Auxiliary Data

This page describes the auxiliary information required to compare TCCON data to other data or model output are embedded in the netCDF files for GGG2014. For (obsolete) GGG2012, the auxiliary data are posted on the TCCON 2012 archive, and for the (obsolete) GGG2009 data, the auxiliary data are posted on the TCCON 2009 archive. 

Comparing TCCON data with profile measurements or models

Note: this is a work-in-progress!

In the GGG2014 netcdf files, one site and time specific a priori profile per day is included. These profiles are indexed (note that the index begins at 0!) to make it easy to link the a priori profile to a particular measurement. The TCCON column averaging kernels vary smoothly with pressure and solar zenith angle, but they do not differ significantly between sites or at different times of year. Therefore, we provide a standard set of TCCON column averaging kernels on a pressure grid and at a set of solar zenith angles. To obtain the column averaging kernel for a particular measurement, you should interpolate the column averaging kernel to the solar zenith angle of the measurement, and terminate the profile at the surface pressure of the measurement. For SZA<10 degrees (the minimum provided), use the SZA=10 column averaging kernel. For SZA>85 degrees (the maximum in the file), use the SZA=85 column averaging kernel.

In order to compare our data with, say, model or high-resolution aircraft profile data, you need to use our column averaging kernels and a priori profiles. This information is also covered in Wunch et al. (2010) and is based on Rodgers and Connor (2003). The main equation is:

where is the quantity of interest: the smoothed column DMF (Dry-air Mole Fraction: a scalar), is the TCCON a priori column DMF (a scalar), describes the vertical summation (a vector), is the TCCON absorber-weighted column averaging kernel (a vector), is the DMF "truth" (either the model profile or the aircraft profile) and is the TCCON a priori profile (vector). There is one a priori profile per local day of measurements (many TCCON sites measure over two UTC days per local day because of their time zones). In order to compute the smoothed columns, you need to know the pressure weighting function, which is the ratio of the vertical column of the gas in each layer  () to the vertical column of dry air ( in molecules/cm^2): 

where is the dry mole fraction of the gas of interest ( or from the first equation)

where is defined by convention:

and the and are in kg/molecule, requiring Avogadro's constant to convert the molar weights. g is the gravitational acceleration and i is the atmospheric layer.

This makes the first equation become:

The current version of our .map files contain g and all the constants necessary to compute the equations above, but you can also find relationships for g as a function of altitude and latitude from, e.g., http://en.wikipedia.org/wiki/Gravity_of_Earth.

 

IMPORTANT!

  • The TCCON apriori is treated as if it is a wet mole fraction. For inclusion in your model/profile-measurement intercomparison with TCCON data, it must therefore be converted to a dry-air mole fraction, via:
  • The TCCON values given are level values, NOT layer means. This means they are defined for exactly the altitude given, not an average for the layer above/below/centred at that altitude. When integrating over layers some interpolation/averaging is therefore necessary.
    • This means that you are given the TCCON aprioris and averaging kernels on a 71 level grid, and must interpolate or average to an appropriate value for the layer means. The can be derived by taking the difference in pressure at the boundaries (levels) surrounding the layer in question.
  • The contribution of H2O to the equations above pertaining to the integration CANNOT be ignored. We recommend using your model's own output of H2O for this, but failing that one can use the TCCON apriori. The difference induced by different H2O formulations is small, but ignoring H2O can cause a significant effect.

 

Sensitivity to assumptions

In order to test the effect of neglecting some of the aspects mentioned above, and highlight there importance, a number of sensitivity studies have been undertaken. These are done using CT2011 simulations, provided as "column output" at 90 minute intervals at TCCON locations. The column output from CT2011 is available from ftp://aftp.cmdl.noaa.gov/products/carbontracker/co2/column

In these studies, we generate one CT "smoothed" point for each FTS measurement, thereby temporally interpolating between the spanning CT model outputs. This has a couple of advantages:

1. We can then use the solar zenith angle of the measurement to interpolate from the generic averaging kernels to provide the averaging kernel for smoothing, along with the daily TCCON apriori profile.

2. Because the TCCON data are not temporally uniform, this ensures that there are no biases between the model and data introduced by non-uniform sampling, even when averaging to daily/weekly/less-frequent periods.

Here we use 3 sites to illustrate the sensitivities - Darwin (wet, tropical), Lamont (mid-lat) and Spitsbergen (dry, polar).

