**Eddy Correlation Flux Measurement\
System Handbook**

D.R. Cook

M.S. Pekour

January 2008

Work supported by the U.S. Department of Energy,

Office of Science, Office of Biological and Environmental Research

**DISCLAIMER**

This report was prepared as an account of work sponsored by the U.S.
Government. Neither the United States nor any agency thereof, nor any of
their employees, makes any warranty, express or implied, or assumes any
legal liability or responsibility for the accuracy, completeness, or
usefulness of any information, apparatus, product, or process disclosed,
or represents that its use would not infringe privately owned rights.
Reference herein to any specific commercial product, process, or service
by trade name, trademark, manufacturer, or otherwise, does not
necessarily constitute or imply its endorsement, recommendation, or
favoring by the U.S. Government or any agency thereof. The views and
opinions of authors expressed herein do not necessarily state or reflect
those of the U.S. Government or any agency thereof.

Contents

[1. General Overview 1](#general-overview)

[2. Contacts 1](#contacts)

[2.1 Mentor 1](#mentor)

[2.2 Instrument Developer 1](#instrument-developer)

[3. Deployment Locations and History
1](#deployment-locations-and-history)

[4. Near-Real-Time Data Plots 2](#near-real-time-data-plots)

[5. Data Description and Examples 2](#data-description-and-examples)

[5.1 Data File Contents 2](#data-file-contents)

[5.1.1 Primary Variables and Expected Uncertainty
2](#primary-variables-and-expected-uncertainty)

[5.1.2 Secondary/Underlying Variables 3](#secondaryunderlying-variables)

[5.1.3 Diagnostic Variables 3](#diagnostic-variables)

[5.1.4 Data Quality Flags 4](#data-quality-flags)

[5.1.5 Dimension Variables 4](#dimension-variables)

[5.2 Annotated Examples 4](#annotated-examples)

[5.3 User Notes and Known Problems 4](#user-notes-and-known-problems)

[5.4 Frequently Asked Questions 5](#frequently-asked-questions)

[6. Data Quality 5](#data-quality)

[6.1 Data Quality Health and Status 5](#data-quality-health-and-status)

[6.2 Data Reviews by Instrument Mentor
6](#data-reviews-by-instrument-mentor)

[6.3 Data Assessments by Site Scientist/Data Quality Office
6](#data-assessments-by-site-scientistdata-quality-office)

[6.4 VAPs and Quality Measurement Experiments
10](#vaps-and-quality-measurement-experiments)

[7. Instrument Details 10](#instrument-details)

[7.1 Detailed Description 10](#detailed-description)

[7.1.1 List of Components 10](#list-of-components)

[7.1.2 System Configuration and Measurement Methods
11](#system-configuration-and-measurement-methods)

[7.1.3 Specifications 11](#specifications)

[7.2 Theory of Operation 11](#theory-of-operation)

[7.3 Calibration 13](#calibration)

[7.3.1 Theory 13](#theory)

[7.3.2 Procedures 13](#procedures)

[7.3.3 History 13](#history)

[7.4 Operation and Maintenance 13](#operation-and-maintenance)

[7.4.1 User Manual 13](#user-manual)

[7.4.2 Routine and Corrective Maintenance Documentation
13](#routine-and-corrective-maintenance-documentation)

[7.4.3 Software Documentation 13](#software-documentation)

[7.4.4 Additional Documentation 13](#additional-documentation)

[7.5 Glossary 13](#__RefHeading___Toc188164673)

[7.6 Acronyms 14](#acronyms)

[7.7 Citable References 14](#citable-references)

Tables

[Table 1: Deployment Information of the ECOR Flux Measurement System
2](#__RefHeading___Toc188164676)

[Table 2: Calculated Quantities for Each 30-min Interval
3](#__RefHeading___Toc188164677)

# General Overview

The eddy correlation (ECOR) flux measurement system provides in situ,
half-hour measurements of the surface turbulent fluxes of momentum,
sensible heat, latent heat, and carbon dioxide (CO~2~). The fluxes are
obtained with the eddy covariance technique, which involves correlation
of the vertical wind component with the horizontal wind component, the
air temperature, the water vapor density, and the CO~2~ concentration.
The instruments used are

-   a fast-response, three-dimensional (3D) wind sensor (sonic
    anemometer) to obtain the orthogonal wind components and the speed
    of sound (SOS) (used to derive the air temperature) and

-   an open-path infrared gas analyzer (IRGA) to obtain the water vapor
    density and the CO~2~ concentration.

The ECOR systems are deployed at the locations where other methods for
surface flux measurements (e.g., energy balance Bowen ratio \[EBBR\]
systems) are difficult to employ, primarily at the north edge of a field
of crops.

