Logo Iris 1.12

Previous topic

Colouring anomaly data with logarithmic scaling

Next topic

Cross section plots

This Page

Deriving the Coriolis frequency over the globeΒΆ

This code computes the Coriolis frequency and stores it in a cube with associated metadata. It then plots the Coriolis frequency on an orthographic projection.

"""
Deriving the Coriolis frequency over the globe
==============================================

This code computes the Coriolis frequency and stores it in a cube with
associated metadata. It then plots the Coriolis frequency on an orthographic
projection.

"""

import cartopy.crs as ccrs
import iris
from iris.coord_systems import GeogCS
import iris.plot as iplt
import matplotlib.pyplot as plt
import numpy as np


def main():
    # Start with arrays for latitudes and longitudes, with a given number of
    # coordinates in the arrays.
    coordinate_points = 200
    longitudes = np.linspace(-180.0, 180.0, coordinate_points)
    latitudes = np.linspace(-90.0, 90.0, coordinate_points)
    lon2d, lat2d = np.meshgrid(longitudes, latitudes)

    # Omega is the Earth's rotation rate, expressed in radians per second
    omega = 7.29e-5

    # The data for our cube is the Coriolis frequency,
    # `f = 2 * omega * sin(phi)`, which is computed for each grid point over
    # the globe from the 2-dimensional latitude array.
    data = 2. * omega * np.sin(np.deg2rad(lat2d))

    # We now need to define a coordinate system for the plot.
    # Here we'll use GeogCS; 6371229 is the radius of the Earth in metres.
    cs = GeogCS(6371229)

    # The Iris coords module turns the latitude list into a coordinate array.
    # Coords then applies an appropriate standard name and unit to it.
    lat_coord = iris.coords.DimCoord(latitudes,
                                     standard_name='latitude',
                                     units='degrees',
                                     coord_system=cs)

    # The above process is repeated for the longitude coordinates.
    lon_coord = iris.coords.DimCoord(longitudes,
                                     standard_name='longitude',
                                     units='degrees',
                                     coord_system=cs)

    # Now we add bounds to our latitude and longitude coordinates.
    # We want simple, contiguous bounds for our regularly-spaced coordinate
    # points so we use the guess_bounds() method of the coordinate. For more
    # complex coordinates, we could derive and set the bounds manually.
    lat_coord.guess_bounds()
    lon_coord.guess_bounds()

    # Now we input our data array into the cube.
    new_cube = iris.cube.Cube(data,
                              standard_name='coriolis_parameter',
                              units='s-1',
                              dim_coords_and_dims=[(lat_coord, 0),
                                                   (lon_coord, 1)])

    # Now let's plot our cube, along with coastlines, a title and an
    # appropriately-labelled colour bar:
    ax = plt.axes(projection=ccrs.Orthographic())
    ax.coastlines(resolution='10m')
    mesh = iplt.pcolormesh(new_cube, cmap='seismic')
    tick_levels = [-0.00012, -0.00006, 0.0, 0.00006, 0.00012]
    plt.colorbar(mesh, orientation='horizontal', label='s-1',
                 ticks=tick_levels, format='%.1e')
    plt.title('Coriolis frequency')
    plt.show()

(Source code)

if __name__ == '__main__':
    main()

(png)

../../_images/coriolis_plot_01_00.png