Tri-Polar Grid Projected Plotting#

This example demonstrates cell plots of data on the semi-structured ORCA2 model grid.

First, the data is projected into the PlateCarree coordinate reference system.

Second four pcolormesh plots are created from this projected dataset, using different projections for the output image.

  • ORCA2 Data Projected to Mollweide, Sea water potential temperature
  • ORCA2 Data Projected to NorthPolarStereo, Sea water potential temperature
  • ORCA2 Data Projected to Orthographic, Sea water potential temperature
  • ORCA2 Data Projected to PlateCarree, Sea water potential temperature
import as ccrs
import matplotlib.pyplot as plt

import iris
import iris.analysis.cartography
import iris.plot as iplt
import iris.quickplot as qplt

def main():
    # Load data
    filepath = iris.sample_data_path("")
    cube = iris.load_cube(filepath)

    # Choose plot projections
    projections = {}
    projections["Mollweide"] = ccrs.Mollweide()
    projections["PlateCarree"] = ccrs.PlateCarree()
    projections["NorthPolarStereo"] = ccrs.NorthPolarStereo()
    projections["Orthographic"] = ccrs.Orthographic(
        central_longitude=-90, central_latitude=45

    pcarree = projections["PlateCarree"]
    # Transform cube to target projection
    new_cube, extent = iris.analysis.cartography.project(cube, pcarree, nx=400, ny=200)

    # Plot data in each projection
    for name in sorted(projections):
        fig = plt.figure()
        fig.suptitle("ORCA2 Data Projected to {}".format(name))
        # Set up axes and title
        ax = plt.subplot(projection=projections[name])
        # Set limits
        # plot with Iris quickplot pcolormesh
        # Draw coastlines

if __name__ == "__main__":

Total running time of the script: (0 minutes 1.432 seconds)

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