# Processing of satellite observations (`satellites`

/ `std`

)¶

## Description¶

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 keywords in the yml file.
In the `datavect`

paragraph, one should format a `satellites`

paragraph as
follows:

```
datavect:
components:
satellites:
parameters:
NO2:
chosenlev: 0
correct_pthick: false
cropstrato: true
dir: /tmp/PYCIF_DATA_TEST/CHIMERE/ACADOK
extend_surf: false
file: 'monitor_OMIQA4ECV_NO2_ACADOK.%Y%m%d.9H.nc'
fill_strato: true
formula: 2
molmass: 10
nchunks: 2
pressure: Pa
stratosphere:
parameters:
NO2:
dir: '/tmp/PYCIF_DATA_TEST//RAW//ECMWF/europe160/%Y/%m/'
file: 'macc.160europe.%Y%m%d0000%H.grb2'
plugin:
name: ECMWF
type: fields
version: grib2
regrid:
method: bilinear
unit_conversion:
scale: 1810375000.0
varname: co2
```

The `stratosphere`

paragraph is optional and is required only with the option
`fill_strato`

.

Note

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. Here is the detail of each variable:

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

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 molec.cm-2.

**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’>

## Yaml template¶

Please find below a template for a Yaml configuration:

```
1transform:
2 plugin:
3 name: satellites
4 version: std
5 type: transform
6
7 # Mandatory arguments
8 formula: XXXXX
9 molmass: XXXXX
10
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
```