AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-4719-2016Automatic processing of atmospheric CO2 and CH4 mole fractions at
the ICOS Atmosphere Thematic CentreHazanLynnlynn.hazan@lsce.ipsl.frTarniewiczJérômehttps://orcid.org/0000-0001-8445-7048RamonetMichelLaurentOlivierAbbarisAmaraLaboratoire des Sciences du Climat et de l'Environnement (LSCE/IPSL), UMR
CEA-CNRS-UVSQ, Gif-sur-Yvette, FranceLynn Hazan (lynn.hazan@lsce.ipsl.fr)22September2016994719473616February201610May201622July20164August2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/4719/2016/amt-9-4719-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/4719/2016/amt-9-4719-2016.pdf
The Integrated Carbon Observation System Atmosphere Thematic Centre (ICOS ATC) automatically processes atmospheric greenhouse gases mole
fractions of data coming from sites of the ICOS network. Daily transferred
raw data files are automatically processed and archived. Data are stored in
the ICOS atmospheric database, the backbone of the system, which has been
developed with an emphasis on the traceability of the data processing. Many
data products, updated daily, explore the data through different angles to
support the quality control of the dataset performed by the principal
operators in charge of the instruments. The automatic processing includes
calibration and water vapor corrections as described in the paper. The mole
fractions calculated in near-real time (NRT) are automatically revaluated as
soon as a new instrument calibration is processed or when the station
supervisors perform quality control. By analyzing data from 11 sites, we
determined that the average calibration corrections are equal to 1.7±0.3µmol mol-1 for CO2 and 2.8±3 nmol mol-1 for CH4. These biases are important to correct to
avoid artificial gradients between stations that could lead to error in flux
estimates when using atmospheric inversion techniques. We also calculated
that the average drift between two successive calibrations separated by
15 days amounts to ±0.05 µmol mol-1 and ±0.7 nmol mol-1 for CO2 and
CH4, respectively. Outliers are generally due to errors in the
instrument configuration and can be readily detected thanks to the data
products provided by the ATC. Several developments are still ongoing to
improve the processing, including automated spike detection and calculation
of time-varying uncertainties.
Introduction
Rising greenhouse gas (GHG) concentration in the atmosphere is a major source
of forcing in the current changing climate (Intergovernmental Panel on
Climate Change, 2013). Worldwide measurement systems are being implemented
(Andrews et al., 2014; Deng et al., 2014; Deutscher et al., 2014; Dils et
al., 2014; Fang et al., 2014; Frankenberg et al., 2015; Houweling et al.,
2014; Ramonet et al., 2010) to both monitor and understand these increasing
concentrations. In Europe, the Integrated Carbon Observation System (ICOS),
an international research infrastructure for precise in situ measurements, is
under construction. ICOS is a distributed infrastructure composed of three
integrated networks measuring GHG in the atmosphere, over the ocean and at
the ecosystem level. Each network is coordinated by a thematic center that
performs, among other things, centralized data processing. Further processing
takes place in the ICOS Carbon Portal where, for example, 2-D GHG flux maps
are computed using the ICOS atmospheric station time series. One of the key
focuses of ICOS is to provide standardized and automated high-precision
measurements, which is achieved through the use of measurement protocols and
standardized instrumentation. The implementation of ICOS included a
preparatory phase (2008–2013, EU FP7 project reference 211574) with a
demonstration experiment, later called “extended demo experiment” in the
period between the end of the preparatory phase and the formal start of ICOS
as a legal entity at the end of 2015. In total, 11 sites have been
participating in the atmospheric network during this demonstration experiment
and its extension. The data center of the ICOS Atmosphere Thematic Centre (ATC), located at the
Laboratoire des Sciences du Climat et de L'Environnement (LSCE, France),
began to automatically process atmospheric GHG mole fractions in
2009. The centralized data processing aims to reduce inter-laboratory
differences and facilitate the production of a coherent dataset in near-real
time (NRT). The NRT processing chain was built on the expertise gained during
previous European projects including CARBOEUROPE, Infrastructure for
Measurements of the European Carbon Cycle (IMECC) and Global Earth
Observation and MONitoring (GEOMON). NRT is defined here as on a daily basis.
NRT data production is more demanding but brings several benefits. In terms
of station management, it allows station principal operators and
investigators to get a fast feedback on the data; it improves reactivity in
case of disruption in the data flow and thus limits data gaps. NRT data are
also useful for campaign-based measurement setups. It allows us, for example, to
adjust the campaign setup and observation plan or to place more emphasis on a
specific phenomenon. On a more scientific level, NRT data allow for early-warning monitoring systems, for example, in the case of extreme GHG events
(e.g., drought, high-pollution event). NRT is a necessity to perform data
assimilation for operational systems (e.g., Monitoring Atmospheric
Composition & Climate – MACC) in which NRT data are either used as a
diagnostic or ingested in assimilation mode to improve operational
forecasting
(http://www.gmes-atmosphere.eu/d/services/gac/verif/ghg/icos).
NRT data are, however, less precise than so-called consolidated data. In
ICOS, consolidated data are expected to be produced on a 6-month basis. They
contain additional data treatment steps ensuring increased precision and
confidence in the dataset. These steps include potential correction due to
drift in the reference scales used to make the measurements and ”manual
visual” inspection of the data to screen for potential problems that are
difficult to detect automatically. The estimation of time-varying
uncertainties, which is an essential information for an optimal use of the
data, is still under development and therefore not addressed in the
framework of this study.
To further increase confidence and trust, ICOS is building an efficient
scheme to ensure traceability of the data. Persistent identifiers will
be attached to the data for both proper acknowledgment and citation. ICOS
atmospheric data are traceable to the Global Atmosphere Watch (WMO/GAW)
international reference scales for GHG, and the history of data processing
steps is archived. This allows full traceability and transparency of the
consolidated dataset, which will be the basis for elaborated products and
services.
