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

.. only:: html

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

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

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

.. _sphx_glr_examples_sigma_to_pressure_interpolation.py:


===============================
Sigma to Pressure Interpolation
===============================

By using `metpy.calc.log_interp`, data with sigma as the vertical coordinate can be
interpolated to isobaric coordinates.

.. GENERATED FROM PYTHON SOURCE LINES 12-23

.. code-block:: default


    import cartopy.crs as ccrs
    import cartopy.feature as cfeature
    import matplotlib.pyplot as plt
    from netCDF4 import Dataset, num2date

    from metpy.cbook import get_test_data
    from metpy.interpolate import log_interpolate_1d
    from metpy.plots import add_metpy_logo, add_timestamp
    from metpy.units import units








.. GENERATED FROM PYTHON SOURCE LINES 24-29

**Data**

The data for this example comes from the outer domain of a WRF-ARW model forecast
initialized at 1200 UTC on 03 June 1980. Model data courtesy Matthew Wilson, Valparaiso
University Department of Geography and Meteorology.

.. GENERATED FROM PYTHON SOURCE LINES 29-40

.. code-block:: default



    data = Dataset(get_test_data('wrf_example.nc', False))
    lat = data.variables['lat'][:]
    lon = data.variables['lon'][:]
    time = data.variables['time']
    vtimes = num2date(time[:], time.units)
    temperature = units.Quantity(data.variables['temperature'][:], 'degC')
    pressure = units.Quantity(data.variables['pressure'][:], 'Pa')
    hgt = units.Quantity(data.variables['height'][:], 'meter')








.. GENERATED FROM PYTHON SOURCE LINES 41-42

Array of desired pressure levels

.. GENERATED FROM PYTHON SOURCE LINES 42-44

.. code-block:: default

    plevs = [700.] * units.hPa








.. GENERATED FROM PYTHON SOURCE LINES 45-53

**Interpolate The Data**

Now that the data is ready, we can interpolate to the new isobaric levels. The data is
interpolated from the irregular pressure values for each sigma level to the new input
mandatory isobaric levels. `mpcalc.log_interp` will interpolate over a specified dimension
with the `axis` argument. In this case, `axis=1` will correspond to interpolation on the
vertical axis. The interpolated data is output in a list, so we will pull out each
variable for plotting.

.. GENERATED FROM PYTHON SOURCE LINES 53-56

.. code-block:: default


    height, temp = log_interpolate_1d(plevs, pressure, hgt, temperature, axis=1)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/MetPy/MetPy/examples/sigma_to_pressure_interpolation.py:54: UserWarning: Interpolation point out of data bounds encountered
      height, temp = log_interpolate_1d(plevs, pressure, hgt, temperature, axis=1)




.. GENERATED FROM PYTHON SOURCE LINES 57-58

**Plotting the Data for 700 hPa.**

.. GENERATED FROM PYTHON SOURCE LINES 58-97

.. code-block:: default


    # Set up our projection
    crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)

    # Set the forecast hour
    FH = 1

    # Create the figure and grid for subplots
    fig = plt.figure(figsize=(17, 12))
    add_metpy_logo(fig, 470, 320, size='large')

    # Plot 700 hPa
    ax = plt.subplot(111, projection=crs)
    ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.75)
    ax.add_feature(cfeature.STATES, linewidth=0.5)

    # Plot the heights
    cs = ax.contour(lon, lat, height[FH, 0, :, :], transform=ccrs.PlateCarree(),
                    colors='k', linewidths=1.0, linestyles='solid')
    cs.clabel(fontsize=10, inline=1, inline_spacing=7, fmt='%i', rightside_up=True,
              use_clabeltext=True)

    # Contour the temperature
    cf = ax.contourf(lon, lat, temp[FH, 0, :, :], range(-20, 20, 1), cmap=plt.cm.RdBu_r,
                     transform=ccrs.PlateCarree())
    cb = fig.colorbar(cf, orientation='horizontal', aspect=65, shrink=0.5, pad=0.05,
                      extendrect='True')
    cb.set_label('Celsius', size='x-large')

    ax.set_extent([-106.5, -90.4, 34.5, 46.75], crs=ccrs.PlateCarree())

    # Make the axis title
    ax.set_title(f'{plevs[0]:~.0f} Heights (m) and Temperature (C)', loc='center', fontsize=10)

    # Set the figure title
    fig.suptitle(f'WRF-ARW Forecast VALID: {vtimes[FH]} UTC', fontsize=14)
    add_timestamp(ax, vtimes[FH], y=0.02, high_contrast=True)

    plt.show()



.. image-sg:: /examples/images/sphx_glr_sigma_to_pressure_interpolation_001.png
   :alt: WRF-ARW Forecast VALID: 1980-06-03 13:00:00 UTC, 700 hPa Heights (m) and Temperature (C)
   :srcset: /examples/images/sphx_glr_sigma_to_pressure_interpolation_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  15.888 seconds)


.. _sphx_glr_download_examples_sigma_to_pressure_interpolation.py:

.. only:: html

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




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

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

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

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


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_