# Contacts

## Mentor

David R. Cook

Environmental Science Division

Argonne National Laboratory, Bldg. 203

Argonne, Illinois 60439-4843

Phone: 630.252.5840

<drcook@anl.gov>

## Instrument Developer

**Sensor Vendors**

3D sonic anemometer, model WindMaster Pro: Gill Instruments, Limited,
[http://www.gill.co.uk](http://www.gill.co.uk/) (U.S. distributor: PP
Systems, 978.834.0505, <support@ppsystems.com>)

IRGA, model LI-7500: LI-COR, Inc., <http://www.licor.com/env> (LI-COR
Environmental, 402.467.3576, 800.447.3576)

# Deployment Locations and History 

Table 1 shows the Southern Great Plains (SGP) site locations of the ECOR
flux measurement system.

[]{#__RefHeading___Toc188164676 .anchor}Table 1: Deployment Information
of the ECOR Flux Measurement System

  ----------------- ----------------- ----------------- -----------------
  **Facility**      **Location**      **Date            **Status**
                                      installed**       

  SGP/EF1           Larned, KS        March 9, 2004     Operational

  SGP/EF3           LeRoy, KS         March 7, 2004     Operational

  SGP/EF5           Halstead, KS      Sept. 9, 2003     Operational

  SGP/EF6           Towanda, KS       Sept. 15, 2003    Operational

  SGP/EF10          Tyro, KS          Oct. 3, 2003      Operational

  SGP/EF14          Lamont, OK        June 3, 2003      Operational

  SGP/EF16          Vici, OK          Sept. 25, 2003    Operational

  SGP/EF21          Okmulgee, OK      Feb. 11, 2004     Operational

  SGP/EF24          Cyril, OK         March 18, 2004    Operational

  AMF               Various           Feb. 1, 2005      Operational
  ----------------- ----------------- ----------------- -----------------

# Near-Real-Time Data Plots

Near-real-time data plots can be accessed via the Atmospheric Radiation
Measurement (ARM) Program Data Quality Health and Status (DQ HandS) Plot
Browser (<http://dq.arm.gov/plotbrowser/>); choose Southern Great Plains
("SGP") as "Search Site" and "sgp30ecor" as "Datastream" in the
corresponding scroll boxes.

# Data Description and Examples

## Data File Contents

Currently, the ECOR systems produce 00- and b1-level data files. Initial
processing takes place on the ECOR computer at the end of each half-hour
measuring period. The data ingest process adds quality control (QC)
flags and generates daily b1-level netCDF files, in keeping with the
Atmospheric Radiation Measurement (ARM) Program policy. See a complete
ECOR netCDF file header description at
<http://science.arm.gov/tool/dod/showdod.php?Inst=ecor>.

Both raw data (00 level) and b1 datastreams are routinely shipped to the
ARM Archive. Although b1 data are available though the standard Archive
interface, the raw data (00 level) are available only by request to the
Archive (<armarchive@ornl.gov>).

### Primary Variables and Expected Uncertainty

The ECOR system makes direct measurements at a rate of 10 Hz of the
following parameters:

-   Three wind components: *u, v,* and *w* \[m s^-1^\]

-   SOS, s \[m s^-1^\], which is used to derive atmospheric temperature,
    ta \[°K\]

-   Water vapor density, q \[mmol m^-3^\]

-   CO~2~ concentration, c \[mmol m^-3^\]

-   Atmospheric pressure, pa \[kPa\]

[]{#__RefHeading___Toc188164677 .anchor}Table 2: Calculated Quantities
for Each 30-min Interval

  -------------------------------- ------------------------- -------------
  **Variable**                     **Name in netCDF file**   **Unit**

  Sensible heat flux, *H*          H                         Wm^-2^

  Latent heat flux, *L~v~E*        lv_e                      Wm^-2^

  Momentum flux (dynamic), *M*     K                         kg m^-1^s^-2^

  Friction velocity, *u\**         Ustar                     m s^-1^

  CO~2~ flux, *F~CO2~*             Fc                        µmol m^-2^
                                                             s^-1^

  Mean wind speed (vector          wind_spd                  m s^-1^
  averaged), *V*                                             

  Mean wind direction, *D*         wind_dir                  deg

  Mean atmospheric temperature,    mean_t                    °K
  *T~a~*                                                     

  Mean water vapor density, *Q*    mean_q                    mmol m^-3^

  Mean CO~2~ concentration, *C*    mean_c                    mmol m^-3^

  Mean atmospheric pressure,       atm_pres                  kPa
  *P~a~*                                                     
  -------------------------------- ------------------------- -------------

Expected uncertainties of the fluxes, due to measurement accuracies of
primary variables, are within the following limits: *δH* = 6%, *δL~v~E*
= 5%, *δM* = 5%, *δF~CO2~* = 4%.