This article describes the computing facility dedicated to the ICOS ATC at
LSCE, the different steps of the automatic processing of CO2 and
CH4 mole fractions, including the automatic quality control of the raw
data, and the corrections due to water vapor interference and calibration
(WMO scale). Most of the processing protocols and parameters are illustrated
with a few examples from instruments currently providing raw data to the
ICOS ATC as part of the ICOS extended demonstration experiment. Because the
paper is focused only on CO2 and CH4, only analyzers deployed in
the monitoring network that measure these gases have been considered. To
date, for these species, only cavity ring-down spectroscopy (CRDS) analyzers
commercialized by the Picarro company meet the ICOS requirements, but other
instruments may be added in the future.
Schematic view of ICOS ATC network
infrastructure.
Automatic data processing of CO2 and
CH4 data at ICOS ATC. We consider three types of data: “in situ”
corresponding to ambient air, “target” when a cylinder filled with a
reference gas is measured and “calibration” when calibration cylinders are
measured.
Server organization and data archive at ICOS ATC
The instrumental raw data are transferred at least once a day from the
monitoring sites to an ATC server using the secure file transfer protocol
(SFTP). The files are first archived, and the data are automatically
processed by the ICOS database. Three dedicated servers (Fig. 1) are
installed and maintained at ICOS ATC to fulfil automatic data collection from
measurement stations, processing and distribution to users.Data collection (icos-ssh server):
ICOS network stations upload
raw data from the instruments to the ATC on a daily basis. This upload can be
managed by upload software developed at ATC. All collected data are
centralized on the icos-ssh server and upon receipt are copied to a dedicated
server for processing and archival. Data are kept on the icos-ssh server for
1 month after their upload. A duplicate archive of the raw data will be
done at the ICOS Carbon Portal. Currently, the amount of data uploaded is on
the order of 6.5 MB day-1 per CO2/CH4 in situ analyzer,
corresponding to a total ∼ 170 MB a day for 26 stations processed
daily by ATC. Note that the files transferred every day to the ATC are not
the high-resolution absorption spectra used to retrieve mole fractions
(Crosson, 2008). The raw data files of the trace gas analyzers currently
processed at ATC contain CO2/CH4 information already in geophysical
units. It is foreseen that the full spectra files will be archived at the
ICOS site on specific hard drives for further post-analysis. The amount of
data to archive would then be approximately 230, 780 MB day-1 and
1.3 GB day-1, respectively, for models ESP100/G1301, G2301 and G2401
of CRDS Picarro analyzers.
Data processing (icos-data server):
Upon reception, data are
processed. The processing is performed on the icos-data server, a dedicated
internal (inaccessible from outside the ATC) server at ATC that also hosts
the ICOS atmospheric database. The icos-ssh server (accessible from outside
the ATC) also hosts the QA / QC applications developed at ATC, used by
principal investigators (PIs) and authorized persons to carry out the
measurement control.
Data distribution (icos-web server):
The distribution of data
and data products are served by the icos-web server. This server hosts the
ATC website and uses an open-source content management system framework
(Drupal). For security purposes, only read-only access is allowed to some
partitions on the icos-data server. Access to the ICOS atmospheric database
hosted on icos-data from icos-web is prohibited.
Traceability of the data downloads and long-term archival, which are not
described here, are being implemented in collaboration with the Carbon Portal
of ICOS, which is hosted and operated at Lund University in Sweden
(https://www.icos-cp.eu).
Processing: automatic filtering of raw data
Specific processing chains have been developed for each type of trace gas
analyzer, but the general framework remains the same. Here, we describe the
processing chain and associated parameters defined for the treatment of
continuous measurements of CO2 and CH4 atmospheric mole fractions.
Similar chains have also been developed for measurements of other ICOS
parameters such as meteorological variables or radon but are not described in
detail in this article. Figure 2 gives an overview of the different steps of
the CO2 and CH4 data processing. One analyzer routinely measures
three types of air samples: ambient air, air from target tanks and air from
calibration tanks. The target tanks, also called “surveillance tanks”, are
used as a quality control tool. Their mole fractions are known (prescribed by
the ICOS Central Analytical Laboratories (CAL) located in Germany, which is
in charge of providing the calibration gases needed by the atmospheric
stations) and are processed similarly to the ambient air. Consequently, the
temporal variations of the target gas measurements can be used to estimate
time-varying uncertainties (Yver Kwok et al., 2015). It should be noted,
however, that the target gases do not pass through the whole air inlet, and
possible bias due to a contamination in the inlet upstream the connection of
the target gas is not considered. As recommended by WMO, two target tanks,
with a significant range in the mole fractions of the measured species, are
required at ICOS stations (WMO, 2012). Short-term target gases are analyzed
at least once per day, whereas long-term target gases are measured only once
every 2 to 4 weeks (after each calibration sequence). This configuration
allows for both frequent measurements using one target gas and the
possibility to keep the other target gas over a long period (10–20 years).
The system also handles so-called inter-comparison (ICP) tanks, which
correspond to cylinders analyzed as part of a comparison exercise like the
round-robin set up by WMO/GAW or by the Integrated non-CO2 Greenhouse
Gas Observing System (InGOS) European project (Manning et al., 2009; WMO,
2012). The ICP gases are processed similarly to target gases. The processing
of the different types of gas follows the same general scheme: data control,
correction, filtering and time aggregation (Fig. 2).
List of user flags. The user flag is instrument independent. Beyond
the validity status of the data, each set of flags conveys additional
meaning. Automatic quality control flags imply that no expert has manually
inspected the data yet, whereas manual quality control flags imply that an
expert has manually inspected the data. The backward propagation of manual
quality control flags implies that an expert has performed manual inspection
of the corresponding aggregate data but not the data directly.
ValidInvalidDefinitionData leveldatadatainvolvedUNAutomatic quality controlRaw, 1 min,hourlyOKManual quality controlRaw, 1 min,hourlyRHBackward propagation of manual quality control from hourlydata to minutely andraw dataRaw, 1 min
List of descriptive flags. The descriptive flag is instrument
independent and is picked from a predefined list. The flag is case sensitive.
Multiple flags (i.e., letters) can be set simultaneously on a single value.
There is a list to be used for invalid data and one to be used for valid
data.
