Plan Position Indicator#

A Plan Position Indicator (PPI) plot is a common plot requested by radar scientists. Let’s show how to create this plot using xradar

Imports#

import cmweather  # noqa
from open_radar_data import DATASETS

import xradar as xd

import cartopy
import matplotlib.pyplot as plt

Plot our Data#

Plot simple PPI#

Now, let’s create our PPI plot! We just use the newly created 2D-coordinates x and y to create a meshplot.

radar["sweep_0"]["DBZ"].plot(x="x", y="y", cmap="ChaseSpectral")

<matplotlib.collections.QuadMesh at 0x7ae350348050>

../_images/7b64fb9c3870707996c4d856e3f08315adc8ee7f7bae47726eae37d61b05bc98.png

Plot PPI with geographic coordinates#

fig, ax = plt.subplots(figsize=(10, 8))
radar = xd.georeference.get_x_y_z_tree(radar, target_crs=4326)
sweep = radar["sweep_0"]
sweep["DBZ"].plot(x="x", y="y", cmap="ChaseSpectral")
ax.grid(True, which="both", linestyle="--", color="gray", alpha=0.5)
ax.set_xlabel("Longitude")
ax.set_ylabel("Latitude")
ax.set_title("Radar Reflectivity Sweep")

Text(0.5, 1.0, 'Radar Reflectivity Sweep')

../_images/9b4fa7263304f1277d35c82adf2c85eaa3ec79b70b6ebf049a02a3c309a91a5c.png

Plot PPI on map with cartopy#

If you have cartopy installed, you can easily plot on maps. We first have to extract the CRS from the dataset and to wrap it in a cartopy.crs.Projection.

proj_crs = xd.georeference.get_crs(radar["sweep_0"].to_dataset(inherit="all_coords"))
cart_crs = cartopy.crs.Projection(proj_crs)

Second, we create a matplotlib GeoAxes and a nice map.

fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection=cartopy.crs.PlateCarree())
radar["sweep_0"]["DBZ"].plot(
    x="x",
    y="y",
    cmap="ChaseSpectral",
    transform=cart_crs,
    cbar_kwargs=dict(pad=0.075, shrink=0.75),
)
ax.coastlines()
ax.gridlines(draw_labels=True)

/home/docs/checkouts/readthedocs.org/user_builds/xradar/conda/stable/lib/python3.13/site-packages/cartopy/mpl/geoaxes.py:1762: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.
  result = super().pcolormesh(*args, **kwargs)

<cartopy.mpl.gridliner.Gridliner at 0x7ae35034aba0>

../_images/af0a3fe1cf8def39662a2f0785a0f688ca810ae872db7d92b9d920c8fcd23f4d.png