#### Definition of Uncertainty

This section is not applicable to this instrument.

### Secondary/Underlying Variables

The b1-level data file contains all statistics that were used for
estimating fluxes and a number of additional variables to support a
variety of advanced QC procedures.

Calculated statistics include mean, variance, covariance, skewness, and
kurtosis of each of the primary measured values: u, v, w, Ta, q, and C.
A two-axis coordinates rotation procedure is applied to find vertical
turbulent fluxes; all relevant rotated statistics (rotation angles,
means, variances, and covariances) are included in the data file. The
file also contains standard deviations of wind direction and wind
elevation angle. Several air parameters needed to obtain fluxes in
conventional "density"-based units (moist air density, specific heat
capacity, etc.) are also in the data file.

### Diagnostic Variables

Several types of diagnostics variables are kept in the b1 file:

1.  **Data processing:** Number of valid samples for each primary
    variable, number, and means of detected and removed outliers, number
    of invalid or out-of-range samples.

2.  **Sensor status:** Serial number of the sensor, number of samples
    with invalid sonic status flag, number of samples with invalid IRGA
    status flag ("hardware problem" and "blocked optical path" given
    separately), IRGA calibration factors used to convert voltages into
    physical units, and the time lag value used to synchronize sonic and
    IRGA datastreams.

3.  **Environmental:** Average voltage of IRGA cooler, average
    temperature inside the IRGA electronics enclosure.

### Data Quality Flags

The b1 data file contains basic data-quality flags for most important
variables; the flags indicate the variable status (bit values), as
follows:

-   0x0 = value is within the specified range.

-   0x1 = value is equal to "missing_value."

-   0x2 = value is less than "valid_min."

-   0x4 = value is greater than "valid_max."

-   0x8 = value failed the "valid_delta" check.

### Dimension Variables

The global attributes section of the netCDF data file contains
geographic coordinates (location) of the ECOR system and the altitude of
the ground where the instrument is deployed; the "sensor location"
parameter refers to the height of the instrument above the ground. The
time variables denote the beginning of the 30-minute measuring period.

The sign convention for primary (measured) variables and estimated
quantities is positive for upward vertical wind component and upward
atmospheric flux.

The standard ARM site arrangement has the sonic sensor "North" mark
pointing along the boom to the tower; the boom is usually pointing due
south; the u wind component is north-south with positive toward the
north; the v wind component is east-west with positive toward the west.
NOTE: No correction is made to convert the u or v component into the
meteorological "north" or "east" wind component when the tower boom is
not aligned to the south; the u wind component is "along boom," the v
wind component is "cross boom."

## Annotated Examples

Not available at this time.

## User Notes and Known Problems

Measurement inadequacies are known to exist in the eddy covariance
measurement technique. Instrument limitations are the source of most of
the measurement problem (Cook et al. 2006; Twine et al. 2000). The
sources are listed below:

1.  Real temperature values derived from sonic SOS measurements by the
    WindMaster Pro are significantly overestimated at low temperatures
    and underestimated at high temperatures. Calibration of nine sonic
    anemometers in a temperature and humidity-controlled environment
    showed that each sensor temperature response was biased and had a
    "slope" that differed significantly from 1:1, ranging from 0.71 to
    0.87. Bias has no influence on sensible heat flux estimates, but
    these "slopes" translate directly into a significant sensible heat
    flux underestimation (this then affects corrections to the latent
    heat flux and CO~2~ flux in the ECOR value-added procedures (VAP)
    that are based on sensible heat flux). This problem became apparent
    during final tryouts of the system, when it was too late to change
    to another sonic model or vendor. A linear correction procedure was
    implemented to account for the identified sensor deficiency; a more
    detailed discussion is in Pekour (2004).

2.  The ECOR technique has a measurement uncertainty of about 10% just
    due to calibration issues, biases, vertical alignment, etc. Part of
    this uncertainty is sometimes reduced through judicious processing
    of the data, which is done every half hour, mostly in the ECOR VAP
    post-processing (vertical alignment and sonic SOS temperature
    corrections are included in the original ECOR datastream data).

