.. Label between '.. _' and ':' ; use :ref:`text <label>` for reference
.. _s5p-hcho-processing:

********************************
Sentinel-5p HCHO data processing
********************************

This chapter describes the tasks performed for processing Sentinel-5p HCHO data.


Product description
===================

The product guides can be found at:

* `Sentinel-5p / Products and Algoritms <https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-5p/products-algorithms>`_

  * ``L2__HCHO___``, ``PUM-HCHO`` Product User Manual

Features:

* The retrieval product is a column density (mol/m2), which will be treated by CSO as a profile
  with :math:`n_r=1` layers:

  .. math::
      \mathbf{y}_r

* The simulation of a retrieval product from a model state does not require an apriori profile,
  and is denoted with:

  .. math::
      \mathbf{y}_s ~=~ \mathbf{A}^{trop}\ \mathbf{H}\mathbf{x}
    
  where:

  * :math:`\mathbf{y}_s` is the simulated retrieval (mol/m2) defined on :math:`n_r=1` layers;
  * :math:`\mathbf{A}^{trop}` is the *tropospheric* averaging kernel matrix with shape :math:`(n_r,n_a)`;
    in this product, :math:`n_r=1` the number of *a priori* layers; see also the remarks below;
  * :math:`\mathbf{x}` is the atmospheric state, which consists of a 3D array of HCHO concentrations;
  * :math:`\mathbf{H}` extracts a simulated profile from the state using horizontal and vertical interpolation;
    the result should be defined on the :math:`n_a` *a priori* layers
    and have the units of the retrieval product (mol/m2).

  In case :math:`\mathbf{x}` is the true atmoshperic state, the retrieval error is quantified
  by the *retrieval error covariance* :math:`\mathbf{R}` (in this scalar product a variance):
  
  .. math::
      \mathbf{y}_s ~-~ \mathbf{A}^{trop}\ \mathbf{H}\mathbf{x}^{true}  ~\sim~ \mathcal{N}\left(\mathbf{o},\mathbf{R}^{trop}\right)
    
* The retrieval status and quality is indicated by the ``qa_value``. 
  The recommended minimum is 0.5, this excludes cloudy scenes and other problematic retrievals.


Downloading Sentinel-5p data
============================

Sentinel-5p data could be downloaded from the `Copernicus Open Access Hub <https://scihub.copernicus.eu/>`_;
see the :ref:`cso-scihub` module for a detailed description.

The :py:class:`cso_scihub.CSO_SciHub_Download` class is available to download data
from this server.
The jobtree configuration could look like::

    ! download task:
    cso.s5p.hcho.task.class    :  cso.CSO_SciHub_Download
    cso.s5p.hcho.task.args     :  '${PWD}/rc/cso-s5p-hcho.rc', rcbase='cso.s5p.hcho.download-s5phub'

See the class documentation for the general settings that define the download.

Data could be download for different processing streams:

* `Near real time (NRTI)`
* `Offline (OFFL)` : available within weeks after observations;
* `Reprocessing (RPRO)` : re-processing of all previously made observations.

For the `Reproecessing` stream the download query looks like::
 
    cso.s5p.hcho.download-s5phub.query    :   platformname:Sentinel-5     AND \
                                              producttype:L2__HCHO__    AND \
                                          processinglevel:L2            AND \
                                           processingmode:Reprocessing

The target directory for downloaded files is specified to have sub-directories per year and month::

    ! output archive, store in subdirs per month:
    cso.s5p.hcho.download-s5phub.output.dir   :  ${my.work}/Copernicus/S5P/RPRO/HCHO/%Y/%m

The first downloaded files are then::

    Copernicus/S5P/RPRO/HCHO/2018/06/S5P_RPRO_L2__HCHO___20180601T002022_20180601T020350_03272_01_010105_20190206T213821.nc
                                     S5P_RPRO_L2__HCHO___20180601T020152_20180601T034520_03273_01_010105_20190206T214248.nc

See the section on *File name convention* in the *Product User Manual* for the meaning of the fields.



