xarray IRIS backend¶

In this example, we read IRIS (sigmet) data files using the wradlib iris xarray backend.

[1]:

import glob
import gzip
import io
import wradlib as wrl
import warnings

warnings.filterwarnings("ignore")
import matplotlib.pyplot as pl
import numpy as np
import xarray as xr

try:
    get_ipython().magic("matplotlib inline")
except:
    pl.ion()

/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Load IRIS Volume Data¶

[2]:

fpath = "sigmet/SUR210819000227.RAWKPJV"
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_iris_dataset(f, reindex_angle=False)

Inspect RadarVolume¶

[3]:

display(vol)

<wradlib.RadarVolume>
Dimension(s): (sweep: 1)
Elevation(s): (0.5)

Goereferencing¶

[6]:

swp = vol[0].copy().pipe(wrl.georef.georeference_dataset)

Plotting¶

[7]:

swp.DBZH.plot.pcolormesh(x="x", y="y")
pl.gca().set_aspect("equal")

../../_images/notebooks_fileio_wradlib_iris_backend_14_0.png

[8]:

fig = pl.figure(figsize=(10, 10))
swp.DBZH.wradlib.plot_ppi(proj="cg", fig=fig)

[8]:

<matplotlib.collections.QuadMesh at 0x7f00a05ba950>

../../_images/notebooks_fileio_wradlib_iris_backend_15_1.png

[9]:

import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature

map_trans = ccrs.AzimuthalEquidistant(
    central_latitude=swp.latitude.values, central_longitude=swp.longitude.values
)

[10]:

map_proj = ccrs.AzimuthalEquidistant(
    central_latitude=swp.latitude.values, central_longitude=swp.longitude.values
)
pm = swp.DBZH.wradlib.plot_ppi(proj=map_proj)
ax = pl.gca()
ax.gridlines(crs=map_proj)
print(ax)

< GeoAxes: +proj=aeqd +ellps=WGS84 +lon_0=25.518660116940737 +lat_0=58.48231002688408 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >

../../_images/notebooks_fileio_wradlib_iris_backend_17_1.png

[11]:

map_proj = ccrs.Mercator(central_longitude=swp.longitude.values)
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection=map_proj)
pm = swp.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines(draw_labels=True)

[11]:

<cartopy.mpl.gridliner.Gridliner at 0x7f00a03df880>

../../_images/notebooks_fileio_wradlib_iris_backend_18_1.png

[12]:

import cartopy.feature as cfeature


def plot_borders(ax):
    borders = cfeature.NaturalEarthFeature(
        category="physical", name="coastline", scale="10m", facecolor="none"
    )
    ax.add_feature(borders, edgecolor="black", lw=2, zorder=4)


map_proj = ccrs.Mercator(central_longitude=swp.longitude.values)
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection=map_proj)

DBZH = swp.DBZH
pm = DBZH.where(DBZH > 0).wradlib.plot_ppi(ax=ax)
plot_borders(ax)
ax.gridlines(draw_labels=True)

[12]:

<cartopy.mpl.gridliner.Gridliner at 0x7f00a0442aa0>

../../_images/notebooks_fileio_wradlib_iris_backend_19_1.png

[13]:

import matplotlib.path as mpath

theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)

map_proj = ccrs.AzimuthalEquidistant(
    central_latitude=swp.latitude.values,
    central_longitude=swp.longitude.values,
)
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection=map_proj)
ax.set_boundary(circle, transform=ax.transAxes)

pm = swp.DBZH.wradlib.plot_ppi(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)

[13]:

<cartopy.mpl.gridliner.Gridliner at 0x7f00a015bbe0>

../../_images/notebooks_fileio_wradlib_iris_backend_20_1.png

[14]:

fig = pl.figure(figsize=(10, 8))
proj = ccrs.AzimuthalEquidistant(
    central_latitude=swp.latitude.values, central_longitude=swp.longitude.values
)
ax = fig.add_subplot(111, projection=proj)
pm = swp.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines()

[14]:

<cartopy.mpl.gridliner.Gridliner at 0x7f0099fb35e0>

../../_images/notebooks_fileio_wradlib_iris_backend_21_1.png

[15]:

swp.DBZH.wradlib.plot_ppi()

[15]:

<matplotlib.collections.QuadMesh at 0x7f00a0159240>

../../_images/notebooks_fileio_wradlib_iris_backend_22_1.png

Create simple plot¶

Using xarray features a simple plot can be created like this. Note the sortby('rtime') method, which sorts the radials by time.

[17]:

swp.DBZH.sortby("rtime").plot(x="range", y="rtime", add_labels=False)

[17]:

<matplotlib.collections.QuadMesh at 0x7f00a984c610>

../../_images/notebooks_fileio_wradlib_iris_backend_26_1.png

[18]:

fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wradlib.plot_ppi(proj={"latmin": 3e3}, fig=fig)

../../_images/notebooks_fileio_wradlib_iris_backend_27_0.png

Export to ODIM and CfRadial2¶

Need to remove DB_XHDR since it can’t be represented as ODIM/CfRadial2 moment.

[21]:

vol[0] = vol[0].drop("DB_XHDR", errors="ignore")
vol[0].DBZH.sortby("rtime").plot(y="rtime")

[21]:

<matplotlib.collections.QuadMesh at 0x7f0099e067d0>

../../_images/notebooks_fileio_wradlib_iris_backend_32_1.png

[22]:

vol.to_odim("iris_as_odim.h5")
vol.to_cfradial2("iris_as_cfradial2.nc")

Check equality¶

We have to drop the time variable when checking equality since IRIS has millisecond resolution.

[25]:

xr.testing.assert_allclose(vol.root.drop("time"), vola.root.drop("time"))
xr.testing.assert_allclose(
    vol[0].drop(["rtime", "time", "DB_HCLASS2"]), vola[0].drop(["rtime", "time"])
)
xr.testing.assert_allclose(vol.root.drop("time"), volb.root.drop("time"))
xr.testing.assert_allclose(vol[0].drop("time"), volb[0].drop("time"))
xr.testing.assert_allclose(vola.root, volb.root)
xr.testing.assert_allclose(vola[0].drop("rtime"), volb[0].drop(["rtime", "DB_HCLASS2"]))

xarray IRIS backend¶

Load IRIS Volume Data¶

Inspect RadarVolume¶

Inspect root group¶

Inspect sweep group(s)¶

Goereferencing¶

Plotting¶

Inspect radar moments¶

Create simple plot¶

Mask some values¶

Export to ODIM and CfRadial2¶

Import again¶

Check equality¶

More Iris loading mechanisms¶

Use `xr.open_dataset` to retrieve explicit group¶

xarray IRIS backend¶

Load IRIS Volume Data¶

Inspect RadarVolume¶

Inspect root group¶

Inspect sweep group(s)¶

Goereferencing¶

Plotting¶

Inspect radar moments¶

Create simple plot¶

Mask some values¶

Export to ODIM and CfRadial2¶

Import again¶

Check equality¶

More Iris loading mechanisms¶

Use xr.open_dataset to retrieve explicit group¶

Use `xr.open_dataset` to retrieve explicit group¶