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

**********************************
Sentinel-5p CHOCHO data processing
**********************************

This chapter describes the tasks performed for processing Sentinel-5p CHOCHO (glyoxal) data.


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

The product is described in:

* | Lerot, C., Hendrick, F., Van Roozendael, M., Alvarado, L. M. A., Richter, A., De Smedt, I., Theys, N., Vlietinck, J., Yu, H., Van Gent, J., Stavrakou, T., Muller, J.-F., Valks, P., Loyola, D., Irie, H., Kumar, V., Wagner, T., Schreier, S. F., Sinha, V., Wang, T., Wang, P., and Retscher, C.: 
  | Glyoxal tropospheric column retrievals from TROPOMI - multi-satellite intercomparison and ground-based validation, 
  | Atmos. Meas. Tech., 14, 7775-7807, `<https://doi.org/10.5194/amt-14-7775-2021>`_, 2021. 

Data is provided via two archives:

* The `GLYRETRO project <https://glyretro.aeronomie.be/>`_ website.
  From here also a (draft) *Product User Manual* is availabe:

  * | Christophe Lerot:
    | GLYoxal Retrievals from TROPOMI (GLYRETRO) - Product User Manual.
    | `CHOCHO_PUM_S5P+I_BIRA_draft.pdf <https://glyretro.aeronomie.be/ProjectDir/documents/CHOCHO_PUM_S5P+I_BIRA_draft.pdf>`_, 2021.
    
  This data needs to be downloaded manually, see below.

* Lates data is provided via the `S5P-PAL Data Portal <https://data-portal.s5p-pal.com/>`_
  (see also the :ref:`cso-pal` module for a more detailed description of this portal).
  This portal provides an updated PUM:
  
  * | S5P Glyoxal Product User Manual
    | `S5P-BIRA-PUM-CHOCHO_1.0.pdf <https://data-portal.s5p-pal.com/product-docs/chocho/S5P-BIRA-PUM-CHOCHO_1.0.pdf>`_

  The CSO tools could inqquire and download from this archive.

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}\ \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}` is the *tropospheric* averaging kernel matrix with shape :math:`(n_r,n_a)`;
    with :math:`n_a` the number of *a priori* layers;
  * :math:`\mathbf{x}` is the atmospheric state, which consists of a 3D array of CHOCHO 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}\ \mathbf{H}\mathbf{x}^{true}  ~\sim~ \mathcal{N}\left(\mathbf{o},\mathbf{R}\right)

* The retrieval status and quality is indicated by the ``qa_value``. 
  The *PUM* section 5.4 recommends a minimum of 0.5;
  excludes cloudy scenes and other problematic retrievals.
  
  * Some orbit files were found having pixels with an undefined ``PRODUCT/time_delta`` value, 
    while ``qa_value`` was 1. For these pixels, also the pixel coordinates (``PRODUCT/longitude`` etc)
    are undefined.





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

Download GlyRetro data
======================

In 2022-08 the best way to obtain Level-2 S5p/CHOCHO data was to:

* browse to the  `GLYRETRO project <https://glyretro.aeronomie.be/>`_ website;
* under the *Data* dropdown select *Request data for download*;
* register for download to obtain a user name and password;
* download data per chunk of days;
  *tested with downloads of 5 days per request, max 6 requests at a time*.

A download returns a zip file with multiple orbit files.
The orbit files are organized in sub-directories per day::

    2018/01/01/S5P_RPRO_L2__CHOCHO___20180101T004140_20180101T022310_01130_01_010000_20210201.nc
               :

Note that the official S5p filename formatting rules require exactly 10 characters for the product identifier;
in the current product a 12-character key ``L2__CHOCHO__`` is used.


CSO processing
==============

*(See* :ref:`tutorial` *chapter for introduction to CSO scripts and configuration)*

An example configuration of the CSO processing of the S5p/CHOCHO data is available via
the following settings:

* `config/Copernicus/cso.rc <../../../config/Copernicus/cso.rc>`_

  Top-level settings that configure the job-tree with various sub-tasks.
  This is a generic file that could be used for multiple S5 products, 
  edit it to select the CHOCHO processing.
   
* `config/Copernicus/cso-user-settings.rc <../../../config/Copernicus/cso-user-settings.rc>`_

  User-specific settings such as the work directory.
  
* `config/Copernicus/cso-s5p-chocho.rc <../../../config/Copernicus/cso-s5p-chocho.rc>`_
  
  Specific settings for CHOCHO product.

Start the job-tree using::

  ./bin/cso  config/Copernicus/cso.rc
  
Selected sub-steps in the processing are described below.



Inquire archives
================

The data files might have been created in different processing streams,
and/or using different processor versions. 
It is therefor useful to first inquire the archives (downloaded, or the PAL archive)
to see which processor versions are available for a certain period.

