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")
../../_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 0x7fdc770caf90>
../../_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 0x7fdc76d56b90>
../../_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 0x7fdc76da3a90>
../../_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 0x7fdc77018090>
../../_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 0x7fdc76e9c610>
../../_images/notebooks_fileio_wradlib_iris_backend_21_1.png
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7fdc76e1cd50>
../../_images/notebooks_fileio_wradlib_iris_backend_22_1.png

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>
../../_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

Mask some values

[19]:
dbzh = swp["DBZH"].where(swp["DBZH"] >= 0)
dbzh.plot(x="x", y="y")
[19]:
<matplotlib.collections.QuadMesh at 0x7fdc76b74d90>
../../_images/notebooks_fileio_wradlib_iris_backend_29_1.png
[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>
../../_images/notebooks_fileio_wradlib_iris_backend_32_1.png
[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>
../../_images/notebooks_fileio_wradlib_iris_backend_35_3.png
[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>
../../_images/notebooks_fileio_wradlib_iris_backend_36_3.png

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