xarray furuno backend¶
In this example, we read scn/scnx (furuno) data files using the wradlib furuno
xarray backend.
Furuno Weather Radars generate binary files. The binary version depend on the radar type. This reader is able to consume SCN (format version 3) and SCNX (format version 10) files.
Uncompressed files are read via numpy.memmap
with lazy-loading mechanism. Gzip compressed files are are opened, read into memory and processed using numpy.frombuffer
.
Radar moments are read as packed data with 16-bit resolution and output as 32bit-floating point data.
[1]:
import glob
import gzip
import io
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 furuno scn Data¶
Data provided by University of Graz, Austria.
[2]:
fpath = "furuno/0080_20210730_160000_01_02.scn.gz"
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_furuno_dataset(f, reindex_angle=False)
Inspect RadarVolume¶
[3]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 1)
Elevation(s): (7.8)
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: 1) Coordinates: time datetime64[ns] 2021-07-30T16:00:00 longitude float64 15.45 altitude float64 407.9 sweep_mode <U20 'azimuth_surveillance' latitude float64 47.08 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 '2021-07-30T16:00:00Z' time_coverage_end <U20 '2021-07-30T16:00:14Z' sweep_group_name (sweep) <U7 'sweep_0' sweep_fixed_angle (sweep) float64 7.8 Attributes: version: None title: None institution: None references: None source: None history: None comment: im/exported using wradlib instrument_name: None fixed_angle: 7.8
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: 1376, range: 602) Coordinates: * azimuth (azimuth) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 ... * range (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04 time datetime64[ns] 2021-07-30T16:00:00 rtime (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20... longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... QUAL (azimuth, range) uint16 ... Attributes: fixed_angle: 7.8
Goereferencing¶
[6]:
swp = vol[0].copy().pipe(wrl.georef.georeference_dataset)
Plotting¶
Currently the data dynamic range is left as read from the file. That way the difference between shortpulse and longpulse can be clearly seen.
[7]:
swp.DBZH.plot.pcolormesh(x="x", y="y")
pl.gca().set_aspect("equal")

[8]:
fig = pl.figure(figsize=(10, 10))
swp.DBZH.wradlib.plot_ppi(proj="cg", fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7fd8f92ea350>

[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.DBZH.wradlib.plot_ppi(proj=map_proj)
ax = pl.gca()
ax.gridlines(crs=map_proj)
print(ax)
< GeoAxes: +proj=aeqd +ellps=WGS84 +lon_0=15.44729 +lat_0=47.07734000000001 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >

[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.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines(draw_labels=True)
[11]:
<cartopy.mpl.gridliner.Gridliner at 0x7fd8f81d0c90>

[12]:
import cartopy.feature as cfeature
def plot_rivers(ax):
rivers = cfeature.NaturalEarthFeature(
category="physical",
name="rivers_lake_centerlines",
scale="10m",
facecolor="none",
)
ax.add_feature(rivers, edgecolor="blue", 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)
DBZH = swp.DBZH
pm = DBZH.where(DBZH > 0).wradlib.plot_ppi(ax=ax)
plot_rivers(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7fd8f1fe9d10>

[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.DBZH.wradlib.plot_ppi(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[13]:
<cartopy.mpl.gridliner.Gridliner at 0x7fd8f925d110>

[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.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines()
[14]:
<cartopy.mpl.gridliner.Gridliner at 0x7fd8f1e5fa10>

