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 0x7fa0163c3580>
../../_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 0x7fa0160b1420>
../../_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 0x7fa016141e70>
../../_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 0x7fa0162698d0>
../../_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 0x7fa01e4ff2b0>
../../_images/notebooks_fileio_wradlib_rainbow_backend_21_1.png
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7fa01642fd00>
../../_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
    units:          dBZ
    long_name:      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 0x7fa016308910>
../../_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 0x7fa015ed0df0>
../../_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 0x7fa015f18490>
../../_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 0x7fa015dcd2d0>
../../_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 0x7fa015cfc550>
../../_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