The processing stream is identified by a 4-character key:

* ``OFFL`` : `Offline`, available within weeks after observations;
* ``RPRO`` : re-processing of all previously made observations.
* ``PAL_`` : processed data stored on the *Product Algorithm Laboratory* portal.

The portals provide data files created with the same retrieval algorithm, but most recent data
(latest processor version) might be available on only one of the portals.
It is therefore necessary to first inquire both archives to see which data is available where,
and what the version numbers are.

The :py:class:`CSO_DataSpace_Inquire <cso_dataspace.CSO_DataSpace_Inquire>` class is available to inquire the
*Copernicus DataSpace*. The settings used by this class allow selection
on for example time range and intersection area. 
The result is a csv file which with columns for keywords such as orbit number and processor version,
as well as the filename of the data and the url that should be used to actually download the data::

    filename                                                                                    ;start_time         ;end_time           ;mission;processing;product_id;orbit;collection;processor_version;processing_time
    2020/01/01/S5P_OFFL_L2__CHOCHO___20200101T005246_20200101T023416_11487_01_010000_20210128.nc;2020-01-01 00:52:46;2020-01-01 02:34:16;S5P    ;OFFL      ;L2__CHOCHO;11487;01        ;010000           ;2021-01-28
    2020/01/01/S5P_OFFL_L2__CHOCHO___20200101T023416_20200101T041546_11488_01_010000_20210128.nc;2020-01-01 02:34:16;2020-01-01 04:15:46;S5P    ;OFFL      ;L2__CHOCHO;11488;01        ;010000           ;2021-01-28
    :

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

A similar class :py:class:`CSO_S5p_Download_Listing <cso_s5p.CSO_S5p_Download_Listing>` 
class is available to list the content of the downloaded GlyRetro files.
Also this will produce a table file.

To visualize what is available from the portal, the
:py:class:`CSO_Inquire_Plot <cso_inquire.CSO_Inquire_Plot>` could be used to create an overview figure:

.. figure:: figs/CHOCHO/Copernicus_S5p_CHOCHO.png
   :scale: 50 %
   :align: center
   :alt: Overview of available glyoxal processings.

The jobtree configuration to inquire the portals and create the overview figure could look like::

    ! single step:
    cso.s5p.chocho.inquire.class                      :  utopya.UtopyaJobStep

    ! inquire downloads and archive, plot overview:
    cso.s5p.chocho.inquire.tasks                      :  table-glyretro table-pal plot

    !~ inquire files downloaded from GlyRetro:
    cso.s5p.chocho.inquire.table-glyretro.class       :  cso.CSO_S5p_Download_Listing
    cso.s5p.chocho.inquire.table-glyretro.args        :  '${PWD}/config/Copernicus/cso-s5p-chocho.rc', \
                                                          rcbase='cso.s5p.chocho.inquire-table-glyretro'
    !~ inquire files available on PAL:
    cso.s5p.chocho.inquire.table-pal.class            :  cso.CSO_PAL_Inquire
    cso.s5p.chocho.inquire.table-pal.args             :  '${PWD}/config/Copernicus/cso-s5p-chocho.rc', \
                                                          rcbase='cso.s5p.chocho.inquire-table-pal'
    !~ create plot of available versions:
    cso.s5p.chocho.inquire.plot.class                 :  cso.CSO_Inquire_Plot
    cso.s5p.chocho.inquire.plot.args                  :  '${PWD}/config/Copernicus/cso-s5p-chocho.rc', \
                                                          rcbase='cso.s5p.chocho.inquire-plot'


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

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

The '``cso.s5p.chocho.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.chocho.convert.class     :  cso.CSO_S5p_Convert
  cso.s5p.chocho.convert.args      :  '${PWD}/config/EMEP/cso-s5p-chocho.rc', rcbase='cso.s5p.chocho.convert'

See the class documentation for the general configuration,
below some specific choices are described.
The example is based on the S5p CHOCHO file from which the header is available in:

* `doc/samples/S5P_OFFL_L2__CHOCHO___20200601T001519_20200601T015649_13643_01_010000_20210128.txt <../../samples/S5P_OFFL_L2__CHOCHO___20200601T001519_20200601T015649_13643_01_010000_20210128.txt>`_


Orbit file selection
--------------------

Based on the inquiry the download and conversion could be limitted to files created with the most recent processor versions.

For the S5P files a useful property is also the *collection number*, a 2-digit number that defines a collection of files
(or actually processor versions) that together form a contineous series. The *collection number* is extracted from the filename,
and stored as a column of the listing file.

