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: \(y= \frac{y^0+\sum_{i=1}^{nlevsat}ak_i(y_{dry,i}^s.dryair_i-y^0_i)}{dryair_{tot}}\)
8: Formula 8: \(y= \sum_{i=1}^{nlevsat} \left( ak_i(y_i^s - y^0_i) \right) + y^0_{chosenlev}\)
10: Formula 10: \(y= \sum_{i=1}\{ [y^0_i +\ ( y^s_i- y^0_i \) ak_i\ ] pwgt\}\)
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: float
Optional arguments¶
parameter: (optional)
Parameter name on which the transform works on
accepted type: str
component: (optional)
Component name on which the transform works on
accepted type: str
orig_parameter_plg: (optional)
Plugin object on which the transform works on
accepted type: Plugin
orig_component_plg: (optional)
Corresponding component object on which the transform works on
accepted type: Plugin
successor: (optional)
Name of the successor transform
accepted type: str
precursor: (optional)
Name of the precursor transform
accepted type: str
ignore_formula: (optional): False
Ignore the given formula number and directly use the individual options (see below):
log_space
precomputed_pwgt
use_prior
unit_scaling
normalize_columns
use_drycols
scale_dpressure
accepted type: bool
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: 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: bool
fill_strato: (optional): False
Filling stratosphere from a global model (temporary implemetation)
accepted type: 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: bool
chosenlev: (optional): -1
For formula type #3, level at which the equivalent of the observation are computed. Counting starts at 0.
accepted type: int
split_tropo_strato: (optional): False
Compute separately the contribution from the stratosphere and the troposphere for debugging purpose
accepted type: bool
unit_scaling: (optional): 1
Re-scale aks using a given scaling factor
accepted type: float
log_space: (optional): False
Apply averaging kernels in a log space
accepted type: bool
precomputed_pwgt: (optional): False
Use pre-computed pressure weights
accepted type: bool
use_prior: (optional): False
Use a prior profile
accepted type: bool
use_drycols: (optional): False
Use dry air columns to scale simulations
accepted type: bool
scale_dpressure: (optional): False
Scale simulated level by pressure thickness
accepted type: bool
normalize_columns: (optional): False
Normalize total columns according to aks and pressure weights, or drycols if use_drycols = True
accepted type: bool
level_based: (optional): False
Averaging kernel pressure levels are defined at the middle (True) of levels, hence with n values, or at the interface (‘False’), hence with n+1 values
accepted type: bool
force_dump_sim_aks: (optional): False
Force dump the full dataframe before applying aks for debugging purposes
accepted type: 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 # 1|2|3|4|5|8|10
9 molmass: XXXXX # float
10
11 # Optional arguments
12 parameter: XXXXX # str
13 component: XXXXX # str
14 orig_parameter_plg: XXXXX # Plugin
15 orig_component_plg: XXXXX # Plugin
16 successor: XXXXX # str
17 precursor: XXXXX # str
18 ignore_formula: XXXXX # bool
19 product: XXXXX # level|column
20 pressure: XXXXX # hPa|Pa
21 nchunks: XXXXX # int
22 cropstrato: XXXXX # bool
23 fill_strato: XXXXX # int
24 correct_pthick: XXXXX # bool
25 chosenlev: XXXXX # int
26 split_tropo_strato: XXXXX # bool
27 unit_scaling: XXXXX # float
28 log_space: XXXXX # bool
29 precomputed_pwgt: XXXXX # bool
30 use_prior: XXXXX # bool
31 use_drycols: XXXXX # bool
32 scale_dpressure: XXXXX # bool
33 normalize_columns: XXXXX # bool
34 level_based: XXXXX # bool
35 force_dump_sim_aks: XXXXX # bool
