro Express? How to use Express in EddyPro 3.0
• Abate execution time and speed up data processing
EddyPro 3.0 introduces advanced settings that give you the flexibility to choose how to process your eddy covariance data and select methods that are best for your site and research needs. Join us to learn how EddyPro 3.0 will simplify and standardize eddy covariance around a flexible, and easy-to-use software platform.
This is the third of five educational webinars on the eddy covariance method. Following up on the previous two webinars, we will demonstrate EddyPro Express eddy covariance data processing software. EddyPro Express will quickly take you from raw data files to publishable flux results in a just few steps. The demonstration will include:
• An end-to-end workflow for logging raw data to the LI-7550 and computing flux results with EddyPro Express
• An explanation of EddyPro Express flux computations and corrections
• Processing GHG, ASCII, and TOB1 files with EddyPro Express
• EddyPro results — statistical files and computed fluxes
In this webinar, we will describe how to configure the LI-7550 to log GHG files that include all the raw data and station information required to compute fluxes. Next, we will introduce EddyPro and discuss its origin, creation, and validation. We outline the steps involved in processing ASCII files, TOB1 files, and GHG files using EddyPro Express. Finally, we will describe how EddyPro Express processes data, and describe the output files that EddyPro Express provides.
Data Processing Options in EddyPro (Express Mode selections in italics)
Raw data filtering based on gas analyzer and sonic anemometer diagnostics
Axis rotation for sonic anemometer tilt correction
Sector-wise planar fit (Wilczak et al., 2001)
Sector-wise planar fit with no velocity bias (van Dijk et al., 2004)
Detrending of raw time series
Exponential running mean
Compensation of time lag between sonic anemometer and gas analyzer measurements
Automatic time lag optimization (optionally as a function of RH for H2O)
Maximum covariance with default (circular correlation)
Maximum covariance without default
None (option to not apply compensation)
Statistical tests for raw time series data (Vickers and Mahrt, 1997)
Spike count/removal (Mauder et al., 2013)
Skewness and kurtosis
Software correction for vertical windspeed measurements in certain Gill WM and WMP models following Gill Instruments (2015)
Angle of attack
Steadiness of horizontal wind
Individually selectable and customizable
Full (rich) output with fluxes, quality flags and much more (standard format or available results only)
GHG Europe format
Raw data statistics
Full length spectra and co-spectra
Binned spectra and co-spectra
Ensemble averaged spectra
Ensemble averaged cospectra, fitted models and ideal (Kaimal) cospectra
Details of steady state and turbulence tests
Raw data time series after each statistical tests/correction
Averaged biomet data
Compensation for air density fluctuations
Webb et al., 1980 (open path) / Ibrom et al., 2007a (closed path)
Use (or convert to) mixing ratio (Burba et al., 2012)
Optional off-season upatake correction for LI-7500 (Burba et al., 2008)
None (option to not apply compensation)
Correction for frequency response (attenuation)
Analytic high-pass filtering correction (Moncrieff et al., 2004)
Low-pass filtering, select and configure:
Moncrieff et al. (1997)
Ibrom et al. (2007b)
Horst and Lenschow (2009)
Fratini et. al. (2012)
Quality control tests for fluxes according to Foken et al. (2004)
Flagging according to Carbo Europe standard (Mauder and Foken, 2004)
Flagging according to Foken (2003)
Flagging after Göckede et al. (2004)
Random uncertainty estimation
Mann and Lenschow (1994)
Finkelstein and Sims (2001)
Flux footprint estimation
Kljun et al. (2004)
Kormann and Meixner (2001)
Hsieh et al. (2000)
Other options applied in both Express and/or Advanced Mode include:
Sonic temperature correction for humidity following van Dijk et al. (2004)
Spectroscopic correction for LI-7700 following McDermitt et al. (2011)
Angle of attack corrections for Gill anemometers following Nakai et al. (2006)
Angle of attack corrections for Gill anemometers following Nakai and Shimoyama (2012)
Inclusion of biomet data for improved flux computation/correction
Mauder et al. (2013), Agricultural and Forest Meteorology, 169: 122-135.
Burba et al. (2008) – Global Change Biology, 14:1854–1876.
Burba et al. (2012) – Global Change Biology, 18:385-399.
Finkelstein and Sims (2001) – Journal of Geophysical Research, 106:3,503 – 3,509.
Foken (2003) – Angewandte Meteorologie, Mikrometeorologische Methoden, 289 pp.
Foken et al. (2004) –Handbook of micrometeorology: A guide for surface flux measurements, 81-108.
Fratini et al. (2012) – Agricultural and Forest Meteorology, 165: 53-63
Gill Instruments (2016) – Gill Technical Key Note, KN1509v3.
Göckede et al. (2004) – Agricultural and Forest Meteorology, 127: 175-188.
Hsieh et al. (2000) – Advances in Water Resources, 23: 765-772.
Horst (1997) – Boundary Layer Meteorology, 82: 219-233.
Horst and Lenschow (2009) – Boundary Layer Meteorology, 130: 275-300.
Ibrom et al. (2007a) – Tellus Series B-Chemical and Physical Meteorology, 59:937-946.
Ibrom et al. (2007b) – Agricultural and Forest Meteorology, 147: 140-156.
Kljun et al. (2004) – Boundary Layer Meteorology, 112: 503-523.
Kormann and Meixner (2001) – Boundary Layer Meteoroogy, 99:207–224.
Mann and Lenschow (1994) – Journal of Geophysical Research, 99:14,519–14,526.
Mauder and Foken (2004) - Documentation and Instruction Manual of the Eddy Covariance Software Package TK2, Arbeitsergebnisse Nr. 26.
Massman (2000) - Agricultural and Forest Meteorology. A104: 185–198.
McDermitt et al. (2011) – Applied Physics B: Laser and Optics, 102: 391-405.
Moncrieff et al. (2004) – Handbook of micrometeorology, 7-31.
Moncrieff et al. (1997) – Journal of Hydrology, 188-189: 589-611.
Nakai et al. (2006) – Agricultural and Forest Meteorology, 136: 19-30.
Nakai and Shimoyama (2012) – Agricultural and Forest Meteorology, 162-163: 14-26
Van Dijk et al. (2004) – Meteorology and Air Quality Group, Wageningen, the Netherlands, 99 pp.
Vickers and Mahrt (1997) – Journal of Atmospheric and Oceanic Technology, 14: 512-526.
Webb et al. (1980) – Quarterly Journal of the Royal Meteorological Society, 106: 85-100.
Wilczak et al. (2001) - Boundary Layer Meteorology, 99: 127-150.