xarray CfRadial1 backend

In this example, we read and write CfRadial1 data files using the xarray cfradial1 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.nc'
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_cfradial1_dataset(f)

Fix several issues of CfRadial1 azimuth’s

[3]:

for i, swp in enumerate(vol):
    num_rays = int(360 // swp.azimuth.diff("azimuth").median())
    start_rays = swp.dims["azimuth"] - num_rays
    vol[i] = swp.isel(azimuth=slice(start_rays, start_rays + num_rays )).sortby("azimuth")

Inspect RadarVolume

[4]:

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).

[5]:

vol.root

[5]:

<xarray.Dataset>
Dimensions:              (sweep: 9)
Coordinates:
    longitude            float64 120.4
    altitude             float64 45.0
    sweep_mode           <U20 'azimuth_surveillance'
    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 '2008-06-04T00:22:17Z'
    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.).

[6]:

display(vol[0])

<xarray.Dataset>
Dimensions:                           (r_calib: 1, azimuth: 480, range: 996)
Coordinates:
    latitude                          float64 22.53
    longitude                         float64 120.4
    altitude                          float64 45.0
    sweep_mode                        <U20 'azimuth_surveillance'
    rtime                             (azimuth) datetime64[ns] 2008-06-04T00:...
  * range                             (range) float32 150.0 300.0 ... 1.494e+05
  * azimuth                           (azimuth) float32 0.0 0.75 ... 358.5 359.2
    elevation                         (azimuth) float32 0.5164 0.5219 ... 0.5219
    time                              datetime64[ns] 2008-06-04T00:15:03
Dimensions without coordinates: r_calib
Data variables: (12/92)
    volume_number                     int32 36
    platform_type                     |S32 b'fixed'
    primary_axis                      |S32 b'axis_z'
    status_xml                        |S1 b''
    instrument_type                   |S32 b'radar'
    radar_antenna_gain_h              float32 45.15
    ...                                ...
    r_calib_index                     (azimuth) int8 -1 -1 -1 -1 ... -1 -1 -1 -1
    measured_transmit_power_h         (azimuth) float32 -9.999e+03 ... -9.999...
    measured_transmit_power_v         (azimuth) float32 -9.999e+03 ... -9.999...
    scan_rate                         (azimuth) float32 -3.277e+04 ... -3.277...
    DBZ                               (azimuth, range) float32 ...
    VR                                (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.5

Goereferencing

[7]:

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

Plotting

[8]:

swp.DBZ.plot.pcolormesh(x='x', y='y')
pl.gca().set_aspect('equal')
../../_images/notebooks_fileio_wradlib_cfradial1_backend_16_0.png 
[9]:

fig = pl.figure(figsize=(10,10))
swp.DBZ.wradlib.plot_ppi(proj='cg', fig=fig)

[9]:

<matplotlib.collections.QuadMesh at 0x7fee86e1cd90>
../../_images/notebooks_fileio_wradlib_cfradial1_backend_17_1.png 
[10]:

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)

[11]:

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 0x7fee7ee1a360> >
../../_images/notebooks_fileio_wradlib_cfradial1_backend_19_1.png 
[12]:

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)

[12]:

<cartopy.mpl.gridliner.Gridliner at 0x7fee7554d4f0>
../../_images/notebooks_fileio_wradlib_cfradial1_backend_20_1.png 
[13]:

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)

[13]:

<cartopy.mpl.gridliner.Gridliner at 0x7fee754dff40>
../../_images/notebooks_fileio_wradlib_cfradial1_backend_21_1.png 
[14]:

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)

[14]:

<cartopy.mpl.gridliner.Gridliner at 0x7fee75476b50>
../../_images/notebooks_fileio_wradlib_cfradial1_backend_22_1.png 
[15]:

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()

[15]:

<cartopy.mpl.gridliner.Gridliner at 0x7fee7534cfd0>
../../_images/notebooks_fileio_wradlib_cfradial1_backend_23_1.png 
[16]:

swp.DBZ.wradlib.plot_ppi()

[16]:

<matplotlib.collections.QuadMesh at 0x7fee75239f70>
../../_images/notebooks_fileio_wradlib_cfradial1_backend_24_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.