FlagDescriptionStatus ofthe dataSStation not working properlyInvalidIInstrument not working properlyInvaliddAir distribution systemInvalidnot working properlyTTank issueInvalidFStabilization/flush periodInvalidLInlet leakageInvalidEExternal disturbance near the stationInvalidCCalibration issueInvalidAMaintenance with contaminationInvalidXInstrument out of orderInvalidGData out of rangeInvalidQQA operationValidMMaintenanceValidZNon-background conditionsValid
For traceability and transparency of the data processing, each rejection of
data is associated with a flag. For this purpose, an internal cumulative flag
has been defined, which is associated with the different steps of the
processing. The steps and the flag will be described in the following
paragraphs. Because ways and conditions to automatically validate raw data
may differ from an instrument model to another, the list of internal flags
are instrument dependent. If these flags are important for the traceability
of the process, they are inconvenient for the majority of the data users who
request a simple and unambiguous way to separate the valid and invalid data.
For this reason, we have defined another flag scheme named “user flag”, as
described in Table 1. It is instrument independent and allows easy
differentiation of the data that have been validated/invalidated either
through NRT data processing or after the requested inspection of the data by
an expert. This flagging scheme is completed with a third type of flag named
“descriptive flag”, which allows the PI to provide codified reasons for
invalidating data or useful information for validating data. For each data
point, there is an automatic descriptive flag and a manual descriptive flag.
The manual flag is set by the expert via a graphical quality control
application, and the automatic flag is set during the automatic processing of
the data. Both flags use the same list of possible values. The flags are set
only on raw data. The flag information on raw data is carried to the
aggregated data (1 min or hourly averaged or injection). A description
of the “descriptive flag” can be found in Table 2.
Definition of a measurement sequence. As an example, we show the
configuration for the instrument installed at Mace Head station (identified
by the three-letter code MHD), Ireland.
Sequence definition Definition of the calibration sequence Example: MHD no. 41Valve port connections of calibration tanks4 tanks on ports 3, 4, 5 and 6Tank measurement duration20 minNumber of calibration cycles4→320 minDefinition of the ambient/target sequence Valve port connections of sampling line(s) and target tanksPort 1: in situ; 2: short-term targetDuration of ambient air measurement660 minDuration of short-term target measurement20 minDuration of long-term target measurement20 minDuration of reference measurement0 minLong-term target measured before the ambient/tgt sequenceyesLong-term target measured after the ambient/tgt sequencenoShort-term target measured before the ambient/tgt sequencenoShort-term target measured after the ambient/tgt sequencenoNumber of ambient/target cycles62→ 43 420 minDefinition of the intercomparison sequence Valve port connections of sampling line(s) and tanks–Duration of intercomparison tank measurement20 minDuration of short-term target measurement0 minDuration of reference measurement0 minDuration of ambient air measurement0 minDefinition of the overall sequence 1× ambient/target seq.1× calibration seq.
List of the parameters
used for the automatic processing of CO2 and CH4 mole fractions by
CRDS analyzers. The humidity filtering applied to the tank measurements
consists of checking the absolute difference between the wet value and the
computed dry value against the defined threshold. The parameters are specific
to the instrument considered.
Instrument parameterization configuration Stabilization duration Example: MHD no. 41In situ gas5 minTarget gas15 minCalibration gas15 minReference gas0 minPhysical parameters Cavity pressure139.8–140.2 torrCavity temperature44.98–45.02 ∘COutlet valve opening15 000–55 000Processing parameters In situ gas: interval filtering350–500 µmol mol-1 (CO2)1700–2500 nmol mol-1 (CH4)Target gas: humidity filtering< 0.05 µmol mol-1 (CO2)< 0.2 nmol mol-1 (CH4)Calibration gas: humidity filtering< 0.05 µmol mol-1 (CO2)< 0.2 nmol mol-1 (CH4)Correction parameters In situ gas: humidity correctiona=-0.00982, b=-2.393×10-4In situ gas: calibration correctioncf. calibration parameters belowTarget gas: humidity correctiona=-0.00982, b=-2.393×10-4Target gas: calibration correctioncf. calibration parameters belowCalibration gas: humidity correctiona=-0.00982, b=-2.393×10-4Calibration computing parameters Standard deviation for 1 min means of calibration gas measurement< 0.08 µmol mol-1 (CO2)< 0.8 nmol mol-1 (CH4)Standard deviation for cycle means of calibration gas measurement< 0.06 µmol mol-1 (CO2)< 0.5 nmol mol-1 (CH4)Minimum number of cycles per tank2Minimum tanks to compute the fitting equation3Number of cycles for the stabilization period1Fitting equation degree1System configuration
The objective of ICOS is to develop a standardized European monitoring
network for greenhouse gases with centralized data processing. Technical
discussions about the measurement protocols have been organized during the
ICOS preparatory phase through seven working groups. This process has
resulted in the first version of the ICOS Atmospheric Station Specifications
(ICOS, 2015). Because the
monitoring stations have specific local constraints, it has been required
that the processing chains can be parameterized to handle some of the
station specificities. A dedicated application, called ATCConfig, has been
developed to allow the station PIs to configure the stations of which they
are in charge. This application enables the following key points to be
described in detail:
contact persons and institutes in charge of the station and
instruments;
geographic coordinates, postal address and description of the
monitoring station including the different measurement setups with plumbing
schemes;
instrument description: category, model, firmware, location to
trace instrument movements (e.g., for reparation) and various related metadata;
calibration/target tanks: model, tank inspection date, valve and
regulator description, filling date and mole fractions values;
description of the sampling line connections and tank
connections to the instruments;
description of the measurement sequences (in situ air,
calibration and target gases; see Table 3);
definition of the measurement processing parameters (control,
correction and data filtering; see Table 4).
Each registered instrument is assigned a unique identifier used to reference
it (preceded by “no.” in this article). A key aspect of the
designed system is to ensure a high level of traceability that leads us to
keep track of the history of all configurations provided by station PIs.
Regarding the configuration of the measurement processing, we consider three
types of sequences: calibration, ambient/target and inter-comparison. Table 3
provides the list of parameters for each of the three sequences, with the
Mace Head station (identified by the three-letter code MHD) configuration as an
example. The station PIs must configure what is measured (tanks or in situ
air), in which order and for how long. Minimum requirements – e.g., at least
three calibration tanks and two target gases – are prescribed by an ICOS
Atmospheric Station Specifications document.
