xarray Rainbow5 backend

In this example, we read Rainbow5 data files using the wradlib rainbow 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 Rainbow5 Volume Data

[2]:
fpath = "rainbow/2013051000000600dBZ.vol"
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_rainbow_dataset(f, reindex_angle=False)

Inspect RadarVolume

[3]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 14)
Elevation(s): (0.6, 1.4, 2.4, 3.5, 4.8, 6.3, 8.0, 9.9, 12.2, 14.8, 17.9, 21.3, 25.4, 30.0)

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: 14)
Coordinates:
    time                 datetime64[ns] 2013-05-10T00:00:06
    longitude            float64 6.38
    altitude             float64 116.7
    sweep_mode           <U20 'azimuth_surveillance'
    latitude             float64 50.86
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 '2013-05-10T00:00:06Z'
    time_coverage_end    <U20 '2013-05-10T00:03:14Z'
    sweep_group_name     (sweep) <U8 'sweep_0' 'sweep_1' ... 'sweep_13'
    sweep_fixed_angle    (sweep) float64 0.6 1.4 2.4 3.5 ... 17.9 21.3 25.4 30.0
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      0.6

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: 361, range: 400)
Coordinates:
  * azimuth     (azimuth) float64 46.52 47.51 48.51 49.52 ... 44.52 45.51 46.51
    elevation   (azimuth) float64 ...
  * range       (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    time        datetime64[ns] 2013-05-10T00:00:06
    rtime       (azimuth) datetime64[ns] 2013-05-10T00:00:06.015151500 ... 20...
    longitude   float64 ...
    latitude    float64 ...
    altitude    float64 ...
    sweep_mode  <U20 ...
Data variables:
    DBZH        (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.6

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_rainbow_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 0x7f49e09df1f0>
../../_images/notebooks_fileio_wradlib_rainbow_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=6.379967 +lat_0=50.856633 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >
../../_images/notebooks_fileio_wradlib_rainbow_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 0x7f49e08df7c0>
../../_images/notebooks_fileio_wradlib_rainbow_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 0x7f49e08a9600>
../../_images/notebooks_fileio_wradlib_rainbow_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 0x7f49dd45ceb0>
../../_images/notebooks_fileio_wradlib_rainbow_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 0x7f49dd2bbe20>
../../_images/notebooks_fileio_wradlib_rainbow_backend_21_1.png
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f49dd2edcf0>
../../_images/notebooks_fileio_wradlib_rainbow_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: 361, range: 400)>
array([[24.5, 11.5,  7.5, ...,  nan,  nan,  nan],
       [24.5, 10. ,  2.5, ...,  nan,  nan,  nan],
       [24.5, 12.5,  3.5, ...,  nan,  nan,  nan],
       ...,
       [25. ,  8. ,  7. , ...,  nan,  nan,  nan],
       [25. ,  9.5,  7.5, ...,  nan,  nan,  nan],
       [25.5, 12. ,  8. , ...,  nan,  nan,  nan]], dtype=float32)
Coordinates: (12/15)
  * azimuth     (azimuth) float64 46.52 47.51 48.51 49.52 ... 44.52 45.51 46.51
    elevation   (azimuth) float64 0.6 0.6 0.6 0.6 0.6 ... 0.6 0.6 0.6 0.6 0.6
  * range       (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    time        datetime64[ns] 2013-05-10T00:00:06
    rtime       (azimuth) datetime64[ns] 2013-05-10T00:00:06.015151500 ... 20...
    longitude   float64 6.38
    ...          ...
    x           (azimuth, range) float64 90.69 272.1 ... 7.225e+04 7.244e+04
    y           (azimuth, range) float64 86.01 258.0 ... 6.855e+04 6.873e+04
    z           (azimuth, range) float64 118.0 120.6 ... 1.744e+03 1.75e+03
    gr          (azimuth, range) float64 124.6 374.6 ... 9.96e+04 9.985e+04
    rays        (azimuth, range) float64 46.52 46.52 46.52 ... 46.51 46.51 46.51
    bins        (azimuth, range) float32 125.0 375.0 ... 9.962e+04 9.988e+04
Attributes:
    standard_name:  radar_equivalent_reflectivity_factor_h
    long_name:      Equivalent reflectivity factor H
    units:          dBZ

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 0x7f49dd31e7a0>
../../_images/notebooks_fileio_wradlib_rainbow_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_rainbow_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 0x7f49e8c7cdf0>
../../_images/notebooks_fileio_wradlib_rainbow_backend_29_1.png
[20]:
vol[0]
[20]:
<xarray.Dataset>
Dimensions:     (azimuth: 361, range: 400)
Coordinates:
  * azimuth     (azimuth) float64 46.52 47.51 48.51 49.52 ... 44.52 45.51 46.51
    elevation   (azimuth) float64 ...
  * range       (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    time        datetime64[ns] 2013-05-10T00:00:06
    rtime       (azimuth) datetime64[ns] 2013-05-10T00:00:06.015151500 ... 20...
    longitude   float64 ...
    latitude    float64 ...
    altitude    float64 ...
    sweep_mode  <U20 ...
Data variables:
    DBZH        (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.6