[17]:

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)
    latitude    float64 22.53
    longitude   float64 120.4
    altitude    float64 45.0
    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
    ...          ...
    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.

[18]:

swp.DBZ.sortby('rtime').plot(x="range", y="rtime", add_labels=False)

[18]:

<matplotlib.collections.QuadMesh at 0x7fee75272e50>
../../_images/notebooks_fileio_wradlib_cfradial1_backend_28_1.png 
[19]:

fig = pl.figure(figsize=(5,5))
pm = swp.DBZ.wradlib.plot_ppi(proj={'latmin': 33e3}, fig=fig)
../../_images/notebooks_fileio_wradlib_cfradial1_backend_29_0.png

Mask some values

[20]:

swp['DBZ'] = swp['DBZ'].where(swp['DBZ'] >= 0)
swp['DBZ'].plot()

[20]:

<matplotlib.collections.QuadMesh at 0x7fee86dd2880>
../../_images/notebooks_fileio_wradlib_cfradial1_backend_31_1.png

Export to ODIM and CfRadial2

[21]:

vol.to_odim('cfradial1_as_odim.h5')
vol.to_cfradial2('cfradial1_as_cfradial2.nc')

Import again

[22]:

vola = wrl.io.open_odim_dataset('cfradial1_as_odim.h5', decode_coords=True,
                                backend_kwargs=dict(keep_azimuth=True,
                                                   keep_elevation=False))

[23]:

vola.root

[23]:

<xarray.Dataset>
Dimensions:              (sweep: 9)
Coordinates:
    time                 datetime64[ns] 2008-06-04T00:15:03
    sweep_mode           <U20 'azimuth_surveillance'
    longitude            float64 120.4
    altitude             float64 45.0
    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 '2008-06-04T00:22:17Z'
    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
[24]:

volb = wrl.io.open_cfradial2_dataset('cfradial1_as_cfradial2.nc')

Check equality

Some variables need to be dropped, since they are not exported to the other standards.

[25]:

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 CfRadial1 loading mechanisms

Use xr.open_dataset to retrieve explicit group

[26]:

swp = xr.open_dataset(f, engine="cfradial1", group="sweep_9")
display(swp)

<xarray.Dataset>
Dimensions:                           (r_calib: 1, azimuth: 483, range: 996)
Coordinates:
    latitude                          float64 22.53
    longitude                         float64 120.4
    altitude                          float64 45.0
    sweep_mode                        <U20 'azimuth_surveillance'
    rtime                             (azimuth) datetime64[ns] 2008-06-04T00:...
  * range                             (range) float32 150.0 300.0 ... 1.494e+05
  * azimuth                           (azimuth) float32 198.8 212.2 ... 213.0
    elevation                         (azimuth) float32 9.091 13.28 ... 12.81
    time                              datetime64[ns] 2008-06-04T00:21:28
Dimensions without coordinates: r_calib
Data variables: (12/92)
    volume_number                     int32 36
    platform_type                     |S32 b'fixed'
    primary_axis                      |S32 b'axis_z'
    status_xml                        |S1 b''
    instrument_type                   |S32 b'radar'
    radar_antenna_gain_h              float32 45.15
    ...                                ...
    r_calib_index                     (azimuth) int8 -1 -1 -1 -1 ... -1 -1 -1 -1
    measured_transmit_power_h         (azimuth) float32 -9.999e+03 ... -9.999...
    measured_transmit_power_v         (azimuth) float32 -9.999e+03 ... -9.999...
    scan_rate                         (azimuth) float32 -3.277e+04 ... -3.277...
    DBZ                               (azimuth, range) float32 ...
    VR                                (azimuth, range) float32 ...
Attributes:
    fixed_angle:  12.8