The full list of parameters to be set up by station PIs for the operation of
in situ CO2/CH4 analyzers is shown in Table 4, with the example of
the Mace Head set of values for instrument no. 41. The means by which those
parameters are used in the automatic processing of the raw measurements of
CO2 and CH4 mole fractions is described in the following
paragraphs.
Control based on analyzer ancillary data
The first step of the processing consists of the evaluation of instrumental
parameters (e.g., temperature, pressure, flow rate). In the case of the
CO2/CH4 analyzers currently used in the ICOS network, each raw data
point is scanned for three parameters: the cavity pressure, the cavity
temperature and the outlet valve opening. These ancillary data are provided
by the analyzer at the same time resolution as the raw CO2 and CH4
data. Consequently, for each single data point, the values of the parameters
are checked against a valid interval or threshold. An example of the range of
variability allowed for those parameters, for instrument no. 41 at Mace Head,
is provided in Table 4. The valid intervals and thresholds are instrument and
location dependent at this point, but discussions are ongoing between the
scientists in charge of the instruments to evaluate the possibility to
standardize these criteria for a given instrument model. This decision
depends on whether the setup of the station has an influence on the
instrument performance. For each GHG data measurement, all selected
parameters are tested against their valid interval or threshold. If at least
one parameter fails, the GHG data are flagged as invalid. Each failure is
traced in the internal cumulative flag (Table 5).
List of internal flags
for instrument type CRDS Picarro model G2301. The example provided in the
third column corresponds to the configuration of instrument no. 41 at Mace
Head station set up on 14 May 2009.
Internal flag nameCriteriaExample (no. 41, MHD)StabilizationData acquired during the stabilization periodAmbient air: 5 min Target gases: 15 min Calibration gases: 15 minCavity pressureCavity pressure not in the valid interval139.8–140.2 torrCavity temperatureCavity temperature not in the valid interval44.98–45.02 ∘COutletOutlet not in the valid interval15 000–55 000HumidityThe difference due to the humidity between raw data and corrected data is above the thresholdCO2: 0.05 µmol mol-1
CH4: 0.20 nmol mol-1FilterData not in the valid intervalCO2: 350–500 µ mol mol-1 CH4: 1700–2500 nmol mol-1CalibrationNo valid calibration–Unitary dataNo unitary data available–One minute standard deviationStandard deviation for calibration 1 min data above the thresholdCO2: 0.08 µmol mol-1
CH4: 0.80 nmol mol-1Cycle standard deviationStandard deviation for calibration injection data above the thresholdCO2: 0.06 µmol mol-1 CH4: 0.50 nmol mol-1MaxDeltaDurationTankThe time interval between 2 successive calibration tanks is too large1 minNbTankThe number of tanks for the calibration is below the minimum required3 tanksTankMinDurationThe measurement duration for a tank is below the configured minimum10 % of the defined duration for the given type of tank (target or calibration)TankMaxDurationThe measurement duration for a tank is above the configured maximum10 % of the defined duration for the given type of tank (target or calibration). The calibration is not rejected, but a warning email is sent to notify the PI that more gas than expected is used up.NbCycleThe number of cycles for a tank measurement during calibration is below the minimum required2 cyclesSequenceCompletenessThe calibration sequence is incompleteSee calibration sequence definitionQuality controlManual rejection flag set up by the station PI–Backwards quality controlPropagation of a manual flag set up by the station PI on an aggregated value (e.g., the hourly mean) to all data used for averaging (e.g., the 1 min means and raw data)–
Examples of user and
internal flags that were attributed to raw data from CRDS Picarro instruments
in 2014. The two last columns provide the number of raw data that have been
attributed an internal flag or combination of internal flags and the
corresponding percentage in the dataset. Most of the data have no internal
flag, indicating that there is no anomaly detected.
Table 6 shows all internal flags that have been attributed to three analyzers
continuously measuring the CH4 mole fractions during 2014. From this
list, it appears that raw data may be rejected for a combination of reasons.
For example, during the stabilization period following the switch from one
gas to another, the cavity pressure and temperature may also be out of the
assigned validity range. Overall, for an instrument working without major
failure, as in the case for the instruments in Table 6, the major cause of
data rejection corresponds to the flushing time needed to stabilize the
measurement after a change in the type of gas to analyze (e.g., from ambient
air to target gas). Typically for a surface site with a single sampling
level, the amount of data rejected for stabilization is on the order of 1 to
2 % of the continuous raw data. For a multiple sampling level site, such
as the Observatoire Pérenne de l'Environnement (identified by the
three-letter code OPE) high tower in France, this percentage of rejected data can
increase to 16 % (Table 6) because of the frequent changes from one
sampling level to another.
Control of the stabilization periods
When the instrument switches between sample types or sampling levels, some
residual gas remains in the common tubing and valves. For a given duration
(called the stabilization period) after such switches, the data are flagged
as invalid to avoid considering residual or mixed gas for further
processing. The stabilization period duration depends on the flow rate, the
volume of the analyzer cell, and the volume of the sampling line where
continuous flushing is impossible. Consequently, the duration of the
stabilization, given in minutes, is instrument and site dependent. Different
values for the flushing time can also be set for in situ measurements and
tank (calibration and target gas) measurements.
CO2 (above) and CH4 (below) mole fraction
differences between each minute and the last minute of the target gas
measurement period (30 min in this case) at the Amsterdam Island station
(identified by the three-letter code AMS). The differences are averaged for all
target gas measurements from 6 August to 6 November 2014. The number of injections
or samplings during this period is provided for each of the four target gases
on the right. The minutes provided on the right of the graph for each gas
correspond to the minute when the difference decreases below the horizontal
dashed lines chosen as half the WMO-recommended compatibility for northern
hemispheric sites (±0.05 µmol mol-1 for CO2 and ±1 nmol mol-1 for CH4).
Average stabilization times (in minutes) estimated to have
a difference from the last minute of the target gas measurement of less than
±0.05 µmol mol-1 for CO2 (in red) and
±1 nmol mol-1 for CH4 (in blue). The time is calculated for
several instruments indicated on the x axis; the left side of the figure
shows short-term target gas measurements, whereas the right side shows the
long-term target measurements, which are less frequent.
