satellites / std


The present plugin manages satellite observations. This includes:

  • applying averaging kernel formulae

  • unfolding satellite levels to individual observations to extract proper levels from models

  • vertically interpolating simulated values to averaging kernels’ pressure levels

  • optionally, for domains not extending to the top of the atmosphere, fetching stratospheric concentrations from another dataset

The satellites transform is triggered according to key words in the yml file. In the datavect paragraph, one should format a satellites paragraph as follows:

          chosenlev: 0
          correct_pthick: false
          cropstrato: true
          extend_surf: false
          file: ''
          fill_strato: true
          formula: 2
          molmass: 10
          nchunks: 2
          pressure: Pa
          dir: '/tmp/PYCIF_DATA_TEST//RAW//ECMWF/europe160/%Y/%m/'
          file: 'macc.160europe.%Y%m%d0000%H.grb2'
            name: ECMWF
            type: fields
            version: grib2
            method: bilinear
            scale: 1810375000.0
          varname: co2

The stratosphere paragraph is optional and is required only with the option fill_strato.


The detailed expected satellite observation file format is explained here.

Yaml arguments

The following arguments are used to configure the plugin. pyCIF will return an exception at the initialization if mandatory arguments are not specified, or if any argument does not fit accepted values or type:

Mandatory arguments

formula: (mandatory)

Number of the formula to use to apply the averaging kernels. With \(nlevsat\) the number of levels of the satellite, \(y\) the equivalent of the satellite data, \(y^s\) the simulated concentrations interpolated on these levels, \(\Delta P_i\) the pressure thicknesses of these levels [remark: if the thicknesses are not provided directly, this implies that the pressures for the sides of the levels are provided, including the surface pressure for the bottom of the lowest level], \(y^0_i\) the prior concentrations on these levels (prior profile), \(ak_i\) the averaging kernels and if relevant, \(chosenlev\) the number of the level of the chosen partial column.

Available formulae are:

accepted values:

  • 1: Formula 1: \(y= \frac{\sum_{i=1}^{nlevsat}y^s_i \Delta P_i ak_i}{\sum_{i=1}^{nlevsat}\Delta P_i ak_i}\)

  • 2: Formula 2: \(y= \sum_{i=1}^{nlevsat}y^s_i ak_i\)

  • 3: Formula 3: \(y= 10^{\left(logy^0_{chosenlev}+\sum_{i=1}^{nlevsat}(logy^s_i-logy^0_i)ak_i\right)}\)

  • 4: Formula 4: \(y= \sum_{i=1}^{nlevsat}y^s_i ak_i 10^3\)

  • 5: Formula 5: \(y= \frac{y^0+\sum_{i=1}^{nlevsat}ak_i(y_{dry,i}^s.dryair_i-y^0_i)}{dryair_{tot}}\)

molmass: (mandatory)

If fill_strato is True and product is column, molar mass (in g) of the species whose field is read in the stratosphere files.

accepted type: <class ‘float’>

Optional arguments

product: (optional): level

Type of product

accepted values:

  • level: Levels in ppb

  • column: Total column. In that case, carry out a conversion from ppb to pas super clair: ou? en quelle unite doit-on donner la col?

pressure: (optional): hPa

Unit for the pressure levels

accepted values:

  • hPa: hectoPascals

  • Pa: Pascals

nchunks: (optional): 50

Number of chunks for the application of averaging kernels. Averaging kernels are applied by chunks and not observation by observation to accelerate computation. Chunks should not be too small, neither too large. As a rule of thumb, chunks of a few hundreds to one thousand observations are working fine. Smaller chunks loose the advantage of chunk-based computations, while too big chunks can overload your memory. For very low number of observations, 1-2 chunks are sufficient;For big datasets, one should test different number (a few tens is typically recommended)

accepted type: <class ‘int’>

cropstrato: (optional): False

Cropping stratospheric averaging kernels. All averaging kernels above the top of the model are excluded. Warning: for domain-limited domain, if cropstrato is False, the top-most value from the model is interpolated to the top of the atmosphere, potentially biasing results (not recommended)

accepted type: <class ‘bool’>

fill_strato: (optional): False

Filling stratosphere from a global model (temporary implemetation)

accepted type: <class ‘int’>

correct_pthick: (optional): False

Correct for the thickness of the column. Due to topography and surface pressure approximations and errors in the model, the total column of air is never exactly the same between observations and the model. For that reason, a correcting factor can be applied to scale the thickness of the model column to make it fit the observed column

accepted type: <class ‘bool’>

chosenlev: (optional): 0

For formula type #3, level at which the equivalent of the observation are computed. Counting starts at 0.

accepted type: <class ‘int’>

split_tropo_strato: (optional): False

Compute separately the contribution from the stratosphere and the troposphere for debugging purpose

accepted type: <class ‘bool’>


The current plugin requires the present plugins to run properly:

Requirement name

Requirement type

Explicit definition

Any valid

Default name

Default version







Yaml template

Please find below a template for a Yaml configuration:

 2  plugin:
 3    name: satellites
 4    version: std
 5    type: transform
 7  # Mandatory arguments
 8  formula: XXXXX
 9  molmass: XXXXX
11  # Optional arguments
12  product: XXXXX
13  pressure: XXXXX
14  nchunks: XXXXX
15  cropstrato: XXXXX
16  fill_strato: XXXXX
17  correct_pthick: XXXXX
18  chosenlev: XXXXX
19  split_tropo_strato: XXXXX