
.. Label between '.. _' and ':' ; use :ref:`text <label>` for reference
.. _s5p-so2-processing:

*******************************
Sentinel-5p SO2 data processing
*******************************

This chapter describes the tasks performed for processing Sentinel-5p SO\ :sub:`2` data
from the operational processing.

See also the chapter on the :ref:`s5p-so2-cobra-processing`.


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__SO2___``, ``PUM-SO2`` Product User Manual

Further product details on filters/validation can be found in (Fioletov et al., 2020)
and (Theys et al., 2019).

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)`;
    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}\ \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 recommended minimum is 0.5, this excludes cloudy scenes and other problematic retrievals.



References
----------

* | Fioletov, V., McLinden, C. A., Griffin, D., Theys, N., Loyola, D. G., Hedelt, P., Krotkov, N. A., and Li, C.: 
  | Anthropogenic and volcanic point source SO\ :sub:`2` emissions derived from TROPOMI on board Sentinel-5 Precursor: first results, 
  | Atmos. Chem. Phys., 20, 5591-5607, `doi:10.5194/acp-20-5591-202 <https://doi.org/10.5194/acp-20-5591-2020>`_, 2020.

* | Theys, N., Hedelt, P., De Smedt, I. et al. 
  | Global monitoring of volcanic SO\ :sub:`2` degassing with unprecedented resolution from TROPOMI onboard Sentinel-5 Precursor. 
  | Sci Rep 9, 2643 (2019). `doi:10.1038/s41598-019-39279-y <https://doi.org/10.1038/s41598-019-39279-y>`_


Acknowledgements
----------------

We hereby thank D. Griffin and V. Fioletov for their valuable input.


.. Label between '.. _' and ':' ; use :ref:`text <label>` for reference
.. _s5p-so2-inquire:

Inquire Sentinel-5p/SO2 archive
===============================

S5p/SO2 observations are available from the
`Copernicus Open Access Hub <https://scihub.copernicus.eu/>`_;
see the :ref:`cso-scihub` module for a detailed description.

Data is available for different processing streams, each identified by a 4-character key:

* ``NRTI`` : `Near real time`, available with a day after observation;
* ``OFFL`` : `Offline`, available within weeks after observations;
* ``RPRO`` : re-processing of all previously made observations;

The portals provide data files created with the same retrieval algorithm, 
but with different processor versions.
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_scihub.CSO_SciHub_Inquire` class is available to inquire the
*Copernicus Open Access Hub*. 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 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::

    orbit;start_time;end_time;processing;collection;processor_version;filename;href
    21497;2021-12-06 14:05:54;2021-12-06 15:47:24;OFFL;02;020301;S5P_OFFL_L2__SO2____20211206T140554_20211206T154724_21497_02_020301_20211208T043331.nc;https://s5phub.copernicus.eu/dhus/odata/v1/Products('d9d33ffa-9fe5-43cc-b5a1-b65c22e874ad')/$value
    21852;2021-12-31 14:37:39;2021-12-31 16:19:09;OFFL;02;020301;S5P_OFFL_L2__SO2____20211231T143739_20211231T161909_21852_02_020301_20220102T064010.nc;https://s5phub.copernicus.eu/dhus/odata/v1/Products('ff5c922c-450c-43db-97e4-f46bdd55ffb2')/$value
    :

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

To visualize what is available from the various portals, the
:py:class:`cso_scihub.CSO_SciHub_InquirePlot` could be used to create an overview figure:

.. figure:: figs/SO2/Copernicus_S5p_SO2.png
   :scale: 50 %
   :align: center
   :alt: Overview of available SO2 processings.