The following setting is used to select specific files from the archive based on the properities stored
in the listing file::

    ! Provide ';' seperated list of to decide if a particular orbit file should be processed.
    ! If more than one file is available for a particular orbit (from "OFFL" and "RPRO" processing),
    ! the file with the first match will be used.
    ! The expressions should include templates '%{header}' for the column values.
    ! Example to select files from collection '03', preferably from processing 'RPRO' but otherwise from 'OFFL':
    !   (%{collection} == '03') and (%{processing} == 'RPRO') ; \
    !   (%{collection} == '03') and (%{processing} == 'OFFL')
    !
    cso.s5p.chocho.convert.selection                  :  (%{collection} == '03') and (%{processing} == 'RPRO') ; \
                                                         (%{collection} == '03') and (%{processing} == 'OFFL')


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


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.chocho.convert.output.vars : longitude longitude_bounds \
                                       latitude latitude_bounds \
                                       track_longitude track_longitude_bounds \
                                       track_latitude  track_latitude_bounds \
                                       time \
                                       qa_value \
                                       pressure kernel amf_trop vmr_apri \
                                       vcd vcd_errvar \
                                       cloud_fraction solar_zenith_angle

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 ``bounds`` attribite since that would
give warnings from the CF-compliance checker::

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

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

The pixel boundaries are necessary to know the exact footprint of a pixel,
which is for example used when averaging over a grid or simulation from a model.
These are available in the input files, but without a ``units`` attribute as these
are implied by the pixel center coordinate; the conversion therefore requires that
units are defined explicitly.
For the ``longitude_bounds`` a special processing is needed for pixels crossing the dateline,
as the original data simply uses longitudes modulo 360 degrees::

  ! corner longitudes; no units in file:
  cso.s5p.chocho.convert.output.var.longitude_bounds.dims            :   pixel corner
  cso.s5p.chocho.convert.output.var.longitude_bounds.from            :   PRODUCT/SUPPORT_DATA/GEOLOCATIONS/longitude_bounds
  cso.s5p.chocho.convert.output.var.longitude_bounds.units           :   degrees_east
  ! ensure that near dateline the corners form a convex region around center
  ! (with some points outside [-180,+180] if necessary)
  cso.s5p.chocho.convert.output.var.longitude_bounds.special         :   longitude_bounds

  ! corner latitudes, no units in file:
  cso.s5p.chocho.convert.output.var.latitude_bounds.dims             :   pixel corner
  cso.s5p.chocho.convert.output.var.latitude_bounds.from             :   PRODUCT/SUPPORT_DATA/GEOLOCATIONS/latitude_bounds
  cso.s5p.chocho.convert.output.var.latitude_bounds.units            :   degrees_north

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

  cso.s5p.chocho.convert.output.var.track_latitude.dims              :   track_scan track_pixel
  cso.s5p.chocho.convert.output.var.track_latitude.special           :   track_latitude
  cso.s5p.chocho.convert.output.var.track_latitude.from              :   PRODUCT/latitude
  cso.s5p.chocho.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.chocho.convert.output.var.time.dims                        :   pixel
  cso.s5p.chocho.convert.output.var.time.special                     :   time-delta
  cso.s5p.chocho.convert.output.var.time.tref                        :   PRODUCT/time
  cso.s5p.chocho.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.chocho.convert.output.var.vcd.dims                         :   pixel retr
  cso.s5p.chocho.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.chocho.convert.output.var.vcd_errvar.dims                  :   pixel retr retr0
  cso.s5p.chocho.convert.output.var.vcd_errvar.special               :   square_sum
  cso.s5p.chocho.convert.output.var.vcd_errvar.from                  :   PRODUCT/formaldehyde_tropospheric_vertical_column_precision
  cso.s5p.chocho.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.chocho.convert.output.var.pressure.dims                    :   pixel layeri
  cso.s5p.chocho.convert.output.var.pressure.special                 :   pmid_to_pressure
  cso.s5p.chocho.convert.output.var.pressure.pmid                    :   PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/glyoxal_profile_apriori_pressure
  cso.s5p.chocho.convert.output.var.pressure.units                   :   Pa
  cso.s5p.chocho.convert.output.var.pressure.units                   :   Pa