.. Label between '.. _' and ':' ; use :ref:`text <label>` for reference
.. _s5p-hcho-convert:

Conversion to CSO format
========================

The '``cso.s5p.hcho.convert``' task creates netCDF files with selected pixels,
for example only those within some region or cloud free pixels.
The selection criteria are defined in the settings, and added
to the '``history``' attribute of the created files as reminder.

The work is done by the :py:class:`.CSO_S5p_Convert` class,
which is initialized using the settings file::

  ! task initialization:
  cso.s5p.hcho.convert.class     :  cso.CSO_S5p_Convert
  cso.s5p.hcho.convert.args      :  '${PWD}/rc/cso-s5p-hcho.rc', rcbase='cso.s5p.hcho.convert'
  
See the class documentation for the general configuration,
below some specific choices are described.
The example is based on the S5p HCHO file from which the header is available in:

* `doc/samples/S5P_RPRO_L2__HCHO___20180611T104207_20180611T122535_03420_01_010105_20190208T155143.txt <../../samples/S5P_RPRO_L2__HCHO___20180611T104207_20180611T122535_03420_01_010105_20190208T155143.txt>`_


Pixel selection
---------------
        
The :py:class:`.CSO_S5p_Convert` class calls the :py:meth:`.S5p_File.SelectPixels` method
to create a pixel selection mask for the input file.
The selection is done using one or more filters.
First provide a list of filter names::

  cso.s5p.hcho.convert.filters   :  lons lats valid quality sza error_ratio ground_pixel

Then provide for each filter the the input variable to be used for testing,
as a path name in the input file.
The next settings is the type of filter to be used, see the :py:meth:`.S5p_File.SelectPixels` for supported types,
and the other settings required by the type.
The following is an example of a selection on longitude::

  cso.s5p.hcho.convert.filter.lons.var                :  Geolocation Fields/Longitude
  cso.s5p.hcho.convert.filter.lons.type               :  minmax
  cso.s5p.hcho.convert.filter.lons.minmax             :  -30.0 45.0
  cso.s5p.hcho.convert.filter.lons.units              :  degrees_east

Extension to the product guide
------------------------------

It is mentioned in the PUM that the quality flag and assesment are incomplete:
Several additions quality filters were based on Vigouroux et al., from which we quote:

  Several diagnostic variables are provided together with the measurements. 
  Quality assurance (QA) values are defined to perform a quick selection of the observations. 
  QA > 0.5 filters out most observations presenting an error flag or a solar zenith angle (SZA)
  larger than 70\ :sup:`o`, a cloud radiance fraction larger than 0.6 at 340 nm, or an air mass 
  factor smaller than 0.1. The product Readme file reports that, in the current version, 
  the QA values are not always correctly set over snow and ice regions or above an SZA of 75\ :sub:`o`. 
  They also need tobe further checked over cloudy scenes. 
  In the forthcoming S5P version 2, QA values will be refined and will exclude data with a 
  surface albedo larger than 0.2 and a snow or ice warning as well as remaining SZAs larger than 
  75\ :sup:`o`.

.. figure:: figs/HCHO/Example_validation_hcho.png
   :scale: 85 %
   :align: center
   :alt: Validation results from Vigouroux et al., 2020, https://amt.copernicus.org/articles/13/3751/2020/amt-13-3751-2020.pdf
   

Variable specification
----------------------
    
The target file is created as an :py:class:`.CSO_S5p_File` object.
It's :py:meth:`AddSelection <.CSO_S5p_File.AddSelection>` method is called with the input object as argument,
and this will copy the selected pixels for variables specified in the settings.

The variable specification starts with a list with variable names to be 
created in the target file::

  cso.s5p.hcho.convert.output.vars   : longitude longitude_bounds \
                                       latitude latitude_bounds \
                                       track_longitude track_longitude_bounds \
                                       track_latitude  track_latitude_bounds \
                                       time \
                                       pressure  qa_value \
                                       vcd vcd_errvar \
                                       cloud_fraction \                           
                                       ground_pixel \
                                       cloud_pressure_crb \
                                       solar_zenith_angle \
                                       amf_troposphere \
                                       quality \
                                       kernel

For each variable settings should be specified that describe the shape of the variable
and how it should be filled from the input.
See the :py:meth:`AddSelection <.CSO_S5p_File.AddSelection>` description for all options,
here we show some examples.

