xarray CfRadial1 backend¶
In this example, we read and write CfRadial1 data files using the xarray wradlib-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()
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.11/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 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) float32 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 ... longitude float64 ... altitude float64 ... 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 ... time datetime64[ns] 2008-06-04T00:15:03 Dimensions without coordinates: r_calib Data variables: (12/92) volume_number int32 ... platform_type |S32 ... primary_axis |S32 ... status_xml |S1 ... instrument_type |S32 ... radar_antenna_gain_h float32 ... ... ... r_calib_index (azimuth) int8 ... measured_transmit_power_h (azimuth) float32 ... measured_transmit_power_v (azimuth) float32 ... scan_rate (azimuth) float32 ... 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")

[9]:
fig = pl.figure(figsize=(10, 10))
swp.DBZ.wradlib.plot_ppi(proj="cg", fig=fig)
[9]:
<matplotlib.collections.QuadMesh at 0x7f84463f9590>

[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: +proj=aeqd +ellps=WGS84 +lon_0=120.43350219726562 +lat_0=22.52669906616211 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >

[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 0x7f843dbd9e90>

[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 0x7f843d81ac10>

[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 0x7f843d9e0290>

[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 0x7f843d737550>

[16]:
swp.DBZ.wradlib.plot_ppi()
[16]:
<matplotlib.collections.QuadMesh at 0x7f843d8d4b50>

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 0x7f843d7df190>

[19]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZ.wradlib.plot_ppi(proj={"latmin": 33e3}, fig=fig)

Mask some values¶
[20]:
swp["DBZ"] = swp["DBZ"].where(swp["DBZ"] >= 0)
swp["DBZ"].plot()
[20]:
<matplotlib.collections.QuadMesh at 0x7f843d863290>

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¶
Warning
Since \(\omega radlib\) version 1.18 the xarray backend engines for polar radar data have been renamed and prepended with wradlib-
(eg. cfradial1
-> wradlib-cfradial1
). 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="cfradial1"
.
[26]:
swp = xr.open_dataset(f, engine="wradlib-cfradial1", group="sweep_1")
display(swp)
<xarray.Dataset> Dimensions: (r_calib: 1, azimuth: 483, range: 996) Coordinates: latitude float64 ... longitude float64 ... altitude float64 ... sweep_mode <U20 ... rtime (azimuth) datetime64[ns] ... * range (range) float32 150.0 300.0 ... 1.494e+05 * azimuth (azimuth) float32 121.5 122.2 ... 123.0 elevation (azimuth) float32 ... time datetime64[ns] ... Dimensions without coordinates: r_calib Data variables: (12/92) volume_number int32 ... platform_type |S32 ... primary_axis |S32 ... status_xml |S1 ... instrument_type |S32 ... radar_antenna_gain_h float32 ... ... ... r_calib_index (azimuth) int8 ... measured_transmit_power_h (azimuth) float32 ... measured_transmit_power_v (azimuth) float32 ... scan_rate (azimuth) float32 ... DBZ (azimuth, range) float32 ... VR (azimuth, range) float32 ... Attributes: fixed_angle: 0.5