Export to ODIM and CfRadial2

[21]:
vol[0].DBZH.sortby("rtime").plot(y="rtime")
[21]:
<matplotlib.collections.QuadMesh at 0x7f49dd2223e0>
../../_images/notebooks_fileio_wradlib_rainbow_backend_32_1.png
[22]:
vol.to_odim("rainbow_as_odim.h5")
vol.to_cfradial2("rainbow_as_cfradial2.nc")

Import again

[23]:
vola = wrl.io.open_odim_dataset(
    "rainbow_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: 14)
Coordinates:
    time                 datetime64[ns] 2013-05-10T00:00:06
    sweep_mode           <U20 'azimuth_surveillance'
    longitude            float64 6.38
    altitude             float64 116.7
    latitude             float64 50.86
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 '2013-05-10T00:00:06Z'
    time_coverage_end    <U20 '2013-05-10T00:03:14Z'
    sweep_group_name     (sweep) <U8 'sweep_0' 'sweep_1' ... 'sweep_13'
    sweep_fixed_angle    (sweep) float64 0.6 1.4 2.4 3.5 ... 17.9 21.3 25.4 30.0
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      0.6
<xarray.Dataset>
Dimensions:     (azimuth: 361, range: 400)
Coordinates:
  * azimuth     (azimuth) float64 -0.4945 0.5492 1.505 ... 356.5 357.5 358.5
    elevation   (azimuth) float64 ...
    rtime       (azimuth) datetime64[ns] 2013-05-10T00:00:15.500000 ... 2013-...
  * range       (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    time        datetime64[ns] 2013-05-10T00:00:06
    sweep_mode  <U20 ...
    longitude   float64 ...
    latitude    float64 ...
    altitude    float64 ...
Data variables:
    DBZH        (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.6
[23]:
<matplotlib.collections.QuadMesh at 0x7f49dcbf35b0>
../../_images/notebooks_fileio_wradlib_rainbow_backend_35_3.png
[24]:
volb = wrl.io.open_cfradial2_dataset("rainbow_as_cfradial2.nc")
display(volb.root)
display(volb[0])
volb[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset>
Dimensions:              (sweep: 14)
Coordinates:
    longitude            float64 6.38
    altitude             float64 116.7
    sweep_mode           <U20 'azimuth_surveillance'
    time                 datetime64[ns] 2013-05-10T00:00:06
    latitude             float64 50.86
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 '2013-05-10T00:00:06Z'
    time_coverage_end    <U20 '2013-05-10T00:03:14Z'
    sweep_group_name     (sweep) <U8 'sweep_0' 'sweep_1' ... 'sweep_13'
    sweep_fixed_angle    (sweep) float64 0.6 1.4 2.4 3.5 ... 17.9 21.3 25.4 30.0
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      0.6
<xarray.Dataset>
Dimensions:     (azimuth: 361, range: 400)
Coordinates:
  * azimuth     (azimuth) float64 -0.4945 0.5492 1.505 ... 356.5 357.5 358.5
    elevation   (azimuth) float64 ...
  * range       (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    rtime       (azimuth) datetime64[ns] 2013-05-10T00:00:15.500000 ... 2013-...
    longitude   float64 ...
    latitude    float64 ...
    altitude    float64 ...
    sweep_mode  <U20 ...
    time        datetime64[ns] 2013-05-10T00:00:06
Data variables:
    DBZH        (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.6
[24]:
<matplotlib.collections.QuadMesh at 0x7f49dca2a770>
../../_images/notebooks_fileio_wradlib_rainbow_backend_36_3.png

Check equality

We have to sort accordingly and drop the time variables when checking equality.

[25]:
xr.testing.assert_allclose(vol.root, vola.root)
xr.testing.assert_allclose(
    vol[0].drop(["rtime", "time"]), vola[0].sortby("rtime").drop(["rtime", "time"])
)
xr.testing.assert_allclose(vol.root.drop("time"), volb.root.drop("time"))
xr.testing.assert_allclose(
    vol[0].drop(["rtime", "time"]), volb[0].sortby("rtime").drop(["rtime", "time"])
)
xr.testing.assert_allclose(vola.root, volb.root)
xr.testing.assert_allclose(vola[0].drop("rtime"), volb[0].drop(["rtime"]))

More Rainbow5 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. rainbow -> wradlib-rainbow). 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="rainbow".

[26]:
swp = xr.open_dataset(
    f, engine="wradlib-rainbow", group=5, backend_kwargs=dict(reindex_angle=False)
)
display(swp)
<xarray.Dataset>
Dimensions:     (azimuth: 361, range: 400)
Coordinates:
  * azimuth     (azimuth) float64 166.5 167.5 168.5 169.5 ... 164.5 165.5 166.5
    elevation   (azimuth) float64 ...
  * range       (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    time        datetime64[ns] ...
    rtime       (azimuth) datetime64[ns] ...
    longitude   float64 ...
    latitude    float64 ...
    altitude    float64 ...
    sweep_mode  <U20 ...
Data variables:
    DBZH        (azimuth, range) float32 ...
Attributes:
    fixed_angle:  6.3