3.  Flux shortfalls of 10% to 25% are often seen in ECOR results (35%
    shortfalls are less common, but can occur), even after
    post-processing, with the smallest shortfalls for bare soil and
    short vegetation and the largest shortfalls for tall vegetation like
    forests, corn, etc. Some conditions that can cause the shortfall
    include:

    a.  Canopy Energy Storage: There is storage of energy in the
        vegetation canopy and litter that is not accounted for by the
        eddy covariance technique or in the radiation/soil heat flux
        measurements (such as measured by the EBBR) that determine the
        energy balance against which the ECOR measurements are typically
        compared.

    b.  Sonic Anemometer Frequency Measurement Limitation: The sonic
        anemometer is physically incapable of properly measuring the
        lowest frequency components of flux. The size of the sensing
        volume of the sonic limits its ability to capture larger
        atmospheric motions that make up the low- frequency component
        (atmospheric wave motions, horizontal and vertical advection
        caused by topography, dissimilar surfaces downwind of the main
        fetch, and changes in air mass).

    c.  Non-steady Atmospheric Conditions: Rapid changes in wind
        direction, atmospheric stability, temperature, pressure, water
        vapor content, and CO~2~ content over a period of seconds to
        several minutes can be too rapid or sustaining for the
        covariances to be properly calculated.

## Frequently Asked Questions

*Where do I get more information about SGP ECOR systems?*

Contact the instrument mentor at <drcook@anl.gov>.

# Data Quality

## Data Quality Health and Status

The following links go to current data quality health and status
results.

-   DQ HandS (http://dq.arm.gov)

-   NCVweb for interactive data plotting
    (<http://dq.arm.gov/ncvweb/ncvweb.cgi>)

The tables and graphs shown contain the techniques used by ARM's data
quality analysts, instrument mentors, and site scientists to monitor and
diagnose data quality.

## Data Reviews by Instrument Mentor

-   Visual QC frequency: Daily to weekly

-   QC delay: Typically 1-3 days

-   QC type: Instrument mentor routinely views graphic displays that
    include plots (day courses) of all calculated quantities and
    comparison plots (time series or scatter plots) of relevant
    parameters with data from adjacent ECOR/EBBR systems and collocated
    EBBR (CF only) and Surface Meteorological Observing Station (SMOS)
    systems (not at EF10 and EF16) (Cook et al. 2006).

Monthly reviews of the ECOR data are prepared by the mentor and
submitted to the Instrument Mentor Monthly Summary (IMMS) report
database; these reports are available at "Related Documents" on the ECOR
web page.

Beginning in FY2006, Data Quality Reports (DQRs) are not written for
missing data or for situations when QC flags clearly show that the data
are incorrect (this is true for most of the conditions listed below).
DQRs are written for periods when data are incorrect, when the situation
is not represented by QC flags in the data, and it is not obvious that
the data should have been flagged as incorrect.

## Data Assessments by Site Scientist/Data Quality Office

The following guidance has been provided by the ECOR mentor for use by
the Data Quality Office in preparing their weekly assessment report for
the ECOR systems.

**ECOR Data Quality Guidance**

**David R. Cook**

**16 December 2006**

**Introduction:** The best way to tell someone what to look for in
assessing the ECOR data is to describe conditions that reflect correct
and incorrect data. For the most part, the QC checks provide adequate
guidance. However, there are conditions for which the QC flags do not
provide the needed guidance to be able to interpret the correctness of
the data. Therefore, please use the information below as further
guidance.

The fc and rho limits were changed in the ingest in late October 2005;
therefore, fewer red QC flags should appear for those now.

**Primary Measurements:** fc (CO~2~ flux), lv_e (latent heat flux), h
(sensible heat flux), k (momentum flux), ustar (friction velocity); all
of these are calculated quantities. The QC limits set in the ingest are
appropriate for the measurements (primary and otherwise), although there
are times when legitimate values fall outside the QC limits.

**Nuisance QC Flags:** the k and ustar flags are frequently tripped,
especially at the E21 Okmulgee forested site. However, it is normally
only when the minimum flag is tripped that there is a concern about data
quality; values below the minimum usually indicate low wind speed
conditions (\< 1 m/s) that do not produce accurate flux (fc, lv_e, h, k)
measurements.

**Comparison of Data at Different ECOR Sites:** The measurements can
generally be favorably compared with those at adjacent sites, keeping in
mind that climate conditions from one side of the SGP site to another
can differ sharply. However, comparisons cannot be made between the E14
(wheat, corn, stubble, bare soil) or E21 (forest) and the other seven
ECORs, which make measurements over grass, because the surface
vegetation types are different; such comparisons are likely to show
significant differences.

