xarray CfRadial2 backend

In this example, we read CfRadial2 data files using the xarray cfradial2 backend.

[1]:
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 CfRadial1 Volume Data

[2]:
fpath = 'netcdf/cfrad.20080604_002217_000_SPOL_v36_SUR_cfradial2.nc'
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_cfradial2_dataset(f)

Inspect RadarVolume

[3]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 9)
Elevation(s): (0.5, 1.1, 1.8, 2.6, 3.6, 4.7, 6.5, 9.1, 12.8)

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: 9)
Coordinates:
    sweep_mode           <U20 'azimuth_surveillance'
    longitude            float64 120.4
    altitude             float64 45.0
    time                 datetime64[ns] 2008-06-04T00:15:03
    latitude             float64 22.53
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 '2008-06-04T00:15:03Z'
    time_coverage_end    <U20 '2008-06-04T00:22:16Z'
    sweep_group_name     (sweep) <U7 'sweep_0' 'sweep_1' ... 'sweep_7' 'sweep_8'
    sweep_fixed_angle    (sweep) float64 0.5 1.1 1.8 2.6 3.6 4.7 6.5 9.1 12.8
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: 480, range: 996)
Coordinates:
    sweep_mode          <U20 'azimuth_surveillance'
    rtime               (azimuth) datetime64[ns] 2008-06-04T00:15:34 ... 2008...
  * range               (range) float32 150.0 300.0 ... 1.492e+05 1.494e+05
  * azimuth             (azimuth) float32 0.0 0.75 1.5 ... 357.8 358.5 359.2
    elevation           (azimuth) float32 0.5164 0.5219 0.5164 ... 0.5219 0.5219
    longitude           float64 120.4
    latitude            float64 22.53
    altitude            float64 45.0
    time                datetime64[ns] 2008-06-04T00:15:03
Data variables: (12/16)
    sweep_number        int32 0
    polarization_mode   |S32 b'not_set'
    prt_mode            |S32 b'not_set'
    follow_mode         |S32 b'not_set'
    fixed_angle         float32 0.4999
    target_scan_rate    float32 -9.999e+03
    ...                  ...
    antenna_transition  (azimuth) int8 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
    n_samples           (azimuth) int32 192 192 192 192 192 ... 192 192 192 192
    r_calib_index       (azimuth) int8 -1 -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1 -1
    scan_rate           (azimuth) float32 -3.277e+04 -3.277e+04 ... -3.277e+04
    DBZ                 (azimuth, range) float32 ...
    VR                  (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.5

Goereferencing

[6]:
swp = vol[0].copy().pipe(wrl.georef.georeference_dataset)

Plotting

[7]:
swp.DBZ.plot.pcolormesh(x='x', y='y')
pl.gca().set_aspect('equal')
../../_images/notebooks_fileio_wradlib_cfradial2_backend_14_0.png
[8]:
fig = pl.figure(figsize=(10,10))
swp.DBZ.wradlib.plot_ppi(proj='cg', fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7fddeac23ac0>
../../_images/notebooks_fileio_wradlib_cfradial2_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.DBZ.wradlib.plot_ppi(proj=map_proj)
ax = pl.gca()
ax.gridlines(crs=map_proj)
print(ax)
< GeoAxes: <cartopy.crs.AzimuthalEquidistant object at 0x7fddeab14d60> >
../../_images/notebooks_fileio_wradlib_cfradial2_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.DBZ.wradlib.plot_ppi(ax=ax)
ax.gridlines(draw_labels=True)
[11]:
<cartopy.mpl.gridliner.Gridliner at 0x7fddea9e2160>
../../_images/notebooks_fileio_wradlib_cfradial2_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)

DBZ = swp.DBZ
pm = DBZ.where(DBZ > 0).wradlib.plot_ppi(ax=ax)
plot_borders(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7fddea8f9e80>
../../_images/notebooks_fileio_wradlib_cfradial2_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.DBZ.wradlib.plot_ppi(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[13]:
<cartopy.mpl.gridliner.Gridliner at 0x7fddeae22400>
../../_images/notebooks_fileio_wradlib_cfradial2_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.DBZ.wradlib.plot_ppi(ax=ax)
ax.gridlines()
[14]:
<cartopy.mpl.gridliner.Gridliner at 0x7fddea632cd0>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_21_1.png
[15]:
swp.DBZ.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7fddea715eb0>
../../_images/notebooks_fileio_wradlib_cfradial2_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. There are attributes connected which are defined by Cf/Radial standard.