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

    ! single step:
    cso.s5p.so2.inquire-scihub.class              :  utopya.UtopyaJobStep
    ! two tasks:
    cso.s5p.so2.inquire-scihub.tasks              :  table
    !~ inquire available files:
    cso.s5p.so2.inquire-scihub.table.class        :  cso.CSO_SciHub_Inquire
    cso.s5p.so2.inquire-scihub.table.args         :  '${PWD}/config/ESA-S5p/cso-s5p-so2.rc', \
                                                          rcbase='cso.s5p.so2.inquire-s5phub-table'
    !~ create plot of available versions:
    cso.s5p.so2.inquire-scihub.plot.class         :  cso.CSO_SciHub_InquirePlot
    cso.s5p.so2.inquire-scihub.plot.args          :  '${PWD}/config/ESA-S5p/cso-s5p-so2.rc', \
                                                          rcbase='cso.s5p.so2.inquire-s5phub-plot'



.. Label between '.. _' and ':' ; use :ref:`text <label>` for reference
.. _s5p-so2-convert:

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

The '``cso.s5p.so2.convert``' task converts orbit files downloaded from a portal into a CSO format.

Files are downloaded from a portal if not present locally yet; eventually they are also removed
after conversion to avoid that the portal is completely mirrored.

To save storage, only selected pixels are included in the converted files,
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.so2.convert.class     :  cso.CSO_S5p_Convert
  cso.s5p.so2.convert.args      :  '${PWD}/config/ESA-S5p/cso-s5p-so2.rc', rcbase='cso.s5p.so2.convert'
  
See the class documentation for the general configuration,
below some specific choices are described.
The example is based on the S5p SO\ :sub:`2` file from which the header is available in:

* `doc/samples/S5P_OFFL_L2__SO2____20200101T005246_20200101T023416_11487_01_010107_20200103T022907.txt <../../samples/S5P_OFFL_L2__SO2____20200101T005246_20200101T023416_11487_01_010107_20200103T022907.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.so2.convert.filters   :  lons lats valid quality sza ground_pixel cloud_fraction

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.so2.convert.filter.lons.var                :  Geolocation Fields/Longitude
  cso.s5p.so2.convert.filter.lons.type               :  minmax
  cso.s5p.so2.convert.filter.lons.minmax             :  -30.0 45.0
  cso.s5p.so2.convert.filter.lons.units              :  degrees_east
  
Extension to the product guide
------------------------------

Several publications report extended data criteria beyond the PUMs quality_flags that should be used to ensure data quality.
Examples of such are the emission source detection paper by *(Fioletov et al., 2020)*,
and the volcanic SO\ :sub:`2` monitoring paper by Theys et al., 2019. 
Both publications mention the poor quality of observations at the edges of TROPOMI observation swath 
as well as the reduced quality at high Solar Zenith Angles.
The integration time of pixels toward the edge of the swath has been reduced to decrease the pixel size,
however this also reduces the overall quality of the observation (SNR).
Therefor we advise to only select the pixels with id's 25-425, and for really strict cases only 50-400.
Examples of both the edge pixels and SZA effects are shown in the figures below, 
with 2 figures from Fioletov et al., 2020 and an example for the SZA based on the CSO results.

.. topic:: Figure from Fioletov et al. (2020)

   .. figure:: figs/SO2/Example_fig1_fioletov_2020.png
      :scale: 85 %
      :align: center
      :alt: Figure 1 from Fioletov (2020)

.. topic:: Figure from Fioletov et al. (2020)

   .. figure:: figs/SO2/Example_fig2_fioletov_2020.png
      :scale: 85 %
      :align: center
      :alt: Figure 2 from Fioletov (2020)
   
.. figure:: figs/SO2/overpass_example_filters_so2.png
   :scale: 50 %
   :align: center
   :alt: Examples of column, error, and quality number for two orbits.
   
   *Examples of retrieved SO2 columns, retrieval errors, and quality flags.*
   
Furthermore, Fioletov et al., 2020 (Fig below) report large-scale biases in the current TROPOMI product, with TROPOMI showing significantly higher total columns, which can be expected to introduce a potential high bias throughout the domain.
A solution advised by Fioletov et al is to remove the bias by comparing up- and down-wind values around an SO\ :sub:`2` emissions source, but that will not be included in this algorithm.

.. topic:: Figure from Fioletov et al. (2020)

   .. figure:: figs/SO2/Example_fig5_fioletov_2020.png
      :scale: 85 %
      :align: center
      :alt: Example of mean SO\ :sub:`2` columns for TROPOMI OMI and OMPS over several regions (Fioletov et al., 2020).