**Comparison of Data with the EBBR:** The only collocated ECOR and EBBR
are at E13/E14 (SGP Central Facility). Caution must be used in the
comparison of the two systems because they usually see different
vegetation surfaces (Cook et al. 2006). The best comparison can be made
for straight north or northwest wind directions, when both systems view
the same grass surface. For other directions, the two systems are
viewing different vegetation surfaces and the fluxes from the two will
probably not be similar, unless perhaps, the ground is snow covered.
Except for the E14 and E21 sites, sensible and latent heat flux, and
wind speed and direction measurements can be compared for adjacent ECOR
and EBBR systems (which all view grassland), again remembering that
there are likely to be climatologically driven differences. No other
measurements can be reliably compared, mostly because the ECOR LI-7500
and sonic anemometer are not meant to produce accurate measurements of
anything else that both systems measure.

**Comparison of Data with the SMOS:** SMOS systems are collocated with
ECOR systems at seven of the nine ECOR sites (exceptions are E10 and
E16). There are no measurements of the two systems that can be directly
compared. Wind speed and direction for the two systems are at different
heights (SMOS 10 m, ECOR 3 m), so it is expected that the SMOS wind
speed will be greater than the ECOR wind speed. Wind direction may be
similar but somewhat different if a frontal passage or strong advection
is taking place. The ECOR temperatures (sonic and IRGA) and pressure
(IRGA) are only approximate and are not meant to provide the same
accuracy as the SMOS absolute measurements; therefore, they can be
expected to be considerably different under warm weather conditions,
especially.

**Common Conditions Reflecting Correct or Incorrect Data:** The ECOR
data are only useful for particular wind directions at each Extended
Facility (EF). Please use the following resources to help in
interpreting the ECOR data from the "new" systems that were installed
in 2004:

1\. QC flags in the ECOR data.

2\. IMMS at <http://www.db.arm.gov/IMMS/>.

3\. SGP EF Surface Conditions Observations (EFSCO) at
<http://198.124.96.210:591/sfc_cond1/default.htm>.

4\. The information below on wind direction dependencies and conditions
that commonly cause incorrect data.

Wind Direction Dependencies (numbers are wind direction in degrees); the
vegetation surfaces and corresponding wind directions over which the
fluxes have been measured are listed; wind directions that are not
listed are directions for which the fetch is insufficient, and
therefore. for which the ECOR flux data are suspect. Appropriate fetch
was determined from a 1/70 measurement height to fetch ratio, resulting
in a required minimum fetch of 210 m. Changes in surface vegetation type
and state can occur with time (refer to the EFSCO):

PYE: For some wind directions, the horizontal fetch was not
representative of the field in which the AMF was located. Therefore, for
the wind direction ranges 66-92 (buildings and trees) degrees, the
fluxes are affected by insufficient fetch and surfaces, buildings, or
vegetation that are not similar to the local field conditions.

NIM: For some wind directions, the horizontal fetch was not
representative of the field in which the AMF was located. Therefore, for
the wind direction ranges 90-170 (buildings) and 220-280 (trees)
degrees, the fluxes are affected by insufficient fetch and surfaces,
buildings, or vegetation that are not similar to the local field
conditions.

FKB: For some wind directions, the horizontal fetch was not
representative of the field in which the AMF was located. Therefore, for
the wind direction ranges 40-159 and 176-209 degrees, the fluxes are
affected by insufficient fetch and surfaces, buildings, and vegetation
that are not similar to the local field conditions.

EF1:

0-53, 120-360 wheat or wheat stubble

EF3:

0-48 pasture

132-260 soybeans, wheat

EF5:

80-154 sorghum or wheat

155-260 wheat or wheat stubble

EF6:

0-90 grazed pasture

91-360 alfalfa and brome grass

EF10:

0-90, 270-360 grazed

91-269 grass

EF14:

129-265 wheat (2004), corn (2005), soybeans (2006),

But normally wheat; 352-85 ungrazed grass

EF16:

134-269 pasture

334-360 ungrazed grass

EF21:

0-360 mixed deciduous forest

(note that for the direction of the tower, 0-30,

the data may be suspect)

EF24:

80-280 wheat or wheat stubble

Some conditions that commonly cause the ECOR primary measurements to be
incorrect or missing, plus some advice for interpreting the data are as
follows:

-   Periods of precipitation, fog, and dew (frost) often cause incorrect
    water vapor and CO~2~ measurements. This is caused by water laying
    on the lower window of the LI-7500 CO~2~/H~2~O sensor, thereby
    obstructing the passage of the sensing infrared (IR) radiation (very
    light precipitation may have little or no effect). The CO~2~ portion
    of the instrument is more sensitive to this condition, so it is not
    unusual for latent heat flux to be correct, even though the CO~2~
    flux is not. I do not write DQRs or indicate time periods in the
    monthly report for this wetting condition. The data user should look
    at the ECOR wetness sensor measurement (mid 2006 on), the collocated
    or nearby SMOS rain gages, or the DQ HandS ECOR plots to determine
    times of precipitation. You can assume that offscale or spiked
    readings in the nighttime hours before dawn are normally caused by
    dew or frost on the CO~2~/H~2~O sensor.