The ``longitude`` and ``latitude`` variables are copied almost directly out of the source files,
the only change that is applied is the selection of pixels.
All original attributes are copied, except for the ``bound`` attribite since that would
give warnings from the CF-compliance checker::

  cso.s5p.hcho.convert.output.var.longitude.dims                   :   pixel
  cso.s5p.hcho.convert.output.var.longitude.from                   :   PRODUCT/longitude
  cso.s5p.hcho.convert.output.var.longitude.attrs                  :   { 'bounds' : None }

  cso.s5p.hcho.convert.output.var.latitude.dims                    :   pixel
  cso.s5p.hcho.convert.output.var.latitude.from                    :   PRODUCT/latitude
  cso.s5p.hcho.convert.output.var.latitude.attrs                   :   { 'bounds' : None }

Also the locations of the pixels in the original track are copied,
since these are useful when creating plots. These cannot be copied directly but require special processing::

  cso.s5p.hcho.convert.output.var.track_longitude.dims             :   track_scan track_pixel
  cso.s5p.hcho.convert.output.var.track_longitude.special          :   track_longitude
  cso.s5p.hcho.convert.output.var.track_longitude.from             :   PRODUCT/longitude
  cso.s5p.hcho.convert.output.var.track_longitude.attrs            :   { 'bounds' : None }

  cso.s5p.hcho.convert.output.var.track_latitude.dims              :   track_scan track_pixel
  cso.s5p.hcho.convert.output.var.track_latitude.special           :   track_latitude
  cso.s5p.hcho.convert.output.var.track_latitude.from              :   PRODUCT/latitude
  cso.s5p.hcho.convert.output.var.track_latitude.attrs             :   { 'bounds' : None }

The observattion times are constructed from time steps relative to a reference time;
this requires special processing too::

  cso.s5p.hcho.convert.output.var.time.dims                        :   pixel
  cso.s5p.hcho.convert.output.var.time.special                     :   time-delta
  cso.s5p.hcho.convert.output.var.time.tref                        :   PRODUCT/time
  cso.s5p.hcho.convert.output.var.time.dt                          :   PRODUCT/delta_time

The observed vertical column density could be copied directly.
The target shape is ``(pixel,retr)`` where ``retr`` is the number of layers in the retrieval product (1 in this case)::

  ! vertical column density:
  cso.s5p.hcho.convert.output.var.vcd.dims                         :   pixel retr
  cso.s5p.hcho.convert.output.var.vcd.from                         :   PRODUCT/formaldehyde_tropospheric_vertical_column

In the converted files, the retrieval error is always expressed as a (co)variance matrix,
to facilitate (future) conversion of profile products.
In this example, it is filled from the square of the error standard deviation::

  ! error variance in vertical column density (after application of kernel),
  ! fill with square sums of random and systematic errors
  ! use dims with different names to avoid that cf-checker complains:
  cso.s5p.hcho.convert.output.var.vcd_errvar.dims                  :   pixel retr retr0
  cso.s5p.hcho.convert.output.var.vcd_errvar.special               :   square_sum
  cso.s5p.hcho.convert.output.var.vcd_errvar.from                  :   PRODUCT/formaldehyde_tropospheric_vertical_column_precision
  cso.s5p.hcho.convert.output.var.vcd_errvar.from2                 :   PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/formaldehyde_tropospheric_vertical_column_kernel_trueness

  The averaging kernel is applied on atmospheric layers, defined by pressure levels.
  In this product the pressure levels are defined using hybride-sigma-pressure coordinates,
  and this requires special processing::

  ! Convert from hybride coefficient bounds in (2,nlev) aray to 3D half level pressure:
  cso.s5p.hcho.convert.output.var.pressure.dims                    :   pixel layeri
  cso.s5p.hcho.convert.output.var.pressure.special                 :   hybounds_to_pressure_hcho
  cso.s5p.hcho.convert.output.var.pressure.sp                      :   PRODUCT/SUPPORT_DATA/INPUT_DATA/surface_pressure
  cso.s5p.hcho.convert.output.var.pressure.hyab                    :   PRODUCT/SUPPORT_DATA/INPUT_DATA/tm5_constant_a
  cso.s5p.hcho.convert.output.var.pressure.hybb                    :   PRODUCT/SUPPORT_DATA/INPUT_DATA/tm5_constant_b
  cso.s5p.hcho.convert.output.var.pressure.units                   :   Pa

  Averaging kernels are converted to matrices with shape ``(layer,retr)``.

