ICON Cell-centered contour plots

This example demonstrates filled and line contours for ICON cell-centered scalar fields.

easyclimate.plot.icon.plot_cell_contourf draws filled contours from values located on ICON cells. The companion easyclimate.plot.icon.plot_cell_contour draws contour lines using the same ICON cell coordinates.

import xarray as xr
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import easyclimate as ecl

from easyclimate.plot.icon import plot_cell_contourf, plot_cell_contour

Open a sample ICON pressure-level file. The temperature variable is defined at ICON cell centers, so one time and pressure level are selected below.

data = ecl.open_tutorial_dataset("icon_native_pl_20080909T000000Z")
data

<xarray.Dataset> Size: 6MB
Dimensions:    (ncells: 81920, vertices: 3, time: 1, plev: 1)
Coordinates:
    clon       (ncells) float64 655kB ...
    clat       (ncells) float64 655kB ...
  * time       (time) float64 8B 2.008e+07
  * plev       (plev) float64 8B 8.5e+04
Dimensions without coordinates: ncells, vertices
Data variables:
    clon_bnds  (ncells, vertices) float64 2MB ...
    clat_bnds  (ncells, vertices) float64 2MB ...
    temp       (time, plev, ncells) float32 328kB ...

xarray.Dataset

• Dimensions:
  • ncells: 81920
  • vertices: 3
  • time: 1
  • plev: 1
• Coordinates: (4)
  • clon
    (ncells)
    float64
    ...

    standard_name :

    longitude

    long_name :

    center longitude

    units :

    radian

    bounds :

    clon_bnds

    [81920 values with dtype=float64]

  • clat
    (ncells)
    float64
    ...

    standard_name :

    latitude

    long_name :

    center latitude

    units :

    radian

    bounds :

    clat_bnds

    [81920 values with dtype=float64]

  • time
    (time)
    float64
    2.008e+07

    standard_name :

    time

    units :

    day as %Y%m%d.%f

    calendar :

    proleptic_gregorian

    axis :

    T

    array([20080909.])

  • plev
    (plev)
    float64
    8.5e+04

    standard_name :

    air_pressure

    long_name :

    pressure

    units :

    Pa

    positive :

    down

    axis :

    Z

    array([85000.])

• Data variables: (3)
  • clon_bnds
    (ncells, vertices)
    float64
    ...

    [245760 values with dtype=float64]

  • clat_bnds
    (ncells, vertices)
    float64
    ...

    [245760 values with dtype=float64]

  • temp
    (time, plev, ncells)
    float32
    ...

    standard_name :

    air_temperature

    long_name :

    Temperature

    units :

    K

    param :

    0.0.0

    CDI_grid_type :

    unstructured

    number_of_grid_in_reference :

    1

    [81920 values with dtype=float32]

Draw filled contours over a longitude-latitude subset. The helper constructs a triangulation from the ICON cell centers.

cf = plot_cell_contourf(
    data,
    data.temp.isel(time=0, plev=0),
    lon_min=110,
    lon_max=220,
    lat_min=25,
    lat_max=70,
    levels=12,
    cmap="RdBu_r",
    cbar_kwargs={"location": "bottom", "aspect": 60},
)

Pass an existing Cartopy axes when the contour should be combined with map decorations.

fig, ax = plt.subplots(
    subplot_kw={"projection": ccrs.PlateCarree(-120)}
)

cf = plot_cell_contourf(
    data,
    data.temp.isel(time=0, plev=0),
    ax=ax,
    transform=ccrs.PlateCarree(),
    lon_min=110,
    lon_max=220,
    lat_min=25,
    lat_max=70,
    levels=12,
    cmap="RdBu_r",
    cbar_kwargs={"location": "bottom", "aspect": 60},
)

ax.set_title("JW Wave 850hPa Temperature\n(plot_cell_contourf)")
ax.coastlines(resolution="110m", linewidth=0.6, color="k")
ax.gridlines(draw_labels=True, alpha=0)

<cartopy.mpl.gridliner.Gridliner object at 0x7354940a6f60>

Use plot_cell_contour when contour lines are preferred over filled contours.

fig, ax = plt.subplots()

cs = plot_cell_contour(
    data,
    data.temp.isel(time=0, plev=0),
    ax=ax,
    lon_min=110,
    lon_max=220,
    lat_min=25,
    lat_max=70,
    levels=12,
    colors="k",
)

ax.set_title("JW Wave 850hPa Temperature\n(plot_cell_contour)")

Text(0.5, 1.0, 'JW Wave 850hPa Temperature\n(plot_cell_contour)')

Contour labels and Cartopy map features can be added to the returned contour set in the usual Matplotlib style.

fig, ax = plt.subplots(
    subplot_kw={"projection": ccrs.PlateCarree(-120)}
)

cs = plot_cell_contour(
    data,
    data.temp.isel(time=0, plev=0),
    ax=ax,
    transform=ccrs.PlateCarree(),
    lon_min=110,
    lon_max=220,
    lat_min=25,
    lat_max=70,
    levels=12,
    colors="k",
)
ax.clabel(cs, inline=True, fontsize=8, fmt="%g")

ax.set_title("JW Wave 850hPa Temperature\n(plot_cell_contour)")
ax.coastlines(resolution="110m", linewidth=0.6, color="b")
ax.gridlines(draw_labels=True, alpha=0)

<cartopy.mpl.gridliner.Gridliner object at 0x73549420f260>