An example of the stabilization of CO2 and CH4 mole fractions is
provided in Fig. 3, showing a synthesis of the calibration gas measurements
at the Amsterdam Island station (identified by the three-letter code AMS,
instrument no. 111). At this station, four calibration gases are analyzed four times for 30 min
every 30 days. The CO2 and CH4 mole
fractions are averaged every minute, and we calculate the differences with
the last minute of each target injection. On average, stabilization
(±0.05 µmol mol-1 for CO2 and
±1 nmol mol-1 for CH4) is reached after 2 to 4 min. When
looking at measurements of short-term and long-term target gases from several
sites (Fig. 4), one can see that stabilization is very often reached within
4–6 min, but more time may be needed for the long-term target. The
difference can be explained by the fact that the long-term target is used
only once a month, and the associated pressure regulator and lines must be
flushed for a little while before being stabilized.
Corrections of the CO2 and CH4 mole fractions
The second step of the processing consists of correcting the data (Fig. 2)
for several artifacts. Corrections are applied only to the raw data that have
been flagged as valid during the first step (see Sect. 3). This step is
common to all types of gas (ambient, target, calibration), but the list of
applicable corrections differs. There can be 0 to n correction(s), where
the order in which they are applied is important. For each type of
correction, there is a correction function defined, and the parameterization
of this function is dependent on the instrument, location, species and type of gas.
The values of all the intermediate corrections are stored for traceability
but if a filter applied on a intermediate corrected value fails, raw data are
flagged as invalid and will not be used to compute the associated aggregated
values.
For CO2 and CH4 measurements, all types of samples (ambient air,
target and calibration) are corrected for humidity effects, and the
calibration gases are not corrected by the calibration equation.
Water vapor correction
To achieve the WMO/GAW compatibility goals for observations of CO2 and
CH4 mole fractions in dry air, it is required when using gas
chromatography or nondispersive infrared spectroscopy to dry the air sample
prior to analysis to a dew point of no more than -50∘C (WMO,
2012). The emergence of new instruments using infrared absorption at specific
spectral lines selected to minimize the interference between
CO2/CH4 and water vapor has enabled precise measurements in humid
air. This technology, including CRDS or cavity-enhanced absorption spectroscopy, has been evaluated in both laboratory and
field conditions by several research groups (Chen et al., 2010; Rella et al.,
2013). Those studies have demonstrated that it is possible to precisely
correct the effects of water vapor dilution and pressure broadening for
CO2 and CH4. An empirical quadratic correction has been established
by Chen et al. (2010) for CRDS Picarro analyzers and confirmed by other
laboratory experiments. All the Picarro CO2/CH4 analyzers use the same
manufacturer's built-in correction coefficients, defined by Chen et
al. (2010), as described by Rella et al. (2013):
CO2dry=CO2wet1-0.012×H-2.674×10-4×H2,CH4dry=CH4wet1-0.00982×H-2.393×10-4×H2,
where CO2wet and CH4wet are the mole fractions
measured in wet air, H the reported H2O mole fraction and
CO2dry and CH4dry the mole fractions in dry air.
However, this generic manufacturer's water correction does not provide the
optimum result as the pressure broadening effect induced by water vapor is
specific to each instrument. In order to improve the water correction, the
ICOS strategy is not to use the dry air mole fractions reported by the
Picarro but to use the mole fractions measured without water vapor correction
then apply a post-processing water correction with specific coefficients for
each instrument. The determination of specific coefficients for one
instrument requires laboratory experiments to be performed as described by
Rella et al. (2013). Such experiments are now performed systematically for
each ICOS instrument at the ATC ICOS Metrology Laboratory. A technical paper
describing these tests and associated results is in preparation. In the ICOS
data processing, the water vapor correction is applied in the same way to all
analyzed samples (calibration and target gases, ambient air).
Figure 5 shows a comparison of the water corrections applied to CO2 and
CH4 measurements on two instruments running in parallel at the Mace Head
station. One instrument (G1301 model, no. 41) is directly measuring the wet
air, whereas for the other one (G2301 model, no. 54) the air is preliminary
dried with a cryogenic dryer using a “cold trap” immersed in an ethanol
bath cooled at -50∘C. The H2O measurements decrease from
approximately 1 % (wet air) to less than 0.01 % (dry air). The mean
water vapor corrections applied in February 2014 for the instrument measuring
the ambient air without any drying are 4.6±0.7µmol mol-1 and 17.8±2.8 nmol mol-1,
respectively, for CO2 and CH4 (Fig. 6). The same corrections
applied to the instrument measuring dry air are 0.04±0.01µmol mol-1 and 0.16±0.05 nmol mol-1,
respectively, for CO2 and CH4. Overall, over the 15-day period
shown in Fig. 6, the differences between the dry mole fractions measured by
the two instruments (no. 41 minus no. 54) at the Mace Head station are
+0.015±0.03µmol mol-1 and -0.41±0.3 nmol mol-1, respectively, for CO2 and CH4.
CO2 (above), CH4 (middle) and H2O
(below) mole fractions observed at Mace Head in February 2014 with two CRDS
analyzers. Left: analyzer Picarro model G1301 (no. 41) measuring wet air.
Right: analyzer Picarro model G2301 (no. 54) measuring dry air. For CO2
and CH4 plots, the blue dashed lines correspond to the raw data, the
gray lines correspond to the raw data corrected for water vapor and the
thick black line corresponds to the calibrated mole fractions in dry air.
Water vapor corrections (dashed lines) and WMO
calibration corrections (thick lines) applied to CO2 (red) and CH4
(blue) mole fractions for two CRDS analyzers used at Mace Head station
(above: no. 41 measuring wet air; below: no. 54 measuring dry air).
Synthesis of the water vapor (above) and calibration
(below) corrections applied to 11 instruments in 2014 for hourly mean
CO2 (left) and CH4 (right) mole fractions. The length of the box
represents the interquartile range, the horizontal line represents the
median and the low and high whiskers show 10 and 90 % percentiles,
respectively. Numbers below the box plots give the maximum and minimum
corrections. It should be noted that the calibration corrections depend on
the calibration settings of the analyzers.