  ! description:
  cso.s5p.hcho.convert.output.var.kernel.dims                      :   pixel layer retr
  cso.s5p.hcho.convert.output.var.kernel.from                      :   PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/averaging_kernel
  Other variables can be copied directly::

  ! quality flag:
  cso.s5p.hcho.convert.output.var.qa_value.dims                   :   pixel
  cso.s5p.hcho.convert.output.var.qa_value.from                   :   PRODUCT/qa_value
  !~ skip some attributes, cf-checker complains ...
  cso.s5p.hcho.convert.output.var.qa_value.attrs                  :   { 'valid_min' : None, 'valid_max' : None }

  ! cloud property:
  cso.s5p.hcho.convert.output.var.cloud_fraction.from             :   PRODUCT/SUPPORT_DATA/INPUT_DATA/cloud_fraction_crb
  cso.s5p.hcho.convert.output.var.cloud_fraction.units            :   1
  cso.s5p.hcho.convert.output.var.cloud_fraction.dims         :   pixel

  cso.s5p.hcho.convert.output.var.solar_zenith_angle.from         :   PRODUCT/SUPPORT_DATA/GEOLOCATIONS/solar_zenith_angle
  cso.s5p.hcho.convert.output.var.solar_zenith_angle.units        :   degree
  cso.s5p.hcho.convert.output.var.solar_zenith_angle.dims         :   pixel
  
  cso.s5p.hcho.convert.output.var.cloud_pressure_crb.from         :   PRODUCT/SUPPORT_DATA/INPUT_DATA/cloud_pressure_crb
  cso.s5p.hcho.convert.output.var.cloud_pressure_crb.units        :   Pa
  cso.s5p.hcho.convert.output.var.cloud_pressure_crb.dims         :   pixel

  cso.s5p.hcho.convert.output.var.amf_troposphere.from            :   PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/formaldehyde_tropospheric_air_mass_factor
  cso.s5p.hcho.convert.output.var.amf_troposphere.units           :   1
  cso.s5p.hcho.convert.output.var.amf_troposphere.dims         :   pixels
  
  
Output files
------------

The name of the target files should be specified with a directory and filename;
the later could include a template for the orbit number::

    ! output directory and filename:
    ! - times are taken from mid of selection, rounded to hours
    ! - use '%{orbit}' for orbit number
    cso.s5p.hcho.convert.output.dir          :  /Scratch/CSO/S5p/RPRO/HCHO/Europe/%Y/%m
    cso.s5p.hcho.convert.output.filename     :  S5p_RPRO_HCHO_%{orbit}.nc

A flag is read to decide if existing files should be renewed or kept::

    cso.s5p.hcho.convert.renew                  :  True     
    
The target file is created as an :py:class:`.CSO_S5p_File` object.
It's :py:meth:`AddSelection <.CSO_S5p_File.AddSelection>` method is called with the input object as argument,
and this will copy the selected pixels for variables specified in the settings.
The :py:meth:`Write <.CSO_File.Write>` method creates the file.

Global attributes for the target file should be specified with::

    ! global attributes:
    cso.s5p.hcho.convert.output.attrs               :  format Conventions author institution email
    !
    cso.s5p.hcho.convert.output.attr.format         :  1.0
    cso.s5p.hcho.convert.output.attr.Conventions    :  CF-1.7
    cso.s5p.hcho.convert.output.attr.author         :  Your Name
    cso.s5p.hcho.convert.output.attr.institution    :  CSO
    cso.s5p.hcho.convert.output.attr.email          :  Your.Name@cso.org

The conversion also creates (or updates) a *listing* file with the names of the created files
(relative to the *listing* file), and the time range of pixels in the file::

    ! csv file that will hold records per file with:
    ! - timerange of pixels in file
    ! - orbit number
    cso.s5p.hcho.convert.output.listing.file        :  /Scratch/CSO/S5p/listing-HCHO-Europe.csv