   
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.so2.convert.output.vars    : longitude longitude_bounds \
                                       latitude latitude_bounds \
                                       track_longitude track_longitude_bounds \
                                       track_latitude  track_latitude_bounds \
                                       time \
                                       pressure kernel qa_value \
                                       vcd vcd_errvar \
                                       detection_flag cloud_fraction solar_zenith_angle ground_pixel

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.so2.convert.output.var.longitude.dims                   :   pixel
  cso.s5p.so2.convert.output.var.longitude.from                   :   PRODUCT/longitude
  cso.s5p.so2.convert.output.var.longitude.attrs                  :   { 'bounds' : None }

  cso.s5p.so2.convert.output.var.latitude.dims                    :   pixel
  cso.s5p.so2.convert.output.var.latitude.from                    :   PRODUCT/latitude
  cso.s5p.so2.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.so2.convert.output.var.track_longitude.dims             :   track_scan track_pixel
  cso.s5p.so2.convert.output.var.track_longitude.special          :   track_longitude
  cso.s5p.so2.convert.output.var.track_longitude.from             :   PRODUCT/longitude
  cso.s5p.so2.convert.output.var.track_longitude.attrs            :   { 'bounds' : None }

  cso.s5p.so2.convert.output.var.track_latitude.dims              :   track_scan track_pixel
  cso.s5p.so2.convert.output.var.track_latitude.special           :   track_latitude
  cso.s5p.so2.convert.output.var.track_latitude.from              :   PRODUCT/latitude
  cso.s5p.so2.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.so2.convert.output.var.time.dims                        :   pixel
  cso.s5p.so2.convert.output.var.time.special                     :   time-delta
  cso.s5p.so2.convert.output.var.time.tref                        :   PRODUCT/time
  cso.s5p.so2.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.so2.convert.output.var.vcd.dims                         :   pixel retr
  cso.s5p.so2.convert.output.var.vcd.from                         :   PRODUCT/sulfurdioxide_total_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),
  ! use dims with different names to avoid that cf-checker complains:
  cso.s5p.so2.convert.output.var.vcd_errvar.dims                  :   pixel retr
  cso.s5p.so2.convert.output.var.vcd_errvar.from                  :   PRODUCT/sulfurdioxide_total_vertical_column_precision
  !~ skip standard name, modifier "standard_error" is not valid anymore:
  cso.s5p.so2.convert.output.var.vcd_errvar.attrs                 :   { 'standard_name' : None }

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.so2.convert.output.var.pressure.dims                    :   pixel layeri
  cso.s5p.so2.convert.output.var.pressure.special                 :   hybounds_to_pressure
  cso.s5p.so2.convert.output.var.pressure.sp                      :   PRODUCT/SUPPORT_DATA/INPUT_DATA/surface_pressure
  cso.s5p.so2.convert.output.var.pressure.hyab                    :   PRODUCT/tm5_constant_a
  cso.s5p.so2.convert.output.var.pressure.hybb                    :   PRODUCT/tm5_constant_b
  cso.s5p.so2.convert.output.var.pressure.units                   :   Pa

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

  ! description:
  cso.s5p.so2.convert.output.var.kernel.dims                      :   pixel layer retr
  cso.s5p.so2.convert.output.var.kernel.from                      :   PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/averaging_kernel

Other variables can be copied directly::

  ! quality flag:
  cso.s5p.so2.convert.output.var.qa_value.dims                   :   pixel
  cso.s5p.so2.convert.output.var.qa_value.from                   :   PRODUCT/qa_value
  !~ skip some attributes, cf-checker complains ...
  cso.s5p.so2.convert.output.var.qa_value.attrs                  :   { 'valid_min' : None, 'valid_max' : None }

  ! cloud property:
  cso.s5p.so2.convert.output.var.cloud_fraction.dims             :   pixel
  cso.s5p.so2.convert.output.var.cloud_fraction.from             :   PRODUCT/SUPPORT_DATA/INPUT_DATA/cloud_fraction_crb
  cso.s5p.so2.convert.output.var.cloud_fraction.attrs            :   { 'coordinates' : None, 'source' : None }