We have made the same calculations for the differences between the CO2
and CH4 mole fractions before and after the water correction for 11
instruments used at monitoring stations in 2014. Statistics of the
comparisons of hourly means over the year are summarized in Fig. 7. The water
vapor corrections shown in Fig. 7 correspond to the difference between data
with and without the H2O correction (amount of water vapor correction) and
not to a measurement or correction bias. These corrections are needed to
convert humid air mole fractions in dry air mole fractions. However, any
error in the water vapor correction would introduce a bias in the resulting
dry air mole fractions, whose amplitude would depend on the H2O
concentration. The determination of a specific correction for each instrument
by the ATC will minimize the bias associated with humid air measurements.
Conversely, drying the air (e.g., using a Nafion membrane) may also cause a
measurement bias by contamination of the sampled air. The evaluation of these
biases is underway at the ATC and will be published separately. Several
instruments are operated with a drier system, and the water vapor corrections
are consequently close to zero, as shown for the Mace Head station (for
instrument no. 54). The instruments operated at Amsterdam Island,
Biscarrosse, Lamto, the Observatoire Pérenne de l'Environnement and Puy
de Dôme were measuring dry air, whereas the Trainou instrument was
successively operated in the two configurations (wet and dry) in 2014. For
the other instruments, the water vapor corrections range for annual averages
from 4 to 12 µmol mol-1 for CO2 and from 18 to
40 nmol mol-1 for CH4, depending on the mean water vapor content.
For example, the lowest corrections are observed at the Pic du Midi station
(identified by the three-letter code PDM), which is a high-altitude station
(2877 m) with drier air compared to low-elevation stations. The statistics
of the Trainou station (identified by the three-letter code TRN) instrument
no. 108 are intermediate between the dry and wet instruments because this
instrument was operated in both situations in 2014.
Calibration correction
All CO2 and CH4 measurements that are intended to be added to the
international monitoring networks database must be calibrated relatively to
the WMO mole fraction scale for gas mole fractions in dry air maintained by
WMO/GAW Central Calibration Laboratories (CCL). The current scales used for
CO2 and CH4 are “WMO CO2 X2007”
(http://www.esrl.noaa.gov/gmd/ccl/co2_scale.html) and “WMO CH4
X2004” (http://www.esrl.noaa.gov/gmd/ccl/ch4_scale.html). Updates of
the WMO scales will be taken into account by the CAL and the time series will
recalculated by the ATC. As explained previously (see Sect. 3.1), the station
PIs are in charge of the configuration of the calibrations performed at their
site (number of calibration tanks, frequency of calibrations and duration of
the gas injections).
A calibration episode is called a “calibration sequence”. When n working
standards (calibration tanks) are measured in a row, the succession of tanks
in a defined order is called a cycle. During a calibration episode, the cycle
is repeated several times, and the calibration sequence is defined as m times
the repetition of the unitary cycle element (Fig. 8).
For each tank and each cycle, 1 min mole fraction means are calculated, and
the injection mean is derived from the average of all minute means over the
entire sampling period (excluding the stabilization period). For each tank,
the mole fraction means are then averaged over all m cycles. These values are
plotted against the tank's standard concentration attributed by the
calibration laboratory, and the calibration equation is determined by linear
least square fitting.
Because the calibration correction is essential for the final in situ or
target data value determination, the calibration data are filtered through a
set of specific controls to determine whether all expected data are present
and the quality is sufficient for use in the computation of the calibration
equation (see below). All controls made on the calibration sequences are
instrument, location and species dependent. If there are enough valid data,
the calibration is accepted and the calibration equation is determined. The
equation coefficients are stored in the database, making them available for
the calibration of the other types of samples (ambient air and target
gases).
Details of a CO2 calibration performed at Mace Head
station (instrument no. 41) on 10 December 2014. (a) Raw CO2 data
measured for four calibration tanks analyzed four times in 20 min. Gray points
show the rejected values during the stabilization period (i.e., flushing
period). Values indicated on the right give the tank ID and their attributed
mole fractions on a WMOx2007 scale. (b) Same as (a) for CO2 mole fraction
differences between measured values and attributed WMO values. (c) Same as (b)
for 1 min averages. Gray crosses show rejected values due to a standard
deviation higher that the threshold value (vertical bar on the left).
(d) Cycle (squares) and calibration sequence averages (dashed lines and values on
the right). The first cycle is rejected as a stabilization period.
Linear fit of the CO2 calibration detailed in Fig. 8.
Coefficients a and b of the fit are shown in bold characters. The lower
plot shows CO2 residuals from the linear regression.
CO2 (above) and CH4 (below) mole fraction
differences between the validated and the near-real-time values at 11
stations in 2014 (left), and at three stations (Finokalia – FKL; Lamto –
LTO; Puy de Dôme – PUY) in June 2014 (right). Most of the
differences correspond to the drift between two calibrations, which cannot
be considered in real time. Each point corresponds to an hourly average.
The controls applied to the calibration data are currently the following:
The expected number of cycles with their associated number of calibration
standards is checked along with the minimum duration of the tank injection.
If the calibration data do not correspond to the defined calibration
sequence, the calibration is not taken into account.
The standard deviation of mole fraction 1 min means must be below a
specified threshold.
The standard deviation of mole fraction injection means must be below a
specified threshold.
A stabilization period given in terms of numbers of cycles can be
applied.
The number of valid calibration injections (or cycle means) for each
working standard, after applying the cycle stabilization, if any, must be
equal to or greater than a minimum.
The number of valid working standard mole fraction means for the entire
calibration sequence to use for the computation of the calibration equation
must be equal to or greater than a minimum.
Statistics of the validated minus NRT differences of
hourly means CO2 (left) and CH4 (right) mole fractions. Each of
the 11 box-and-whisker plots describes the differences for monitoring
stations in 2014. The length of the box represents the interquartile range,
the horizontal line represents the median and the low and high whiskers
show the first and ninth deciles, respectively. The numbers below the
box plots give the maximum and minimum differences.