 

  1. Incorrectly using the TCCON apriori as if it is a dry-air mole fraction.

 

As the figure below shows, this induces significant differences (on the order of the TCCON precision and accuracy), and could thereby compromise any comparison between TCCON measurements and models. The differences are noticeably smaller at the dry site, Spitsbergen.

 CT_wet_dry_ap_diffs.png

  1. Incorrectly treating the as wet

 

By the formulation above, when integrating it is strictly necessary to consider the H2O correction as being the dry-air mole fraction of H2O (by definition, by considering it as a mixing ratio rather than a mole fraction). This makes a small difference (<< 0.1ppm) to the smoothed xCO2.

 

CT_wet_dry_h2o_diffs.png

 

  1. Incorrectly treating the values as layer means rather than level values.

 

TCCON averaging kernels, aprioris and other values are provided at exact levels, not averaged across an altitude range. Here we have compared the formulation integrating across every layer using the mean of the level boundaries of the layer with assuming that the value at the lower altitude (i.e. higher pressure) is representative of the entire layer above. The differences are bordering on significant, at approximately 0.1 ppm.

 

CT_layers_levels_diffs.png 

 

  1. Incorrectly ignoring the apriori contribution to the smoothing equation

 

This is a common mistake, and critical. The effect of convolving the profile with the averaging kernel can be large, however it is mostly cancelled out by the effect of also convolving the apriori profile with the kernel. Ignoring the apriori therefore leads to large errors, sometimes larger than 1% depending on the zenith angle of the measurement (because of the zenith angle dependence of the averaging kernels).

 

CT_noapriori_diffs.png

 

 

  1. Incorrectly ignoring the in the integration to column values

 

Because the integration deals with dry-air mole fractions, but an integration over pressure is effectively with respect to wet-air (i.e. the pressure includes the contribution of H2O), this dilution due to H2O must be taken into account. This differences from ignoring this are generally small because of cancellation between the VC(gas) and the VC(air), but can exceed 0.1 ppm.

 

CT_noh2o_diffs.png

 

Note: These things are to be added:

  • The finer the vertical grid the better... but cannot go finer than the lowest resolution grid
    • check this (TCCON vs model grid differences)
  • time-resolution of H2O profiles (NCEP 1x vs 4x daily, e.g.)
  • How to treat smoothing of tracer components (e.g. fossil fuel, biosphere, ocean, fires in CT)?
    • subtract components from total and assess variability? fractional variability (of total)?
  • Treatment of SZAs (for AK interpolation)?

 

A Priori Profiles and Column Averaging Kernels for the (obsolete) GGG2009 Data

Site Averaging Kernels A Priori Profiles
Lamont, Oklahoma lamont_averaging_kernels_co2.dat
lamont_averaging_kernels_ch4.dat lamont_averaging_kernels_co.dat
lamont_averaging_kernels_n2o.dat
lamont_averaging_kernels_h2o.dat
lamont_apriori_profiles_20080706_20110705.tgz  
Park Falls, Wisconsin

parkfalls_averaging_kernels_co2.dat parkfalls_averaging_kernels_ch4.dat
parkfalls_averaging_kernels_co.dat
parkfalls_averaging_kernels_n2o.dat
parkfalls_averaging_kernels_h2o.dat

parkfalls_apriori_profiles_20040526_20110501.tgz 
JPL, California (the instrument is now in Lamont, OK) Please use the Lamont averaging kernels jpl_apriori_profiles.tgz
Lauder, New Zealand  Please use the Lamont averaging kernels

lauder_120hr_apriori_profiles_2004_2010.tgz 

lauder_125hr_apriori_profiles_2010.tgz 

Darwin, Australia Please use the Lamont averaging kernels. If necessary extrapolate to lower SZA. AKs binned into 5 degree SZA averages are also available on request.  db_apriori.tar.gz
Wollongong, Australia Please use the Lamont averaging kernels. AKs binned into 5 degree SZA averages are also available on request.  wg_apriori.tar.gz 
Bremen, Germany Please use the Lamont averaging kernels.  br_aprioris.tar.gz
Bialystok, Poland Please use the Lamont averaging kernels.  bi_aprioris.tar.gz 
Orleans, France Please use the Lamont averaging kernels.  or_aprioris.tar.gz 
Tsukuba, Japan  Please use the Lamont averaging kernels.

tj(120HR)_apriori_20081225_20100322.zip

tk(125HR)_20110804_20140131.apriori.tgz 

Sodankyla, Finland Please use the Lamont averaging kernels. so_apriori.tar.gz 
Izana, Spain     iz_aprioris.tar.gz 
Karlsruhe, Germany  Please use the Lamont averaging kernels.

 ka_aprioris.tar.gz 

Eureka, Canada   Eureka_apriori_profiles_20100724_20100924.zip
Garmisch, Germany Please use the Lamont averaging kernels. gm_aprioris.tgz

 

This site has been viewed 19881 times.