  ! detection flag, for observations near known source locations:
  cso.s5p.so2.convert.output.var.detection_flag.dims             :   pixel
  cso.s5p.so2.convert.output.var.detection_flag.from             :   PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/sulfurdioxide_detection_flag
  cso.s5p.so2.convert.output.var.detection_flag.attrs            :   { 'coordinates' : None }
  cso.s5p.so2.convert.output.var.detection_flag.dtype            :   i1

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.so2.convert.output.dir          :  /Scratch/CSO/S5p/RPRO/SO2/Europe/%Y/%m
    cso.s5p.so2.convert.output.filename     :  S5p_RPRO_SO2_%{orbit}.nc

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

    cso.s5p.so2.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.so2.convert.output.attrs               :  format Conventions author institution email
    !
    cso.s5p.so2.convert.output.attr.format         :  1.0
    cso.s5p.so2.convert.output.attr.Conventions    :  CF-1.7
    cso.s5p.so2.convert.output.attr.author         :  Your Name
    cso.s5p.so2.convert.output.attr.institution    :  CSO
    cso.s5p.so2.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-SO2-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 like::

    filename                     ;start_time                   ;end_time                     ;orbit
    2018/06/S5p_RPRO_SO2_03272.nc;2018-06-01T01:32:46.673000000;2018-06-01T01:36:12.948000000;03272
    2018/06/S5p_RPRO_SO2_03273.nc;2018-06-01T03:12:53.649000000;2018-06-01T03:17:43.082000000;03273
    2018/06/S5p_RPRO_SO2_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-so2-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.so2.catalogue.task.figs.class  :  cso.CSO_Catalogue
    cso.s5p.so2.catalogue.task.figs.args   :  '${PWD}/config/ESA-S5p/cso-s5p-so2.rc', \
                                                rcbase='cso.s5p.so2.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.so2.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_SO2_03278__vcd.png
                                      S5p_RPRO_SO2_03278__qa_value.png
                                      :

.. figure:: figs/SO2/S5p_RPRO_SO2_03278__vcd.png 
   :scale: 50 %
   :align: center
   :alt: S5p SO\ :sub:`2` 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.so2.catalogue.task.index.class     :  utopya.Indexer
    cso.s5p.so2.catalogue.task.index.args      :  '${PWD}/config/ESA-S5p/cso-s5p-so2.rc', \
                                                   rcbase='cso.s5p.so2.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/SO2/CSO_SO2_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/SO2/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 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
  tutorial.S5p.hcho.var.ys.attr.long_name        :  simulated retrieval
  tutorial.S5p.hcho.var.ys.attr.units            :  mol m-2

  ! 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
  tutorial.S5p.hcho.var.shx.attr.long_name       :  tropospheric column in local model
  tutorial.S5p.hcho.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/ESA-S5p/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.so2.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.so2.sim-catalogue.var.yr.source          :  data:vcd
    ! convert units:
    cso.s5p.so2.sim-catalogue.var.yr.units           :  1e15 mlc/cm2
    ! style:
    cso.s5p.so2.sim-catalogue.var.yr.vmin            :   0.0
    cso.s5p.so2.sim-catalogue.var.yr.vmax            :  50.0

    ! variable:
    cso.s5p.so2.sim-catalogue.var.ys.source          :  state:y
    ! convert units:
    cso.s5p.so2.sim-catalogue.var.ys.units           :  1e15 mlc/cm2
    ! style:
    cso.s5p.so2.sim-catalogue.var.ys.vmin            :   0.0
    cso.s5p.so2.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/SO2//2018/06/01/S5p_RPRO_SO2_20180601_1200_yr.png
                                                         S5p_RPRO_SO2_20180601_1200_ys.png
                                      

.. figure:: figs/SO2/S5p_RPRO_SO2_20180601_1200_ys.png 
   :scale: 50 %
   :align: center
   :alt: S5p SO2 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.so2.catalogue.task.index.class     :  utopya.Indexer
    cso.s5p.so2.catalogue.task.index.args      :  '${PWD}/config/ESA-S5p/cso-s5p-so2.rc', \
                                                   rcbase='cso.s5p.so2.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/SO2/index__20180601.html

.. figure:: figs/SO2/CSO_SO2_sim-catalogue.png
   :scale: 50 %
   :align: center
   :alt: Index for Simulated and S5p SO\ :sub:`2` columns


