Formatting Coordinates

Note

Go to the end to download the full example code.

Formatting Coordinates#

Before proceeding with all the steps, first import some necessary libraries and packages

import easyclimate as ecl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import cartopy.crs as ccrs

The data we use here is monthly data from Jan 2022 to Feb 2022 and contains 17 vertical levels.

u_data = ecl.tutorial.open_tutorial_dataset("uwnd_202201_mon_mean").sortby("lat").uwnd
u_data

<xarray.DataArray 'uwnd' (time: 2, level: 17, lat: 73, lon: 144)> Size: 1MB
[357408 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 16B 2022-01-01 2022-02-01
  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * lat      (lat) float32 292B -90.0 -87.5 -85.0 -82.5 ... 82.5 85.0 87.5 90.0
  * level    (level) float32 68B 1e+03 925.0 850.0 700.0 ... 50.0 30.0 20.0 10.0
Attributes:
    long_name:     Monthly mean u wind
    units:         m/s
    precision:     2
    var_desc:      u-wind
    level_desc:    Pressure Levels
    statistic:     Mean
    parent_stat:   Other
    dataset:       NCEP Reanalysis Derived Products
    actual_range:  [-68.194824 124.399994]


Formatting of the Latitude and Lontitude Tickes#

draw_data1 is extracted from time level 0 and 500hPa vertical level.

draw_data1 = u_data.isel(time=0).sel(level=500)
draw_data1

<xarray.DataArray 'uwnd' (lat: 73, lon: 144)> Size: 42kB
[10512 values with dtype=float32]
Coordinates:
    time     datetime64[ns] 8B 2022-01-01
  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * lat      (lat) float32 292B -90.0 -87.5 -85.0 -82.5 ... 82.5 85.0 87.5 90.0
    level    float32 4B 500.0
Attributes:
    long_name:     Monthly mean u wind
    units:         m/s
    precision:     2
    var_desc:      u-wind
    level_desc:    Pressure Levels
    statistic:     Mean
    parent_stat:   Other
    dataset:       NCEP Reanalysis Derived Products
    actual_range:  [-68.194824 124.399994]


Now we call xarray.plot.pcolormesh to plot the latitudinal wind field on the 500hPa isobaric surface. Noting that the x-axis and y-axis are not in standard geographic coordinate format, our next step is to format these coordinates.

fig, ax = plt.subplots(1, 1)

draw_data1.plot.pcolormesh(
    ax=ax,
)
time = 2022-01-01, level = 500.0 [millibar]
<matplotlib.collections.QuadMesh object at 0x7f804da39ab0>

easyclimate.plot.set_lon_format_axis, easyclimate.plot.set_lat_format_axis can help us quickly format the coordinates on the x-axis and y-axis, respectively, into a geographic coordinate format.

fig, ax = plt.subplots(1, 1)

draw_data1.plot.pcolormesh(
    ax=ax,
)

ecl.plot.set_lon_format_axis(ax, axis="x")
ecl.plot.set_lat_format_axis(ax, axis="y")
time = 2022-01-01, level = 500.0 [millibar]

It is worth mentioning that the easyclimate.plot.set_lon_format_axis, easyclimate.plot.set_lat_format_axis methods contain a parameter dmi which helps us to convert DD (Decimal Degrees) format to DMS (Degrees Minutes Seconds) format.

Now let’s start by selecting a smaller area

draw_data1_1 = (
    u_data.isel(time=0).sel(level=500).sel(lon=slice(100, 110), lat=slice(20, 23))
)
draw_data1_1

<xarray.DataArray 'uwnd' (lat: 2, lon: 5)> Size: 40B
[10 values with dtype=float32]
Coordinates:
    time     datetime64[ns] 8B 2022-01-01
  * lon      (lon) float32 20B 100.0 102.5 105.0 107.5 110.0
  * lat      (lat) float32 8B 20.0 22.5
    level    float32 4B 500.0
Attributes:
    long_name:     Monthly mean u wind
    units:         m/s
    precision:     2
    var_desc:      u-wind
    level_desc:    Pressure Levels
    statistic:     Mean
    parent_stat:   Other
    dataset:       NCEP Reanalysis Derived Products
    actual_range:  [-68.194824 124.399994]


