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: 0.5Inspect 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: 0.5Goereferencing¶
[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 0x7efd0e76e110>
[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 0x7efd0e63cc70>
[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 0x7efd0e4b8580>
[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 0x7efd0e4051e0>
[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 0x7efd0e292a40>
[16]:
swp.DBZ.wradlib.plot_ppi()
[16]:
<matplotlib.collections.QuadMesh at 0x7efd0e2afdc0>
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_mappingCreate 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 0x7efd0e2d61a0>
[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 0x7efd16be86d0>
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