This file will be used by the observation operator to selects orbits with pixels valid for 
a desired time range.
The *listing* is a csv file that looks something like::

    filename                     ;start_time                   ;end_time                     ;orbit
    2018/06/S5p_RPRO_HCHO_03272.nc;2018-06-01T01:32:46.673000000;2018-06-01T01:36:12.948000000;03272
    2018/06/S5p_RPRO_HCHO_03273.nc;2018-06-01T03:12:53.649000000;2018-06-01T03:17:43.082000000;03273
    2018/06/S5p_RPRO_HCHO_03274.nc;2018-06-01T04:52:43.586000000;2018-06-01T04:59:12.377000000;03274
    :



.. Label between '.. _' and ':' ; use :ref:`text <label>` for reference
.. _s5p-hcho-catalogue:

Catalogue
=========

The :py:class:`CSO_Catalogue <.cso_catalogue.CSO_Catalogue>` class could be used
to create a catalogue of images for the converted files.
Configuration could look like::

    ! catalogue creation task:
    cso.s5p.hcho.catalogue.task.figs.class  :  cso.CSO_Catalogue
    cso.s5p.hcho.catalogue.task.figs.args   :  '${PWD}/rc/cso-s5p-hcho.rc', \
                                                rcbase='cso.s5p.hcho.catalogue'

The configuration describes where to find a *listing* file with orbits, 
which variables should be plot, the colorbar properties, etc.
See :py:class:`CSO_Catalogue <.cso_s5p.CSO_Catalogue>` class description for how
the settings in general look like.

The class creates figures for a list of variables::

  ! variables to be plotted:
  cso.s5p.hcho.catalogue.vars                    :  vcd vcd_errvar qa_value \
                                                      cloud_fraction cloud_radiance_fraction

By default the catalogue creator simply creates a map with the value of the a variable on the track.
Optionally settings could be used to specifiy a different unit, or the value range for the colorbar::

  ! convert units:
  cso.tutorial.catalogue.var.vcd.units          :  1e15 mlc/cm2
  ! style:
  cso.tutorial.catalogue.var.vcd.vmin           :   0.0
  cso.tutorial.catalogue.var.vcd.vmax           :  10.0

Figures are saved to files with the basename of the original orbit file and the plotted variable::

    /Scratch/CSO/catalogue/2018/06/01/S5p_RPRO_HCHO_03278__vcd.png
                                      S5p_RPRO_HCHO_03278__qa_value.png
                                      :

.. figure:: figs/HCHO/S5p_RPRO_HCHO_03278__vcd.png 
   :scale: 50 %
   :align: center
   :alt: S5p hcho columns

To search for interesting features in the data, 
the :py:class:`Indexer <utopya_index.Indexer>` class could be used to create index pages.
Configuration could look like::

    ! index creation task:
    cso.s5p.hcho.catalogue.task.index.class     :  utopya.Indexer
    cso.s5p.hcho.catalogue.task.index.args      :  '${PWD}/rc/cso-s5p-hcho.rc', \
                                                   rcbase='cso.s5p.hcho.catalogue-index'

When succesful, the index creator displays an url that could be loaded in a browser::

    Browse to:
      file:///Scratch/CSO/catalogue/index.html

.. figure:: figs/HCHO/CSO_HCHO_catalogue.png
   :scale: 50 %
   :align: center
   :alt: Index for S5p HCHO columns



Configuration of observation operator
=====================================

The *observation operator* described in chapter ':ref:`obsoper`' requires settings from
an rcfile.

First specify the (relative) location of the *listing* file with orbit file names and time ranges::

    ! template for listing with converted files:
    <rcbase>.listing           : ../S5p/RPRO/HCHO/CAMS/listing.csv

The operator should read variables from the data files that are needed to simulate a retrieval
from the model arrays.
This includes for example the pressures that define the *a priori* layers, the averaging kernel,
and for this product, the airmass factor and tropopause level.
Specify a list of names for these variables::

  ! data variables:
  tutorial.S5p.hcho.dvars             :  hp yr vr A M nla
  
Example settings::

  ! half-level pressures:
  !~ dimensions, copied from data file:
  tutorial.S5p.hcho.dvar.hp.dims      :  layeri
  !~ source variable:
  tutorial.S5p.hcho.dvar.hp.source    :  pressure