-   Large spikes (positive and negative) in CO~2~ flux can occur when
    the flux is essentially zero (see E16, 08/16/05, 0800-0930 GMT).

-   Friction velocity (ustar) and momentum flux (k) are often flagged
    during light wind conditions. This is normal, because these
    measurements, as well as the fluxes of sensible heat flux (h),
    latent heat flux (lv_e), and CO~2~ flux (fc) cannot be trusted
    because of the lack of ability of the sonic anemometer to make
    proper measurements during very low wind speeds (especially \< 1
    m/s).

-   Sudden shifts in wind direction are not handled well by the ECOR
    coordinate transform routine, often resulting in a spike in the
    fluxes for a half-hour measurement period.

-   Momentum flux and friction velocity have opposite signs, and mirror
    each other, because friction velocity is computed from momentum
    flux; in the DQ HandS plots they are plotted to scales with opposite
    sign orientations, so they trend together.

-   ECOR time stamps are for the beginning of the half hour, whereas
    those for the SMOS and EBBR are for the end of the half hour.
    Therefore, when comparing data for these systems (such as on DQ
    HandS plots), the ECOR measurements show a half hour earlier than
    the commensurate measurements for the SMOS and EBBR.

-   Plots of water vapor flux (lv_e) and CO~2~ flux (fc) normally mirror
    each other: in the DQ HandS plots they are plotted to scales with
    opposite sign orientations, so they tend to trend together.

-   During rare occasions, the QC flag for elevation (angle of attack of
    the wind) is exceeded, usually on the positive side. The QC flag
    limits for elevation are quite generous; this was done to try to
    accommodate the large angles that can occur at the forested Okmulgee
    site EF21. However, the angles at the Okmulgee site can often be
    much larger than the QC limits because of the very uneven height of
    trees in the mixed deciduous forest.

-   Fluxes of CO~2~, sensible heat, and latent heat at E21 Okmulgee
    forest are often larger than at other sites, particularly the fluxes
    of water vapor and CO~2~; the latter will often be twice what it is
    at the other ECOR sites.

-   The plots of data from the forest site at Okmulgee exhibit more
    noise than is seen at the other ECOR sites; this is expected and
    normal because the tree structure presents a much rougher and less
    homogeneous surface than exists for grassland or crops.

-   When the LI-7500 CO~2~/H~2~O serial datastream is not available
    (pressure and temperature missing), default values are used in the
    calculation of the CO~2~ and latent heat fluxes; when default values
    are used, resulting errors in the fluxes are within the +/- 10%
    system error.

-   Missing data periods occur at times; this is usually a site data
    system collection/communication problem; it can also occur if the
    ECOR data acquisition computer fails. Missing data are sometimes
    filled in later from manual or automatic re-collection of the data.

## VAPs and Quality Measurement Experiments

An ECOR VAP is under development. The VAP will perform corrections for
WPL buoyancy effects (Webb et al.,1980) and sensor separation and
frequency attenuation effects, as well as outlier removal and
gap-filling of outlier and missing data.

# Instrument Details

## Detailed Description

### List of Components

**Ultrasonic anemometer:** WindMaster Pro by Gill Instruments, Ltd.
([http://www.gill.co.uk](http://www.gill.co.uk/)):

-   Full wind vector in the form of orthogonal wind components u, v, and
    w Accuracy:

```{=html}
<!-- -->
```
-   For u and v = 1.5% root mean square (RMS) error for winds below 20
    ms-1, 3% otherwise

-   For w = 3% of magnitude

```{=html}
<!-- -->
```
-   SOS

**Range:** 307 to 367 ms^-1^

**Resolution:** 0.01 ms^-1^

**Accuracy:** 3% RMS error for winds \<20 ms^-1^, 6% RMS error for winds
20 to 60 ms^-1^

-   Analog inputs

**Type:** eight single-ended or four differential (software selectable)

**Range:** --5 to +5 VDC

**Resolution:** 14 bit

**Accuracy:** 0.05% of full scale (for temperature from +5 to +35°C)

0.1% of full scale (for --40 to +5°C, +35 to +60°C)

**Open-path CO~2~/H~2~O IRGA:** LI-7500 by LI-COR, Inc.
(<http://www.licor.com/env>):

-   water vapor density

**Range:** 0 to 2000 mmol m^-3^ (software selectable)

**Accuracy:** About 1% (limited by calibration)

**Precision:** About 0.14 mmol m^-3^ (typical RMS noise).

-   CO~2~ concentration

**Range:** 8 to 32 mmol m^-3^ (software selectable)

**Accuracy:** About 1% (limited by calibration)

**Precision:** About 4 µmol m^-3^ (typical RMS noise).

-   Analog outputs

**Type:** Two-user selectable

**Range:** 0 to 5 V DC

**Resolution:** 16 bit

**Update rate:** 300 Hz

**Data acquisition computer**

-   Single board computer system using Linux operating system

-   Data collection and initial processing performed with ECOR software
    (written in C by M. Pekour at Argonne National Laboratory).