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

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

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

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

  cso.s5p.chocho.convert.output.var.solar_zenith_angle.from         :   PRODUCT/SUPPORT_DATA/GEOLOCATIONS/solar_zenith_angle
  cso.s5p.chocho.convert.output.var.solar_zenith_angle.units        :   degree
  cso.s5p.chocho.convert.output.var.solar_zenith_angle.dims         :   pixel

  cso.s5p.chocho.convert.output.var.cloud_pressure_crb.from         :   PRODUCT/SUPPORT_DATA/INPUT_DATA/cloud_pressure_crb
  cso.s5p.chocho.convert.output.var.cloud_pressure_crb.units        :   Pa
  cso.s5p.chocho.convert.output.var.cloud_pressure_crb.dims         :   pixel

  cso.s5p.chocho.convert.output.var.amf_troposphere.from            :   PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/formaldehyde_tropospheric_air_mass_factor
  cso.s5p.chocho.convert.output.var.amf_troposphere.units           :   1
  cso.s5p.chocho.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.chocho.convert.output.dir          :  /Scratch/CSO/S5p/RPRO/CHOCHO/Europe/%Y/%m
    cso.s5p.chocho.convert.output.filename     :  S5p_RPRO_CHOCHO_%{orbit}.nc

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

    cso.s5p.chocho.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.chocho.convert.output.attrs               :  format Conventions author institution email
    !
    cso.s5p.chocho.convert.output.attr.format         :  1.0
    cso.s5p.chocho.convert.output.attr.Conventions    :  CF-1.7
    cso.s5p.chocho.convert.output.attr.author         :  Your Name
    cso.s5p.chocho.convert.output.attr.institution    :  CSO
    cso.s5p.chocho.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.chocho.convert.output.listing.file        :  /Scratch/CSO/S5p/listing-CHOCHO-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_CHOCHO_03272.nc;2018-06-01T01:32:46.673000000;2018-06-01T01:36:12.948000000;03272
    2018/06/S5p_RPRO_CHOCHO_03273.nc;2018-06-01T03:12:53.649000000;2018-06-01T03:17:43.082000000;03273
    2018/06/S5p_RPRO_CHOCHO_03274.nc;2018-06-01T04:52:43.586000000;2018-06-01T04:59:12.377000000;03274
    :



.. TO BE DONE

    .. Label between '.. _' and ':' ; use :ref:`text <label>` for reference
    .. _s5p-glyox-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.chocho.catalogue.task.figs.class  :  cso.CSO_Catalogue
        cso.s5p.chocho.catalogue.task.figs.args   :  '${PWD}/config/EMEP/cso-s5p-glyox.rc', \
                                                    rcbase='cso.s5p.chocho.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.chocho.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_CHOCHO_03278__vcd.png
                                          S5p_RPRO_CHOCHO_03278__qa_value.png
                                          :

    .. figure:: figs/CHOCHO/S5p_RPRO_CHOCHO_03278__vcd.png 
       :scale: 50 %
       :align: center
       :alt: S5p glyox 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.chocho.catalogue.task.index.class     :  utopya.Indexer
        cso.s5p.chocho.catalogue.task.index.args      :  '${PWD}/config/EMEP/cso-s5p-glyox.rc', \
                                                       rcbase='cso.s5p.chocho.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/CHOCHO/CSO_CHOCHO_catalogue.png
       :scale: 50 %
       :align: center
       :alt: Index for S5p CHOCHO 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/CHOCHO/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.chocho.dvars             :  hp yr vr A M nla

    Example settings::

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

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

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

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

      ! number of apriori layers in retrieval layer:
      !~ dimensions, copied from data file:
      tutorial.S5p.chocho.dvar.nla.dims     :  retr
      !~ source variable:
      tutorial.S5p.chocho.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.chocho.vars                         :  mod_conc mod_hp mod_tcc mod_cc hx ys shx

    Example settings::

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

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

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

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

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

      ! simulated retrievals
      !~ retrieval layers:
      tutorial.S5p.chocho.var.ys.dims                  :  retr
      !~ how computed:
      tutorial.S5p.chocho.var.ys.formula               :  A x
      tutorial.S5p.chocho.var.ys.formula_terms         :  A: A x: hx
      !~ standard attributes:
      tutorial.S5p.chocho.var.ys.attrs                 :  long_name units
      tutorial.S5p.chocho.var.ys.attr.long_name        :  simulated retrieval
      tutorial.S5p.chocho.var.ys.attr.units            :  mol m-2

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


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

        ! variable:
        cso.s5p.chocho.sim-catalogue.var.ys.source          :  state:y
        ! convert units:
        cso.s5p.chocho.sim-catalogue.var.ys.units           :  1e15 mlc/cm2
        ! style:
        cso.s5p.chocho.sim-catalogue.var.ys.vmin            :   0.0
        cso.s5p.chocho.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/CHOCHO//2018/06/01/S5p_RPRO_CHOCHO_20180601_1200_yr.png
                                                              S5p_RPRO_CHOCHO_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.chocho.catalogue.task.index.class     :  utopya.Indexer
        cso.s5p.chocho.catalogue.task.index.args      :  '${PWD}/config/EMEP/cso-s5p-glyox.rc', \
                                                       rcbase='cso.s5p.chocho.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/CHOCHO/index__20180601.html

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



