.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/retrievals/plot_wavelet_pbl.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_retrievals_plot_wavelet_pbl.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_retrievals_plot_wavelet_pbl.py:


Planetary Boundary Layer Height Wavelet Method Retrieval
---------------------------------------------------------

This example shows how to estimate the planetary boundary layer
height via a Haar wavelet covariance transform retrieval

Author: Robert Jackson

.. GENERATED FROM PYTHON SOURCE LINES 10-55



.. image-sg:: /auto_examples/retrievals/images/sphx_glr_plot_wavelet_pbl_001.png
   :alt: SGP Ceilometer PBL Height Estimate via Wavelet Method
   :srcset: /auto_examples/retrievals/images/sphx_glr_plot_wavelet_pbl_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    from arm_test_data import DATASETS

    import act

    # Read Ceilometer data for an example
    filename_ceil = DATASETS.fetch('sgpceilC1.b1.20190101.000000.nc')
    ds = act.io.arm.read_arm_netcdf(filename_ceil)

    # Apply corrections to the dataset
    ds = act.corrections.correct_ceil(ds, var_name='backscatter')

    # Estimate PBL Height via a Haar wavelet covariance transform,
    # limiting the search to below 2000 m to exclude elevated cloud layers
    ds = act.retrievals.pbl_lidar.calculate_wavelet_pbl(
        ds, var_name='backscatter', range_name='range', scale=60.0, max_height=1500.0
    )

    # Plot the pbl height estimates
    display = act.plotting.TimeSeriesDisplay(ds, figsize=(10, 5))

    # plot the CL backscatter before overlaying the Wavelet Method PBL Height
    display.plot(
        'backscatter',
        cmap='ChaseSpectral',
        vmin=-6,
        vmax=6,
        set_title='SGP Ceilometer PBL Height Estimate via Wavelet Method',
    )

    # overlay the PBL Height estimate. We will compute a 10 minute running average to smooth the estimate.
    # The rolling function is used to compute a running average of the PBL height estimate over a 10 minute window,
    # with a minimum of 3 valid data points required for the average to be computed.
    # The center=True argument ensures that the average is centered on the current time point.
    display.axes[0].plot(
        ds['resampled_time'].values,
        ds['pbl_wavelet'].rolling(resampled_time=10, center=True, min_periods=3).mean().values,
        color='w',
        linewidth=2,
        label='Wavelet PBL Height Estimate',
    )
    # shorten the range
    display.set_yrng([0, 2000])
    plt.show()


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

   **Total running time of the script:** (0 minutes 10.908 seconds)


.. _sphx_glr_download_auto_examples_retrievals_plot_wavelet_pbl.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_wavelet_pbl.zip <plot_wavelet_pbl.zip>`


.. only:: html

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

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