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/python3.11/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)
Inspect root group¶
The sweep
dimension contains the number of scans in this radar volume. Further the dataset consists of variables (location coordinates, time_coverage) and attributes (Conventions, metadata).
[4]:
vol.root
[4]:
<xarray.Dataset> Dimensions: (sweep: 1) Coordinates: time datetime64[ns] 2021-08-19T00:02:27.432000 longitude float64 25.52 altitude float64 157.0 sweep_mode <U20 'azimuth_surveillance' latitude float64 58.48 Dimensions without coordinates: sweep Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' primary_axis <U6 'axis_z' time_coverage_start <U20 '2021-08-19T00:02:28Z' time_coverage_end <U20 '2021-08-19T00:02:49Z' sweep_group_name (sweep) <U7 'sweep_0' sweep_fixed_angle (sweep) float64 0.5 Attributes: version: None title: None institution: None references: None source: None history: None comment: im/exported using wradlib instrument_name: None fixed_angle: 0.5
Inspect sweep group(s)¶
The sweep-groups can be accessed via their respective keys. The dimensions consist of range
and time
with added coordinates azimuth
, elevation
, range
and time
. There will be variables like radar moments (DBZH etc.) and sweep-dependend metadata (like fixed_angle
, sweep_mode
etc.).
[5]:
display(vol[0])
<xarray.Dataset> Dimensions: (azimuth: 359, range: 833) Coordinates: * azimuth (azimuth) float32 0.03021 1.035 2.054 ... 357.0 358.0 359.0 elevation (azimuth) float32 ... rtime (azimuth) datetime64[ns] 2021-08-19T00:02:31.104000 ... 2021-... time datetime64[ns] 2021-08-19T00:02:27.432000 * range (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05 longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... Data variables: DB_XHDR (azimuth, range) object ... DBTH (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... SQIH (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... DB_HCLASS2 (azimuth, range) int16 ... SNRH (azimuth, range) float32 ... Attributes: fixed_angle: 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")

[8]:
fig = pl.figure(figsize=(10, 10))
swp.DBZH.wradlib.plot_ppi(proj="cg", fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7fdc770caf90>

[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 >

[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 0x7fdc76d56b90>

[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 0x7fdc76da3a90>

[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 0x7fdc77018090>

[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 0x7fdc76e9c610>

[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7fdc76e1cd50>

Inspect radar moments¶
The DataArrays can be accessed by key or by attribute. Each DataArray has dimensions and coordinates of it’s parent dataset.
[16]:
display(swp.DBZH)
<xarray.DataArray 'DBZH' (azimuth: 359, range: 833)> array([[-327.68, -327.68, -327.68, ..., -327.68, -327.68, -327.68], [-327.68, 3.39, 6.45, ..., -327.68, -327.68, -327.68], [ -8.86, 5.25, 8.58, ..., -327.68, -327.68, -327.68], ..., [-327.68, -327.68, -327.68, ..., -327.68, -327.68, -327.68], [-327.68, 4.75, 10.95, ..., -327.68, -327.68, -327.68], [-327.68, -327.68, 4.94, ..., -327.68, -327.68, -327.68]]) Coordinates: (12/15) * azimuth (azimuth) float32 0.03021 1.035 2.054 ... 357.0 358.0 359.0 elevation (azimuth) float64 0.5054 0.5054 0.5054 ... 0.5054 0.5054 0.5054 rtime (azimuth) datetime64[ns] 2021-08-19T00:02:31.104000 ... 2021-... time datetime64[ns] 2021-08-19T00:02:27.432000 * range (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05 longitude float64 25.52 ... ... x (azimuth, range) float64 0.0791 0.2373 ... -4.206e+03 -4.211e+03 y (azimuth, range) float64 150.0 450.0 ... 2.493e+05 2.496e+05 z (azimuth, range) float64 158.3 161.0 ... 6.023e+03 6.034e+03 gr (azimuth, range) float64 150.0 450.0 ... 2.493e+05 2.496e+05 rays (azimuth, range) float32 0.03021 0.03021 0.03021 ... 359.0 359.0 bins (azimuth, range) float32 150.0 450.0 ... 2.494e+05 2.498e+05 Attributes: long_name: Equivalent reflectivity factor H units: dBZ standard_name: radar_equivalent_reflectivity_factor_h
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 0x7fdc76d10c90>

[18]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wradlib.plot_ppi(proj={"latmin": 3e3}, fig=fig)

Mask some values¶
[19]:
dbzh = swp["DBZH"].where(swp["DBZH"] >= 0)
dbzh.plot(x="x", y="y")
[19]:
<matplotlib.collections.QuadMesh at 0x7fdc76b74d90>