Tag page

Files 29

FileSizeDateAttached by 
 bi_aprioris.tar.gz
Bialystok aprioris
456.42 kB07:22, 29 Jul 2011nmd03Actions
 br_aprioris.tar.gz
Bremen aprioris
513.73 kB07:22, 29 Jul 2011nmd03Actions
 db_apriori.tar.gz
Darwin aprioris
1546.52 kB07:23, 29 Jul 2011nmd03Actions
 Eureka_apriori_profiles_20100724_20100924.zip
Eureka aprioris
57.12 kB13:05, 20 Nov 2011mendoncaActions
 gm_aprioris.tgz
Garmisch aprioris
675.63 kB06:07, 25 Nov 2011markus.rettingerActions
 iz_aprioris.tar.gz
Izana aprioris
243.59 kB02:25, 16 Nov 2011doheActions
 jpl_apriori_profiles.tgz
JPL a priori profiles
366.41 kB15:19, 20 Apr 2011dwunchActions
 ka_aprioris.tar.gz
Karlsruhe aprioris
185.06 kB02:25, 16 Nov 2011doheActions
 lamont_apriori_profiles.tgz
Lamont a priori profiles
971.39 kB15:19, 20 Apr 2011dwunchActions
 lamont_apriori_profiles_20080706_20110705.tgz
Lamont a priori profiles 20080706 - 20110705
1172.26 kB08:20, 18 Jul 2011dwunchActions
 lamont_averaging_kernels_ch4.dat
Lamont CH4 column averaging kernels
9.51 kB15:19, 20 Apr 2011dwunchActions
 lamont_averaging_kernels_co.dat
Lamont CO column averaging kernels
9.51 kB15:28, 20 Apr 2011dwunchActions
 lamont_averaging_kernels_co2.dat
Lamont CO2 column averaging kernels
9.51 kB15:19, 20 Apr 2011dwunchActions
 lamont_averaging_kernels_h2o.dat
Lamont H2O column averaging kernels
9.51 kB15:34, 20 Apr 2011dwunchActions
 lamont_averaging_kernels_n2o.dat
Lamont N2O column averaging kernels
9.51 kB15:31, 20 Apr 2011dwunchActions
 lauder_120hr_apriori_profiles_2004_2010.tgz
No description
878.06 kB12:52, 27 Jul 2011v.sherlockActions
 lauder_125hr_apriori_profiles_2010.tgz
No description
77.22 kB12:42, 27 Jul 2011v.sherlockActions
 or_aprioris.tar.gz
Orleans aprioris
213.74 kB07:22, 29 Jul 2011nmd03Actions
 parkfalls_apriori_profiles.tgz
Park Falls a priori profiles
1654.12 kB15:19, 20 Apr 2011dwunchActions
 parkfalls_apriori_profiles_20040526_20110501.tgz
Park Falls a priori profiles 20040526 - 20110501
1700.07 kB08:20, 18 Jul 2011dwunchActions
 parkfalls_averaging_kernels_ch4.dat
Park Falls CH4 column averaging kernels
8.88 kB15:19, 20 Apr 2011dwunchActions
 parkfalls_averaging_kernels_co.dat
Park Falls CO column averaging kernels
8.88 kB08:35, 18 Jul 2011dwunchActions
 parkfalls_averaging_kernels_co2.dat
Park Falls CO2 column averaging kernels
8.88 kB15:19, 20 Apr 2011dwunchActions
 parkfalls_averaging_kernels_h2o.dat
Park Falls H2O column averaging kernels
8.88 kB08:35, 18 Jul 2011dwunchActions
 parkfalls_averaging_kernels_n2o.dat
Park Falls N2O column averaging kernels
8.88 kB08:35, 18 Jul 2011dwunchActions
 so_apriori.tar.gz
Sodankyla a priori profiles
213.35 kB01:01, 15 Nov 2011kyroActions
 tj(120HR)_apriori_20081225_20100322.zip
No description
483.19 kB04:11, 3 Feb 2012morinoActions
 tk(125HR)_20110804_20140131.apriori.tgz
No description
790.95 kB21:18, 23 Mar 2014morinoActions
 wg_apriori.tar.gz
Wollongong aprioris
558.39 kB07:23, 29 Jul 2011nmd03Actions