An example of calibration for instrument no. 41 at the Mace Head station on
10 December 2014 is shown in Figs. 8 and 9. The set of parameters defined
by the PI for this instrument are given in Tables 3 and 4. Four calibration
tanks are used and are analyzed four times (cycles) for 20 min in each
calibration, including 15 min dedicated to the flushing of the inlet lines
and analyzer cell (stabilization time). Overall, the calibration lasts for
320 min. Figure 8 shows the different steps of the calibration process from
analyzing the raw data and aggregating to the minute to calculating the cycle
and calibration sequence averages. A fitting function (see Fig. 9) is then
applied to the results of the calibration to define the coefficients of the
correction, which will be applied to in situ air and target gas measurements
to ensure the data are compatible with the WMO reference scales.
Similar to the analysis of the water vapor corrections, we have summarized
the calibration corrections applied at 11 instruments in 2014 (Fig. 10).
All stations are calibrated with standard gases, which are themselves
measured against the international WMO scales. The correction applied to the
raw data depends on the pre-set calibration parameters of the CRDS analyzers,
which correspond in this study to the factory settings. The mean CO2
correction applied to the 11 instruments is 1.7±0.3µmol mol-1, and its variability over a 1-year period,
expressed as the mean standard deviation, is 0.07 µmol mol-1.
Calibration corrections calculated for CH4 mole fractions have a mean of
2.8±3 nmol mol-1 over the 11 sites and a yearly standard
deviation of 0.7 nmol mol-1 on average. Even if the corrections are
quite homogeneous from instrument to instrument and over the course of a
year, these values demonstrate the need for regular calibrations with
standard references to comply with WMO objectives of compatibility goals.
The data are corrected with the closest calibration equation in time existing
before the data. As soon as there are calibration episodes before and after
the considered data, the correction is made with a linear interpolation of
the enclosing calibration equations. It is important to note that NRT data
provided after 24 h will be automatically modified after a few weeks once
the next calibration is available to estimate the temporal drift of the
analyzer. If no calibration equation is available within a period of 180 days
to correct the data, the data are flagged as incorrect, and the explanation
is added to the internal cumulative flag.
We have analyzed, for 11 monitoring stations, the differences in the
CO2 and CH4 mole fractions processed in near-real time with the
same dataset after calibration drift correction and manual validation by the
PI. A posteriori verification of the NRT dataset is important to qualify this
specific product, which is increasingly requested by users. Understanding the
reasons for differences between NRT and validated datasets will also help
improve the automatic processing of the measurements. Figure 10 shows the
differences for the hourly means. The most evident feature of the differences
for all sites is the linear drift correction between two calibration
sequences (≈ 2 to 4 weeks). At the Amsterdam Island station we see
a reverse slope for a short period (2 weeks) in early July 2014, with the
drift changing towards a smaller bias with time. This is due to a revision of
the calibration performed on 1 July, after the correction of an erroneous
injection of one calibration gas. In most cases (95 %), the differences
are within ±0.06 µmol mol-1 for CO2 and
±0.75 nmol mol-1 for CH4. The statistics of the validated
minus NRT mole fractions are shown for each site in Fig. 11. It is worth
noting that for most of the stations, the median differences are less than or
equal to zero. Only three instruments show a positive median difference for
CO2 (Lamto station – identified by the three-letter code LTO with
instrument no. 192; PDM, no. 222; Ivittuut station – identified by the
three-letter code IVI with instrument no. 93) and one for CH4 (IVI –
no. 93). This means that almost all instruments have a tendency to drift
positively; consequently, when a NRT dataset is revised after a few days or
weeks with the new calibration sequence, its value is slightly decreased.
This tendency for a positive drift for CH4 measurements by CRDS
analyzers was also noticed by Yver-Kowk et al. (2015).
Example of a file
provided to the MACC-II project in near-real time (24 h). The first block
represents the metadata of the station, and the second block contains hourly
means of CO2 mole fractions in dry air for 1 day (Observatoire
Pérenne de l'Environnement station, identified by the three-letter code OPE,
instrument no. 91).
In addition to the data corrections due to instrumental drift, we also detect
in Fig. 10 some isolated events that present a different profile of
variability, and there are also a few outliers. For example, not visible in
this figure (out of scale) is a 5-day period (10–15 July) at the Mace
Head station (no. 41) with very high differences between NRT and validated
mole fractions: up to -25µmol mol-1 for CO2 and
-250 nmol mol-1 for CH4. This event corresponds to the
installation of a new calibration scale at the Mace Head station, with
erroneous values of the standard gases entered into the database.
Consequently, the mole fractions calculated in NRT were wrong, and a few days
were required to identify the problem and reprocess the dataset. Another
example is the relatively constant differences observed at the Finokalia
station (identified by the three-letter code FKL) from 5 to 20 June 2014:
+0.09 µmol mol-1 and +1.4 nmol mol-1 for CO2
and CH4, respectively (Fig. 10, right). This event corresponds to an
error in the first calibration performed at the installation of the station.
The calibration episode was later rejected, and the subsequent calibration
was therefore the only one used to correct the raw values, as explained
previously. This issue may be difficult to detect immediately upon the start
of a monitoring site because we lack references for evaluation. The zoom into
June 2014 (Fig. 10, right) also shows small oscillations in the CO2
differences at the Lamto station. This feature is related to the strong
diurnal cycle observed at this tropical site (typically 50 ppm). Since the
correction applied to the data depends on concentration, the differences
between NRT and validated data also display a diurnal cycle . We also observe
for some periods a relatively high random variability of the mole fraction
differences for the Trainou station instrument (no. 108). This is due to the
leakage of one valve that is used to evacuate the liquid water from a water
trap setup inside a refrigerator. This problem caused contamination for a few
minutes. These contaminated values were used in the NRT data processing,
whereas they were excluded after the quality control of the measurements
performed by the station PI, which explains the differences between the two
datasets. This example shows the importance of the expert examination; it is
very hard to completely automatize the quality control and the PI may have
additional information at hand to help define the status of the data.
However, when invalidating data the PI has to provide codified reasons (the
list of such reasons, called “descriptive flag”, can be found in Table 2).