[16]:
display(swp.DBZ)
<xarray.DataArray 'DBZ' (azimuth: 480, range: 996)>
array([[ 20.699957,  39.96934 ,  29.650644, ...,  -2.799595,  -3.549335,
         -1.650112],
       [ 13.829709,  35.710747,   8.869345, ..., -18.780428,  -3.080303,
         -4.519378],
       [ -9.129745,  14.810412,   4.539685, ...,   0.179822,  -0.550375,
         -3.519132],
       ...,
       [  5.889927,  26.049406,  32.379555, ...,  -2.550866,  -1.060269,
         -1.900617],
       [  0.959765,  23.579884,   9.29929 , ...,  -8.680257,  -5.039932,
         -2.410512],
       [ 20.079912,  39.15031 ,  13.190121, ...,  -4.91912 ,  -3.160252,
         -1.319658]], dtype=float32)
Coordinates: (12/15)
    sweep_mode  <U20 'azimuth_surveillance'
    rtime       (azimuth) datetime64[ns] 2008-06-04T00:15:34 ... 2008-06-04T0...
  * range       (range) float32 150.0 300.0 450.0 ... 1.492e+05 1.494e+05
  * azimuth     (azimuth) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2
    elevation   (azimuth) float32 0.5164 0.5219 0.5164 ... 0.5219 0.5219 0.5219
    longitude   float64 120.4
    ...          ...
    x           (azimuth, range) float32 -6.556e-06 -1.311e-05 ... -1.955e+03
    y           (azimuth, range) float32 150.0 300.0 ... 1.492e+05 1.493e+05
    z           (azimuth, range) float32 46.0 47.0 48.0 ... 2.714e+03 2.718e+03
    gr          (azimuth, range) float32 150.0 300.0 ... 1.492e+05 1.494e+05
    rays        (azimuth, range) float32 0.0 0.0 0.0 0.0 ... 359.2 359.2 359.2
    bins        (azimuth, range) float32 150.0 300.0 ... 1.492e+05 1.494e+05
Attributes:
    long_name:             Computed Horizontal Co-polar Reflectivit
    standard_name:         equivalent_reflectivity_factor
    units:                 dBZ
    threshold_field_name:
    threshold_value:       -9999.0
    sampling_ratio:        1.0
    grid_mapping:          grid_mapping

Create simple plot

Using xarray features a simple plot can be created like this. Note the sortby('time') method, which sorts the radials by time.

[17]:
swp.DBZ.sortby('rtime').plot(x="range", y="rtime", add_labels=False)
[17]:
<matplotlib.collections.QuadMesh at 0x7fddea678d30>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_26_1.png
[18]:
fig = pl.figure(figsize=(5,5))
pm = swp.DBZ.wradlib.plot_ppi(proj={'latmin': 33e3}, fig=fig)
../../_images/notebooks_fileio_wradlib_cfradial2_backend_27_0.png

Mask some values

[19]:
swp['DBZ'] = swp['DBZ'].where(swp['DBZ'] >= 0)
swp['DBZ'].plot()
[19]:
<matplotlib.collections.QuadMesh at 0x7fddea95cc10>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_29_1.png

Export to ODIM and CfRadial2

[20]:
vol.to_odim('cfradial2_as_odim.h5')
vol.to_cfradial2('cfradial2_as_cfradial2.nc')

Import again

[21]:
vola = wrl.io.open_odim_dataset('cfradial2_as_odim.h5')
[22]:
volb = wrl.io.open_cfradial2_dataset('cfradial2_as_cfradial2.nc')

Check equality

Some variables need to be dropped, since they are not exported to the other standards or differ slightly (eg. re-indexed ray times).

[23]:
drop = set(vol[0]) ^ set(vola[0]) | set({"elevation", "rtime"})
xr.testing.assert_allclose(vol.root, vola.root)
xr.testing.assert_allclose(vol[0].drop_vars(drop), vola[0].drop_vars(drop, errors="ignore"))
xr.testing.assert_allclose(vol.root, volb.root)
xr.testing.assert_equal(vol[0], volb[0])
xr.testing.assert_allclose(vola.root, volb.root)
xr.testing.assert_allclose(vola[0].drop_vars(drop, errors="ignore"), volb[0].drop_vars(drop, errors="ignore"))

More CfRadial2 loading mechanisms

Use xr.open_dataset to retrieve explicit group

[24]:
swp = xr.open_dataset(f, engine="cfradial2", group="sweep_8")
display(swp)
<xarray.Dataset>
Dimensions:             (azimuth: 480, range: 996)
Coordinates:
    sweep_mode          <U20 'azimuth_surveillance'
    rtime               (azimuth) datetime64[ns] 2008-06-04T00:21:49 ... 2008...
  * range               (range) float32 150.0 300.0 ... 1.492e+05 1.494e+05
  * azimuth             (azimuth) float32 0.0 0.75 1.5 ... 357.8 358.5 359.2
    elevation           (azimuth) float32 12.8 12.8 12.8 ... 12.79 12.79 12.8
    longitude           float64 120.4
    latitude            float64 22.53
    altitude            float64 45.0
    time                datetime64[ns] 2008-06-04T00:21:28
Data variables: (12/16)
    sweep_number        int32 8
    polarization_mode   |S32 b'not_set'
    prt_mode            |S32 b'not_set'
    follow_mode         |S32 b'not_set'
    fixed_angle         float32 12.8
    target_scan_rate    float32 -9.999e+03
    ...                  ...
    antenna_transition  (azimuth) int8 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
    n_samples           (azimuth) int32 192 192 192 192 192 ... 192 192 192 192
    r_calib_index       (azimuth) int8 -1 -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1 -1
    scan_rate           (azimuth) float32 -3.277e+04 -3.277e+04 ... -3.277e+04
    DBZ                 (azimuth, range) float32 ...
    VR                  (azimuth, range) float32 ...
Attributes:
    fixed_angle:  12.8