.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ex3_cmaq_tropomi.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_ex3_cmaq_tropomi.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ex3_cmaq_tropomi.py:


TropOMI/CMAQ L3
===============

This script is designed to create a CMAQ L3 file including alternative TropOMI
NO2 using the CMAQ AMF.

It assumes that you have a 3D CMAQ CONC file, the METCRO3D file, and a TropOMI
NO2 custom L3 file. The TropOMI custom L3 file is created in the TropOMI L3
OpenDAP example. For the 3D CONC or METCRO3D, there are two options.

For CONC and METCRO3D, you can either use your own data or download data from
the EPA's Air QUAlity TimE Series (`EQUATES`_) Project. The EQUATES project is
available through EPA's Remote Sensing Information Gateway (RSIG) data archive.
RSIG data is served one variable at a time, so you will have four files: (1) 3D
CONC file and (3) METCRO3D files -- DENS, ZF, and PRES. The links below are each
approximately 1 GB in size and must be saved using the names suggested.

* Download CMAQ CONC3D `NO2`_ and save as `CCTM_CONC_2019-07-24_12US1.nc`
* Download METCRO3D `PRES`_ and save as `METCRO3D_PRES_2019-07-24_12US1.nc`
* Download METCRO3D `DENS`_ and save as `METCRO3D_DENS_2019-07-24_12US1.nc`
* Download METCRO3D `ZF`_ and save as `METCRO3D_ZF_2019-07-24_12US1.nc`

.. _EQUATES: https://www.epa.gov/cmaq/equates
.. _NO2: https://ofmpub.epa.gov/rsig/rsigserver?SERVICE=wcs&VERSION=1.0.0&REQUEST=GetCoverage&FORMAT=netcdf-ioapi&TIME=2019-07-24T00:00:00Z/2019-07-24T23:59:59Z&BBOX=-135.000000,15.000000,-55.000000,70.000000&COVERAGE=cmaq.equates.conus.conc.NO2&COMPRESS=0&NOLONLATS=1
.. _PRES: https://ofmpub.epa.gov/rsig/rsigserver?SERVICE=wcs&VERSION=1.0.0&REQUEST=GetCoverage&FORMAT=netcdf-ioapi&TIME=2019-07-24T00:00:00Z/2019-07-24T23:59:59Z&BBOX=-135.000000,15.000000,-55.000000,70.000000&COVERAGE=cmaq.equates.conus.metcro3d.PRES&COMPRESS=0&NOLONLATS=1
.. _DENS: https://ofmpub.epa.gov/rsig/rsigserver?SERVICE=wcs&VERSION=1.0.0&REQUEST=GetCoverage&FORMAT=netcdf-ioapi&TIME=2019-07-24T00:00:00Z/2019-07-24T23:59:59Z&BBOX=-135.000000,15.000000,-55.000000,70.000000&COVERAGE=cmaq.equates.conus.metcro3d.DENS&COMPRESS=0&NOLONLATS=1
.. _ZF: https://ofmpub.epa.gov/rsig/rsigserver?SERVICE=wcs&VERSION=1.0.0&REQUEST=GetCoverage&FORMAT=netcdf-ioapi&TIME=2019-07-24T00:00:00Z/2019-07-24T23:59:59Z&BBOX=-135.000000,15.000000,-55.000000,70.000000&COVERAGE=cmaq.equates.conus.metcro3d.ZF&COMPRESS=0&NOLONLATS=1

.. GENERATED FROM PYTHON SOURCE LINES 31-33

Import Library and Configure
----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 33-45

.. code-block:: default

    import cmaqsatproc as csp
    import xarray as xr

    # Using common EPA Lambert Conic Conformal 12-km grid
    GDNAM = '12US1'
    # Doing just one day
    date='2019-07-24'
    # Using TropOMINO2 reader
    readername = 'TropOMINO2'
    # Defining output path
    outpath = f'{readername}_{date}_{GDNAM}_CMAQ.nc'


.. GENERATED FROM PYTHON SOURCE LINES 46-48

Gather Data and Processors
--------------------------

.. GENERATED FROM PYTHON SOURCE LINES 48-71

.. code-block:: default


    # Get satellite reader
    satreader = csp.reader_dict[readername]
    # Get custom L3 file made earlier
    l3 = xr.open_dataset(f'{readername}_{date}_{GDNAM}.nc')

    # Open a CMAQ 3D CONC file that has the NO2 variable.
    qf = csp.open_ioapi(f'CCTM_CONC_{date}_{GDNAM}.nc')[['NO2']]

    # Open a single METCRO3D file or separate files if downloaded from RSIG.
    # Defaulting to separate files
    presf = csp.open_ioapi(f'METCRO3D_PRES_{date}_{GDNAM}.nc')
    densf = csp.open_ioapi(f'METCRO3D_DENS_{date}_{GDNAM}.nc')
    zff = csp.open_ioapi(f'METCRO3D_ZF_{date}_{GDNAM}.nc')

    # Combine DENS, ZF and PRES from METCRO3D files (or file) with CONC
    qf['DENS'] = densf['DENS']
    qf['ZF'] = zff['ZF']
    qf['PRES'] = presf['PRES']

    # Create satellite according to CMAQ, and CMAQ according to satellite
    overf = satreader.cmaq_process(qf, l3)
    overf.to_netcdf(outpath)


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_ex3_cmaq_tropomi.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: ex3_cmaq_tropomi.py <ex3_cmaq_tropomi.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: ex3_cmaq_tropomi.ipynb <ex3_cmaq_tropomi.ipynb>`


.. only:: html

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