Data time aggregation and associated metadata
Further processing consists of aggregating the data in time. The 1 min,
hourly and daily means are computed for in situ data. The 1 min means
and injection means are computed for tank data (calibration and target
gases). As recommended by the World Data Centre for Greenhouse Gases (WDCGG;
WMO, 2012), we calculate the means using data from the nearest time
aggregation level and not always using the raw data. This implies that raw
data are used to calculate 1 min averages, which are then used to calculate
hourly averages and so on. For each single averaged data point, we provide
the number of data used to compute the average and the standard deviation.
The measurement time associated with an average dataset corresponds to the
beginning of the averaging period (e.g., the hourly means at 13:00 are
calculated from the 1 min means from 13:00 to 13:59), which is also in
line with the recommendation of WDCGG (WMO, 2012). The times provided to the
users are always universal time. The time difference between local time and
universal time is provided in the metadata of the station.
Different data output formats can be provided to fit user needs. The files
provided to the users always include the following information for each
average mole fraction in dry air: time/date of the measurement, site and
instrument identifiers, number of data and standard deviation, user flag and
an internal identifier tracing all processing parameters
(an example can be found in Table 7). In addition, the
header of the file provides metadata including the station coordinates, the
measurement calibration scale, the name of a contact person and the
institute in charge of the monitoring program. More information (raw data,
internal flags, etc.) is available upon request to the ATC data center.
Conclusion and perspectives
The provision of atmospheric GHG mole fractions in NRT is useful
for early detection of anomalies, whether they are instrumental or
geophysical, and data assimilation schemes. As part of the construction of
the ICOS ATC data center, we have developed a framework for fast delivery
(24 h) of the atmospheric greenhouse gases dataset. The setup of the
hardware and software needed for data collection, data processing,
configuration of measurements and quality control of the time series have
been performed over the past years in close collaboration with
experimentalists in charge of running stations during the demonstrator phase
of ICOS. In the last few years, we moved from a situation in which each
European station was performing its own data processing to the ICOS
configuration with a central database and a set of software codes processing
the raw data transferred from all ICOS sites daily. This configuration
ensures better inter-comparison of the data. By analyzing data from 11
sites, we determined that the average calibration corrections applied in the
data process by the ATC equals 1.7±0.3µmol mol-1 for
CO2 and 2.8±3 nmol mol-1 for CH4. These biases are
important to correct to avoid artificial gradients between stations that
could lead to error in flux estimates when using atmospheric inversion
techniques. Masarie et al. (2011) showed that a 1 µmol mol-1
bias at a measurement tower in Wisconsin induced a response in terms of
fluxes of 68 TgC year-1 when using the carbon tracker inversion system
(Peters et al., 2007). This flux represents approximately 10 % of the
estimated North American annual terrestrial uptake.
We have also evaluated that the average drift between two calibrations
separated by 15 days amounts to ±0.05 µmol mol-1 and
±0.7 nmol mol-1 for CO2 and CH4, respectively. Outliers
may occur, which are generally associated with an error in the metadata
information provided by the station PI (e.g., error in the attributed value
of the calibration gas).
ICOS aims to maintain very high-precision measurements with a high level of
data recovery, traceability and fast delivery. Rapid access to processed
data and their associated metadata, as well as a catalogue of data products
updated daily, is intended to facilitate the verification of the
measurements. In 2013, 17.8 GB of data files and data products were viewed
by users on the ICOS ATC website (https://icos-atc.lsce.ipsl.fr), which
corresponds to more than 17 000 hits and more than 380 000 pages viewed.
Traceability of the downloads, long-term archival and data policies beyond
the scope of this paper are being designed in collaboration with the carbon
portal of ICOS.
Thus far, the NRT dataset has been provided to the participants of the ICOS
Preparatory Phase and the following projects: InGOS
(http://ingos-atm.lsce.ipsl.fr/), ICOS-INWIRE
(http://www.icos-inwire.lsce.ipsl.fr/) and MACC-III/COPERNICUS
(http://www.copernicus-atmosphere.eu/d/summary/macc/gac/verif/ghg/icos/).
The format of the files provided to the users was adapted to their needs, and
the identifier which allows for the traceability of the measurements is part
of the compulsory information. The MACC-III project is using the CO2
data in NRT time to evaluate their assimilation and forecasting system
developed at the European Centre for Medium-range Weather Forecasts
(Agustí-Panareda et al., 2014). In another study, the authors performed
a CH4 inversion to test the ability of the European network of
atmospheric observations to detect the leakage of an offshore oil platform at
Elgin Field, North Sea (Berchet et al., 2013).
The continuous enhancement of automatic processing is important, and new
developments are in progress. This includes the evaluation of spike
detection algorithms that would allow the automatic identification of data
being significantly influenced by local processes. Another perspective is to
interface the database with the electronic logbooks of the station
operations (maintenance, troubleshooting, etc.), as a support of the quality
control of the time series. One important issue is the estimation of
time-varying uncertainties based on regular measurements of the target
gases, comparison of in situ and flask measurements and analysis of
specific tests. Evaluation of algorithms to estimate random and systematic
errors was performed by the INGOS and ICOS-INWIRE European projects, and we
have started to transfer some of them into the ICOS data processing. Within
the ICOS project research actions are ongoing for a better assessment of the
calibration strategy and the water vapor correction, and their associated
uncertainties. The outputs of these studies will be implemented later in the
data processing to improve the current data corrections and uncertainties
estimates.
Data availability
The NRT data of the ICOS atmospheric stations processed as described in the paper
will be freely accessible from the ATC website in the near future.
Raw data are available upon request to the authors.
Acknowledgements
The authors acknowledge the scientists and engineers from the stations
contributing to the development of the ICOS data center. Their regular
feedback and comments are essentials for the success of the project. We wish
to acknowledge the valuable comments of M. Steinbacher, H. Chen, L. Rivier,
J. D. Paris, M. Delmotte and N. Schneider as well as the entire staff of the ICOS
Atmospheric Data Center. This study was funded in part by the European
Commission under the EU Seventh Research Framework Programme through ICOS
(grant agreement no. 211574) and ICOS-INWIRE (grant agreement
no. 313169). Edited by:
D. Brunner Reviewed by: J. Klausen and one anonymous referee
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