### System Configuration and Measurement Methods

In a typical arrangement, the ECOR system is placed on the north side of
a wheat field; sonic and IRGA sensor heads are mounted on a small tower
at 3 m above ground level, at the end of a horizontal boom pointing
south. The computer and communication devices are installed in an
enclosure with basic temperature control (ventilation or heating). One
exception to the usual arrangement is the Okmulgee site (EF21), where
the ECOR system is installed on a tall tower (15 m above ground, about
3 m above the canopy) in a hardwood forest.

The IRGA provides fast-response measurements of water vapor density and
CO~2~ concentration in digital and analog form; the sonic anemometer
provides three wind components and the SOS data in digital form
(retrieved via serial link) at a rate of 10 Hz and performs synchronous
digitization of the IRGA analog outputs. The digital datastream from the
IRGA is also recorded by the data acquisition computer; it is used to
extract IRGA diagnostics values and as a second copy of the water vapor
density and CO~2~ concentration data.

The raw datastream is recorded into raw data files by 30-min portions
and is processed every half hour by the ECOR computer. The raw and
processed data files are transferred to the SGP Central Facility for
ingest (conversion into the netCDF format and incorporation of QC flags)
and shipment to the ARM Data Archive.

### Specifications

This section is not applicable to this instrument.

## Theory of Operation

The WindMaster Prosonic anemometer uses three pairs of orthogonally
oriented, ultrasonic transmitter/receiver transducers to measure the
transit time of sound signals traveling between the transducer pairs in
both directions. Pairs of measurements made along each axis, 30 times
per second, are averaged appropriately to provide a 10-Hz datastream.
The wind speed along each axis is determined from the difference in
transit times. The SOS is determined from the average transit time along
all three axes. The air temperature can be derived from the SOS with a
well-known correction for the humidity effects.

The IRGA measures water vapor density and CO~2~ density by detecting the
absorption of IR radiation by water vapor or CO~2~ in the light path.
Two IR wavelength bands are used, centered on strong water vapor or
CO~2~ absorption lines. The sensor provides digital (serial RS232) and
analog (two 16-bit DAC) outputs. Details of the IRGA principles, design,
and performance can be obtained from LI‑COR Environmental
(<http://env.licor.com/PDF_Files/LI7500.pdf>).

The sonic anemometer samples the gas analyzer analog outputs ten times
per second, synchronously with wind measurements, and combines all the
data into a single serial datastream.

The data acquisition computer continuously records serial datastreams
from both sensors and stores them into 30-minute files. Half-hourly flux
data processing is accomplished on the same computer, independently of
the data acquisition process. Half-hour-averaged ambient air
temperature, water vapor pressure, and barometric pressure derived from
the sonic anemometer and the IRGA raw data are used in the calculations
of moist air density, specific heat of dry and moist air at constant
pressure, and heat of vaporization of water necessary for presentation
of the fluxes in "density-based" units.

Data analysis includes a de-spiking procedure, basic QC of every data
point (sensor status, minimum/maximum check), time delay for the sonic
data to account for the internal delay in the IRGA, conversion of the
SOS into air temperature, and computation of statistics (mean, variance,
covariance, skewness, kurtosis, etc.). Two-dimensional (2D) coordinate
rotations are applied to the variances and covariances to achieve zero
mean vertical wind speed.

Momentum flux is determined from the correlation between horizontal and
vertical wind components in "rotated" coordinates. Similarly, the
vertical fluxes of sensible heat, latent heat, and CO~2~ are determined
directly from the correlation between the "rotated" vertical velocity
and temperature, water vapor density, and CO~2~ concentration,
respectively.

In general, the fluxes calculated as described represent vertical fluxes
from a variable area (a footprint) of the surface upwind of the
instrument. The size of this area usually varies from 10 to 100 times
the height of the sensors above the surface. The footprint depends on
surface properties (roughness, displacement height, etc), atmospheric
state and stability, and turbulent intensity within the atmospheric
surface layer. The surface conditions (crop height, density, and state)
can be found in the ECOR section of EFSCO weekly reports
(<http://198.124.96.210:591/sfc_cond1/default.htm>).