  ! retrieval: 
  !~ dimensions, copied from data file:
  tutorial.S5p.hcho.dvar.yr.dims      :  retr
  !~ source variable:
  tutorial.S5p.hcho.dvar.yr.source    :  vcd

  ! retrieval error covariance: 
  !~ dimensions, copied from data file:
  tutorial.S5p.hcho.dvar.vr.dims      :  retr retr
  !~ source variable:
  tutorial.S5p.hcho.dvar.vr.source    :  vcd_errvar

  ! kernel:
  !~ dimensions, copied from data file:
  tutorial.S5p.hcho.dvar.A.dims       :  retr layer
  !~ source variable:
  tutorial.S5p.hcho.dvar.A.source     :  kernel_trop

  ! number of apriori layers in retrieval layer:
  !~ dimensions, copied from data file:
  tutorial.S5p.hcho.dvar.nla.dims     :  retr
  !~ source variable:
  tutorial.S5p.hcho.dvar.nla.source   :  nla

For the simulated values, also define a list of variable names that should be created::

  ! state varaiables to be put out from model:
  tutorial.S5p.hcho.vars                         :  mod_conc mod_hp mod_tcc mod_cc hx ys shx

Example settings::

  ! model concentration profile:
  !~ model layer dimension:
  tutorial.S5p.hcho.var.mod_conc.dims            :  model_layer
  !~ standard attributes:
  tutorial.S5p.hcho.var.mod_conc.attrs           :  long_name units
  tutorial.S5p.hcho.var.mod_conc.attr.long_name  :  model HCHO concentrations
  tutorial.S5p.hcho.var.mod_conc.attr.units      :  ppb

  ! model hpentration profile:
  !~ model layer interfaces:
  tutorial.S5p.hcho.var.mod_hp.dims              :  model_layeri
  !~ standard attributes:
  tutorial.S5p.hcho.var.mod_hp.attrs             :  long_name units
  tutorial.S5p.hcho.var.mod_hp.attr.long_name    :  model pressure at layer interfaces
  tutorial.S5p.hcho.var.mod_hp.attr.units        :  Pa

  ! total cloud cover:
  !~ no extra dimensions:
  tutorial.S5p.hcho.var.mod_tcc.dims             :  
  !~ standard attributes:
  tutorial.S5p.hcho.var.mod_tcc.attrs            :  long_name units
  tutorial.S5p.hcho.var.mod_tcc.attr.long_name   :  total cloud cover
  tutorial.S5p.hcho.var.mod_tcc.attr.units       :  1

  ! cloud cover profiles:
  !~ model layer dimension:
  tutorial.S5p.hcho.var.mod_cc.dims              :  model_layer
  !~ standard attributes:
  tutorial.S5p.hcho.var.mod_cc.attrs             :  long_name units
  tutorial.S5p.hcho.var.mod_cc.attr.long_name    :  cloud cover
  tutorial.S5p.hcho.var.mod_cc.attr.units        :  1

  ! model concentrations at apriori layers:
  !~ apriori layers:
  tutorial.S5p.hcho.var.hx.dims                  :  layer
  !~ how computed:
  tutorial.S5p.hcho.var.hx.formula               :  LayerAverage( hp, mod_hp, mod_conc )
  tutorial.S5p.hcho.var.hx.formula_terms         :  hp: hp mod_hp: mod_hp mod_conc: mod_conc
  !~ standard attributes:
  tutorial.S5p.hcho.var.hx.attrs                 :  long_name units
  tutorial.S5p.hcho.var.hx.attr.long_name        :  model simulations at apriori layers
  tutorial.S5p.hcho.var.hx.attr.units            :  mol m-2

  ! simulated retrievals
  !~ retrieval layers:
  tutorial.S5p.hcho.var.ys.dims                  :  retr
  !~ how computed:
  tutorial.S5p.hcho.var.ys.formula               :  A x
  tutorial.S5p.hcho.var.ys.formula_terms         :  A: A x: hx
  !~ standard attributes:
  tutorial.S5p.hcho.var.ys.attrs                 :  long_name units multiplication_factor_to_convert_to_molecules_percm2
  tutorial.S5p.hcho.var.ys.attr.long_name        :  simulated retrieval
  tutorial.S5p.hcho.var.ys.attr.units            :  mol m-2
  tutorial.S5p.hcho.var.ys.attr.multiplication_factor_to_convert_to_molecules_percm2 : float: 6.022141e+19

