Auxiliary Data

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Auxiliary information required to compare TCCON data to other data or model output will be posted here for the GGG2009 data, and elsewhere for GGG2012.

Comparing TCCON data with profile measurements or models

Note: this is a work-in-progress!

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 prioris. This information is also covered in Wunch et al. (2010) and is based on Rodgers and Connor (2003).

where is the quantity of interest: the smoothed column DMF (a scalar), is the TCCON a priori column (a scalar), is like a pressure weighting function (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). 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 (): 

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:

A future version of our .map files will contain g and all the constants necessary to compute the equations above, but you can find relationships for g as a function of altitude and latitude from, e.g.,



  • 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.
  • 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 is 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 "special output" at 90 minute intervals at TCCON locations. In these studies, we generate one CT "smoothed" point for each FTS measurement, thereby temporally interpolating between the spanning CT model outputs. We 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. Here we use 3 sites to illustrate the sensitivities - Darwin (wet), Lamont (mid-lat) and Spitsbergen (dry, polar).


  1. Using the TCCON apriori as if it is already 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.


  1. Treating the H2O mole fraction as dry or 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).


Note: These things are to be added:

  • TCCON apriori as wet mole fraction
  • sensitivity to various assumptions
  • whether CO2, H2O, AK are layer means or level values (check FAQ!!) - GFIT values are level values. Models may or may not be layer means - this might depend on the model!
  • 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)?


  • Recommendations:
    • Models to use their own H2O for each model output
      • If not, then can use TCCON local noon (apriori).

A Priori Profiles and Column Averaging Kernels for the 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
Park Falls, Wisconsin

parkfalls_averaging_kernels_co2.dat parkfalls_averaging_kernels_ch4.dat

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



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.



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.


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


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