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()

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 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
    latitude    float64 50.86
    altitude    float64 116.7
    sweep_mode  <U20 'azimuth_surveillance'
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 0x7fc54a85bfa0>
../../_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 0x7fc54a606c10>
../../_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 0x7fc54a5968e0>
../../_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 0x7fc542e8ff70>
../../_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 0x7fc54a6504f0>
../../_images/notebooks_fileio_wradlib_rainbow_backend_21_1.png
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7fc54a8918b0>
../../_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:
  * 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
    latitude    float64 50.86
    altitude    float64 116.7
    sweep_mode  <U20 'azimuth_surveillance'
    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:
    units:          dBZ
    long_name:      Equivalent reflectivity factor H
    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 0x7fc552aca820>
../../_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 0x7fc54a3a7dc0>
../../_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 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
    latitude    float64 50.86
    altitude    float64 116.7
    sweep_mode  <U20 'azimuth_surveillance'
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 0x7fc54a318100>
../../_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 0.6 0.6 0.6 0.6 0.6 ... 0.6 0.6 0.6 0.6 0.6
    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 'azimuth_surveillance'
    longitude   float64 6.38
    latitude    float64 50.86
    altitude    float64 116.7
Data variables:
    DBZH        (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.6
[23]:
<matplotlib.collections.QuadMesh at 0x7fc54a189520>
../../_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 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
    rtime       (azimuth) datetime64[ns] 2013-05-10T00:00:15.500000 ... 2013-...
    longitude   float64 6.38
    latitude    float64 50.86
    altitude    float64 116.7
    sweep_mode  <U20 'azimuth_surveillance'
    time        datetime64[ns] 2013-05-10T00:00:06
Data variables:
    DBZH        (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.6
[24]:
<matplotlib.collections.QuadMesh at 0x7fc54a0f1d60>
../../_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

[26]:
swp = xr.open_dataset(f, engine="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 6.3 6.3 6.3 6.3 6.3 ... 6.3 6.3 6.3 6.3 6.3
  * range       (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    time        datetime64[ns] 2013-05-10T00:01:14
    rtime       (azimuth) datetime64[ns] 2013-05-10T00:01:14.015151500 ... 20...
    longitude   float64 6.38
    latitude    float64 50.86
    altitude    float64 116.7
    sweep_mode  <U20 'azimuth_surveillance'
Data variables:
    DBZH        (azimuth, range) float32 ...
Attributes:
    fixed_angle:  6.3