  ! partial columns as sum over apriori layers
  !~ retrieval layers:
  tutorial.S5p.hcho.var.shx.dims                 :  retr
  !~ how computed:
  tutorial.S5p.hcho.var.shx.formula              :  PartialColumns( nla, x )
  tutorial.S5p.hcho.var.shx.formula_terms        :  nla: nla x: hx
  !~ standard attributes:
  tutorial.S5p.hcho.var.shx.attrs                :  long_name units multiplication_factor_to_convert_to_molecules_percm2
  tutorial.S5p.hcho.var.shx.attr.long_name       :  tropospheric column in local model
  tutorial.S5p.hcho.var.shx.attr.units           :  mol m-2
  tutorial.S5p.hcho.var.shx.attr.multiplication_factor_to_convert_to_molecules_percm2 : float: 6.022141e+19


Sim-Catalogue
=============

The :py:class:`CSO_Catalogue <.cso_catalogue.CSO_Catalogue>` class could be used
to create a catalogue of images for the converted files.
Configuration could look like::

    ! catalogue creation task:
    cso.s5p.TRACER.sim-catalogue.task.class          :  cso.CSO_SimCatalogue
    cso.s5p.TRACER.sim-catalogue.task.args           :  '${PWD}/rc/cso-s5p-TRACER.rc', \
                                                      rcbase='cso.s5p.TRACER.sim-catalogue'

The configuration describes where to find a *listing* file with orbits, 
which variables should be plot, the colorbar properties, etc.
See :py:class:`CSO_SimCatalogue <.cso_s5p.CSO_SimCatalogue>` class description for how
the settings in general look like.

The class creates figures for a list of variables::

  ! variables to be plotted:
  cso.s5p.hcho.catalogue.vars                    :  yr ys

By default the catalogue creator simply creates a map with the value of the a variable on the track.
Optionally settings could be used to specifiy a different unit, or the value range for the colorbar::

    ! variable:
    cso.s5p.hcho.sim-catalogue.var.yr.source          :  data:vcd
    ! convert units:
    cso.s5p.hcho.sim-catalogue.var.yr.units           :  1e15 mlc/cm2
    ! style:
    cso.s5p.hcho.sim-catalogue.var.yr.vmin            :   0.0
    cso.s5p.hcho.sim-catalogue.var.yr.vmax            :  50.0

    ! variable:
    cso.s5p.hcho.sim-catalogue.var.ys.source          :  state:y
    ! convert units:
    cso.s5p.hcho.sim-catalogue.var.ys.units           :  1e15 mlc/cm2
    ! style:
    cso.s5p.hcho.sim-catalogue.var.ys.vmin            :   0.0
    cso.s5p.hcho.sim-catalogue.var.ys.vmax            :  50.0

Figures are saved to files with the basename of the original orbit file and the plotted variable::

     file://${my.run.base}/cso-catalogue/HCHO//2018/06/01/S5p_RPRO_HCHO_20180601_1200_yr.png
                                                          S5p_RPRO_HCHO_20180601_1200_ys.png
                                      

To search for interesting features in the data, 
the :py:class:`Indexer <utopya_index.Indexer>` class could be used to create index pages.
Configuration could look like::

    ! index creation task:
    cso.s5p.hcho.catalogue.task.index.class     :  utopya.Indexer
    cso.s5p.hcho.catalogue.task.index.args      :  '${PWD}/rc/cso-s5p-hcho.rc', \
                                                   rcbase='cso.s5p.hcho.catalogue-index'

When succesful, the index creator displays an url that could be loaded in a browser::

    Browse to:
      file://${my.run.base}/cso-catalogue/HCHO/index__20180601.html

.. figure:: figs/HCHO/CSO_HCHO_sim-catalogue.png
   :scale: 50 %
   :align: center
   :alt: Index for Simulated and S5p HCHO columns