Note the difference in geo-labeling on the x-axis.

fig, ax = plt.subplots(1, 2, figsize=(15, 5))

for axi in ax.flat:
    draw_data1_1.plot(ax=axi, cmap="Reds")
    axi.xaxis.set_major_locator(ticker.LinearLocator(5))

ecl.plot.set_lon_format_axis(ax[0], axis="x")
ecl.plot.set_lat_format_axis(ax[0], axis="y")
ax[0].set_title("dms = False")

ecl.plot.set_lon_format_axis(ax[1], axis="x", dms=True)
ecl.plot.set_lat_format_axis(ax[1], axis="y", dms=True)
ax[1].set_title("dms = True")
dms = False, dms = True
Text(0.5, 1.0, 'dms = True')

Barometric Profile Label Formatting#

Here we choose the longitudinally averaged latitudinal direction at time level 0 to plot the profile

draw_data2 = u_data.isel(time=0).mean(dim="lon")

Notice that the x-axis and y-axis labels are unformatted, so we’ll take care of that next.

fig, ax = plt.subplots(1, 1)

draw_data2.plot.contourf(ax=ax, levels=21, yincrease=False)
time = 2022-01-01
<matplotlib.contour.QuadContourSet object at 0x7f804e168940>

easyclimate.plot.set_p_format_axis can help us format barometric vertical labels and similarly easyclimate.plot.set_lat_format_axis can help us format latitude labels.

fig, ax = plt.subplots(1, 1)

draw_data2.plot.contourf(ax=ax, levels=21, yincrease=False)

ecl.plot.set_lat_format_axis(ax, axis="x")
ecl.plot.set_p_format_axis(ax, axis="y")
time = 2022-01-01

Polar Stereo of the Circle Boundary#

For the sake of illustration, we use here the sea ice concentration (SIC) data from the Barents-Kara Seas (30°−90°E, 65°−85°N). The results of the data under the 10th time level are described below.

sic_data = ecl.tutorial.open_tutorial_dataset("mini_HadISST_ice").sic.isel(time=10)
sic_data.plot.contourf(cmap="Blues", levels=11)
time = 1981-11-30
<matplotlib.contour.QuadContourSet object at 0x7f804e6dc8e0>

easyclimate.plot.draw_Circlemap_PolarStereo helps us to easily establish the boundary of the circle under the projection of the polar stereo.

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

ax.coastlines(edgecolor="black", linewidths=0.5)
ax.stock_img()

ecl.plot.draw_Circlemap_PolarStereo(
    ax=ax,
    lon_step=30,
    lat_step=10,
    lat_range=[50, 90],
    draw_labels=True,
    gridlines_kwargs={"color": "grey", "alpha": 0.5, "linestyle": "--"},
)

sic_data.plot.contourf(cmap="Blues", levels=11, transform=ccrs.PlateCarree())
time = 1981-11-30
<cartopy.mpl.contour.GeoContourSet object at 0x7f804e4113f0>

Adjusting north_pad and south_pad appropriately can help us compensate for not completing the circle boundaries.

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

ax.coastlines(edgecolor="black", linewidths=0.5)
ax.stock_img()

ecl.plot.draw_Circlemap_PolarStereo(
    ax=ax,
    lon_step=30,
    lat_step=10,
    lat_range=[50, 90],
    draw_labels=True,
    set_map_boundary_kwargs={"north_pad": 0.3, "south_pad": 0.4},
    gridlines_kwargs={"color": "grey", "alpha": 0.5, "linestyle": "--"},
)

sic_data.plot(cmap="Blues", levels=11, transform=ccrs.PlateCarree())
time = 1981-11-30
<cartopy.mpl.geocollection.GeoQuadMesh object at 0x7f804e4a8190>

Total running time of the script: (0 minutes 6.548 seconds)