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')
[9]:
fig = pl.figure(figsize=(10,10))
swp.DBZ.wradlib.plot_ppi(proj='cg', fig=fig)
[9]:
<matplotlib.collections.QuadMesh at 0x7f4ff14e7d60>
[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 0x7f4ff11b2980>
[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 0x7f4ff1036110>
[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 0x7f4ff0ddd930>
[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 0x7f4ff0e18730>
[16]:
swp.DBZ.wradlib.plot_ppi()
[16]:
<matplotlib.collections.QuadMesh at 0x7f4ff0e39ab0>
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 0x7f4ff0e4c8e0>
[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 0x7f4ff985e380>
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