[20]:
vol[0]
[20]:
<xarray.Dataset> Dimensions: (azimuth: 359, range: 833) Coordinates: * azimuth (azimuth) float32 0.03021 1.035 2.054 ... 357.0 358.0 359.0 elevation (azimuth) float32 ... rtime (azimuth) datetime64[ns] 2021-08-19T00:02:31.104000 ... 2021-... time datetime64[ns] 2021-08-19T00:02:27.432000 * range (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05 longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... Data variables: DB_XHDR (azimuth, range) object ... DBTH (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... SQIH (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... DB_HCLASS2 (azimuth, range) int16 ... SNRH (azimuth, range) float32 ... Attributes: fixed_angle: 0.5
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 0x7fdc769e7290>

[22]:
vol.to_odim("iris_as_odim.h5")
vol.to_cfradial2("iris_as_cfradial2.nc")
Import again¶
[23]:
vola = wrl.io.open_odim_dataset(
"iris_as_odim.h5", reindex_angle=False, keep_elevation=True
)
display(vola.root)
display(vola[0])
vola[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset> Dimensions: (sweep: 1) Coordinates: time datetime64[ns] 2021-08-19T00:02:28 sweep_mode <U20 'azimuth_surveillance' longitude float64 25.52 altitude float64 157.0 latitude float64 58.48 Dimensions without coordinates: sweep Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' primary_axis <U6 'axis_z' time_coverage_start <U20 '2021-08-19T00:02:28Z' time_coverage_end <U20 '2021-08-19T00:02:49Z' sweep_group_name (sweep) <U7 'sweep_0' sweep_fixed_angle (sweep) float64 0.5 Attributes: version: None title: None institution: None references: None source: None history: None comment: im/exported using wradlib instrument_name: None fixed_angle: 0.5
<xarray.Dataset> Dimensions: (azimuth: 359, range: 833) Coordinates: * azimuth (azimuth) float32 0.03021 1.035 2.054 ... 357.0 358.0 359.0 elevation (azimuth) float64 ... rtime (azimuth) datetime64[ns] 2021-08-19T00:02:31.104000 ... 2021-... * range (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05 time datetime64[ns] 2021-08-19T00:02:28 sweep_mode <U20 ... longitude float64 ... latitude float64 ... altitude float64 ... Data variables: DBTH (azimuth, range) float32 ... SNRH (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... SQIH (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... Attributes: fixed_angle: 0.5
[23]:
<matplotlib.collections.QuadMesh at 0x7fdc6c400090>

[24]:
volb = wrl.io.open_cfradial2_dataset("iris_as_cfradial2.nc")
display(volb.root)
display(volb[0])
volb[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset> Dimensions: (sweep: 1) Coordinates: longitude float64 25.52 altitude float64 157.0 sweep_mode <U20 'azimuth_surveillance' time datetime64[ns] 2021-08-19T00:02:28 latitude float64 58.48 Dimensions without coordinates: sweep Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' primary_axis <U6 'axis_z' time_coverage_start <U20 '2021-08-19T00:02:28Z' time_coverage_end <U20 '2021-08-19T00:02:49Z' sweep_group_name (sweep) <U7 'sweep_0' sweep_fixed_angle (sweep) float64 0.5 Attributes: version: None title: None institution: None references: None source: None history: None comment: im/exported using wradlib instrument_name: None fixed_angle: 0.5
<xarray.Dataset> Dimensions: (azimuth: 359, range: 833) Coordinates: * azimuth (azimuth) float32 0.03021 1.035 2.054 ... 357.0 358.0 359.0 elevation (azimuth) float32 ... rtime (azimuth) datetime64[ns] 2021-08-19T00:02:31.104000 ... 2021-... * range (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05 longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... time datetime64[ns] 2021-08-19T00:02:28 Data variables: DBTH (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... SQIH (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... DB_HCLASS2 (azimuth, range) int16 ... SNRH (azimuth, range) float32 ... Attributes: fixed_angle: 0.5
[24]:
<matplotlib.collections.QuadMesh at 0x7fdc6c1384d0>

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"]))
More Iris loading mechanisms¶
Use xr.open_dataset
to retrieve explicit group¶
Warning
Since \(\omega radlib\) version 1.18 the xarray backend engines for polar radar data have been renamed and prepended with wradlib-
(eg. iris
-> wradlib-iris
). This was necessary to avoid clashes with the new xradar-package, which will eventually replace the wradlib engines. Users have to make sure to check which engine to use for their use-case when using xarray.open_dataset
. Users might install and test xradar
, and check if it is
already robust enough for their use-cases (by using xradar’s engine="iris"
.
[26]:
swp = xr.open_dataset(
f, engine="wradlib-iris", group=1, backend_kwargs=dict(reindex_angle=False)
)
display(swp)
<xarray.Dataset> Dimensions: (azimuth: 359, range: 833) Coordinates: * azimuth (azimuth) float32 0.03021 1.035 2.054 ... 357.0 358.0 359.0 elevation (azimuth) float32 ... rtime (azimuth) datetime64[ns] ... time datetime64[ns] ... * range (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05 longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... Data variables: DB_XHDR (azimuth, range) object ... DBTH (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... SQIH (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... DB_HCLASS2 (azimuth, range) int16 ... SNRH (azimuth, range) float32 ... Attributes: fixed_angle: 0.5