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

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

.. _sphx_glr_auto_examples_hyp_oecs_plot_merra_ile.py:


MERRA iLE
=========

Compute the iLE field and for atmospheric flow at time of Godzilla dust
storm using MERRA-2 data which is vertically averaged over pressure surfaces
ranging from 500hPa to 800hPa.

.. GENERATED FROM PYTHON SOURCE LINES 10-18

.. code-block:: Python


    # Author: ajarvis
    # Data: MERRA-2 - Global Modeling and Assimilation Office - NASA

    import numpy as np
    import matplotlib.pyplot as plt
    from numbacs.flows import get_interp_arrays_2D, get_callable_2D
    from numbacs.diagnostics import ile_2D_func







.. GENERATED FROM PYTHON SOURCE LINES 19-27

Get flow data
--------------
Load in atmospheric velocity data, dates, and coordinates. Set domain for
iLE computation, set time, and retrieve jit-callable function for velocity data.

.. note::
   Pandas is a simpler option for storing and manipulating dates but we use
   numpy here as Pandas is not a dependency.

.. GENERATED FROM PYTHON SOURCE LINES 27-58

.. code-block:: Python


    # load in atmospheric data
    dates = np.load("../data/merra_june2020/dates.npy")
    dt = (dates[1] - dates[0]).astype("timedelta64[h]").astype(int)
    t = np.arange(0, len(dates) * dt, dt, np.float64)
    lon = np.load("../data/merra_june2020/lon.npy")
    lat = np.load("../data/merra_june2020/lat.npy")

    # NumbaCS uses 'ij' indexing, most geophysical data uses 'xy'
    # indexing for the spatial coordintes. We need to switch axes and
    # scale by 3.6 since velocity data is in m/s and we want km/hr.
    u = np.moveaxis(np.load("../data/merra_june2020/u_500_800hPa.npy"), 1, 2) * 3.6
    v = np.moveaxis(np.load("../data/merra_june2020/v_500_800hPa.npy"), 1, 2) * 3.6
    nt, nx, ny = u.shape

    # set domain more refined domain on which iLE will be computed
    dx = 0.15
    dy = 0.15
    lonf = np.arange(-35, 25 + dx, dx)
    latf = np.arange(-5, 40 + dy, dy)

    # get interpolant arrays of velocity field
    grid_vel, C_eval_u, C_eval_v = get_interp_arrays_2D(t, lon, lat, u, v)

    # get jit-callable interpolant of velocity data
    vel_func = get_callable_2D(grid_vel, C_eval_u, C_eval_v, spherical=1)

    # set time at which iLE will be computed
    day = 20
    t0_date = np.datetime64(f"2020-06-{day:02d}")
    t0 = t[np.nonzero(dates == t0_date)[0][0]]







.. GENERATED FROM PYTHON SOURCE LINES 59-62

iLE
----
Compute iLE field from velocity data directly at time t0.

.. GENERATED FROM PYTHON SOURCE LINES 62-64

.. code-block:: Python

    ile = ile_2D_func(vel_func, lonf, latf, t0=t0, h=1e-2)








.. GENERATED FROM PYTHON SOURCE LINES 65-69

Plot
----
Plot the results. Using the cartopy package for plotting geophysical data is
advised but it is not a dependency so we simply use matplotlib.

.. GENERATED FROM PYTHON SOURCE LINES 69-79

.. code-block:: Python

    coastlines = np.load("../data/merra_june2020/coastlines.npy")
    fig, ax = plt.subplots(dpi=200)
    ax.scatter(coastlines[:, 0], coastlines[:, 1], 1, "k", marker=".", edgecolors=None, linewidths=0)
    ax.contourf(
        lonf, latf, ile.T, levels=np.linspace(0, np.percentile(ile, 99.5), 51), extend="both", zorder=0
    )
    ax.set_xlim([lonf[0], lonf[-1]])
    ax.set_ylim([latf[0], latf[-1]])
    ax.set_aspect("equal")
    plt.show()



.. image-sg:: /auto_examples/hyp_oecs/images/sphx_glr_plot_merra_ile_001.png
   :alt: plot merra ile
   :srcset: /auto_examples/hyp_oecs/images/sphx_glr_plot_merra_ile_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 7.364 seconds)


.. _sphx_glr_download_auto_examples_hyp_oecs_plot_merra_ile.py:

.. only:: html

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

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

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

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

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

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

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


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

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