See Citable References for further discussions.

## Calibration

### Theory

Ideally, the sonic anemometer does not require calibration for either
wind or temperature measurements, although the WindMaster Pro SOS
channel needs to be calibrated to achieve accurate sensible heat
measurement (see Pekour 2004 for more detailed discussion). The IRGA
sensors need to be calibrated periodically. The IRGA is calibrated by
passing gas of known concentration through a calibration tube installed
in the sensor head, so that the tube surrounds the light path over which
IR absorption is measured. The zero (offset) is typically calibrated by
using "zero" gas or dry nitrogen from a gas cylinder. The gains of the
CO~2~ and H~2~O channels are calibrated by using a cylinder with a known
concentration of CO~2~ and flow from a water vapor generator (e.g.,
Licor Inc. LI-610 Dew Point Generator).

### Procedures

A chemical replacement and calibration procedure was developed and
implemented for the ECOR\
LI-7500 CO~2~/H~2~0 IRGA sensors. This procedure is available on the
[ECOR web page](http://www.arm.gov/instruments/instrument.php?id=ecor).

### History

The Calibration Test Reports can be accessed here
(<http://198.124.96.210/menus/caltopmenu.html>).

## Operation and Maintenance

### User Manual

No single comprehensive user manual for the ECOR system is available for
general use; rather, vendor-supplied documentation on sensors and a
collection of procedures prepared by mentors are provided for internal
use by Site Operations.

### Routine and Corrective Maintenance Documentation

The ECOR Preventive Maintenance procedures and reports are online at
<http://198.124.96.210:591/ef_pm1/default_1.htm>, as is a database of
Corrective Maintenance reports,
<http://198.124.96.210/menus/cmreports.html>.

### Software Documentation

Contact the instrument mentor (<drcook@anl.gov>) for software
documentation.

### Additional Documentation

This section is not applicable to this instrument.

## Glossary

Also see the [ARM Glossary](http://www.arm.gov/about/glossary.stm).

## Acronyms

2D two-dimensional

3D three-dimensional

ARM Atmospheric Radiation Measurement (Program)

DQ Data Quality

DQ HandS Data Quality Health and Status

DQR Data Quality Report

ECOR eddy correlation (flux measurement)

EF Extended Facility

EFSCO EF Surface Conditions Observations

IMMS Instrument Mentor Monthly Summaries

IR infrared

IRGA infrared gas analyzer

QC quality control

RMS root mean square

SGP Southern Great Plains

SMOS Surface Meteorological Observing Station

SOS speed of sound

QC quality control

WPL Webb, Pearman, Leuning

Also see the [ARM Acronyms and
Abbreviations](http://www.arm.gov/about/acronyms.stm).

## Citable References

Cook DR, ML Fischer, and DJ Holdridge. 2006. "Comparison of ECOR, EBBR,
and CO2FLX System Fluxes." Abstracts (from poster), *Sixteenth ARM
Science Team Meeting*, Albuquerque, New Mexico, p. 56; March 27-31.

Kaimal JC and JJ Finnigan. 1994. *Atmospheric Boundary Layer Flows:
Their Structure and Measurement*. Oxford University Press, New York.

Massman WJ and X Lee. 2002. "Eddy Covariance Flux Corrections and
Uncertainties in Long-Term Studies of Carbon and Energy Exchanges."
*Agricultural and Forest Meteorology* 113(1-4):121-144.

Moore,CJ. 1986. "Frequency Response Corrections for Eddy Correlation
Systems." *Boundary-Layer Meteorology* 37(1-2):17-35.

Pekour MS. 2004. "Experiences with the Windmaster Pro Sonic Anemometer."
In the *Proceeding of the 12^th^ International Symposium on Acoustic
Remote Sensing*, 11-16 July, 2004, Clare College, Cambridge, UK,
pp. 137-140.

Twine TE, WP Kustas, JM Norman, DR Cook, PR Houser, TP Meyers, JH
Prueger, PJ Starks, and ML Wesely. 2000. Correcting Eddy-covariance Flux
Underestimates Over a Grassland." *Journal of Agricultural and Forest
Meteorology* 103:279-300.

Webb EK, GI Pearman, and R Leuning. 1980. "Correction of Flux
Measurements for Density Effects Due to Heat and Water Vapour Transfer."
*Quarterly Journal of the Royal Meteorology Society* 106(44):85-100.

Wilczak JM, SP Oncley, and SA Stage. 2001. "Sonic Anemometer Tilt
Correction Algorithms." *Boundary-Layer Meteorology* 99(1):127-150.