[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7fd8f1ea0c50>

Inspect radar moments¶
The DataArrays can be accessed by key or by attribute. Each DataArray has dimensions and coordinates of it’s parent dataset.
[16]:
display(swp.DBZH)
<xarray.DataArray 'DBZH' (azimuth: 1376, range: 602)> array([[ nan, nan, nan, ..., -70.70001 , -70.19 , -70.07001 ], [ nan, nan, nan, ..., -70.98999 , -70.28 , -70.26999 ], [ nan, nan, nan, ..., -70.78 , -70.26001 , -70.31 ], ..., [ nan, nan, nan, ..., -70.04001 , -70.389984, -69.369995], [ nan, nan, nan, ..., -69.81 , -70.17001 , -69.600006], [ nan, nan, nan, ..., -69.95999 , -69.98999 , -69.98001 ]], dtype=float32) Coordinates: (12/15) * azimuth (azimuth) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 7.8 7.8 7.8 7.8 7.8 ... 7.8 7.8 7.8 7.8 7.8 * range (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04 time datetime64[ns] 2021-07-30T16:00:00 rtime (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20... longitude float64 15.45 ... ... x (azimuth, range) float64 0.09078 0.2723 0.4539 ... -31.13 -31.19 y (azimuth, range) float64 24.77 74.3 ... 2.973e+04 2.978e+04 z (azimuth, range) float64 411.4 418.2 ... 4.535e+03 4.542e+03 gr (azimuth, range) float64 24.77 74.3 ... 2.973e+04 2.978e+04 rays (azimuth, range) float64 0.21 0.21 0.21 ... 359.9 359.9 359.9 bins (azimuth, range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04 Attributes: units: dBZ standard_name: radar_equivalent_reflectivity_factor_h long_name: Equivalent reflectivity factor H
Create simple plot¶
Using xarray features a simple plot can be created like this. Note the sortby('rtime')
method, which sorts the radials by time.
[17]:
swp.DBZH.sortby("rtime").plot(x="range", y="rtime", add_labels=False)
[17]:
<matplotlib.collections.QuadMesh at 0x7fd8f1ee9890>

[18]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wradlib.plot_ppi(proj={"latmin": 3e3}, fig=fig)

Mask some values¶
[19]:
dbzh = swp["DBZH"].where(swp["DBZH"] >= 0)
dbzh.plot(x="x", y="y")
[19]:
<matplotlib.collections.QuadMesh at 0x7fd8f1c3bf10>

[20]:
vol[0]
[20]:
<xarray.Dataset> Dimensions: (azimuth: 1376, range: 602) Coordinates: * azimuth (azimuth) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 ... * range (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04 time datetime64[ns] 2021-07-30T16:00:00 rtime (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20... longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... QUAL (azimuth, range) uint16 ... Attributes: fixed_angle: 7.8
Export to ODIM and CfRadial2¶
[21]:
vol[0].DBZH.sortby("rtime").plot(y="rtime")
[21]:
<matplotlib.collections.QuadMesh at 0x7fd8f1c8f450>

[22]:
vol.to_odim("furuno_scn_as_odim.h5")
vol.to_cfradial2("furuno_scn_as_cfradial2.nc")
Import again¶
[23]:
vola = wrl.io.open_odim_dataset(
"furuno_scn_as_odim.h5", reindex_angle=False, keep_elevation=True
)
display(vola.root)
display(vola[0])
vola[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset> Dimensions: (sweep: 1) Coordinates: time datetime64[ns] 2021-07-30T16:00:00 sweep_mode <U20 'azimuth_surveillance' longitude float64 15.45 altitude float64 407.9 latitude float64 47.08 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 '2021-07-30T16:00:00Z' time_coverage_end <U20 '2021-07-30T16:00:14Z' sweep_group_name (sweep) <U7 'sweep_0' sweep_fixed_angle (sweep) float64 7.8 Attributes: version: None title: None institution: None references: None source: None history: None comment: im/exported using wradlib instrument_name: None fixed_angle: 7.8
<xarray.Dataset> Dimensions: (azimuth: 1376, range: 602) Coordinates: * azimuth (azimuth) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 ... rtime (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723392 ... 20... * range (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04 time datetime64[ns] 2021-07-30T16:00:00 sweep_mode <U20 ... longitude float64 ... latitude float64 ... altitude float64 ... Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... Attributes: fixed_angle: 7.8
[23]:
<matplotlib.collections.QuadMesh at 0x7fd901303c90>

[24]:
volb = wrl.io.open_cfradial2_dataset("furuno_scn_as_cfradial2.nc")
display(volb.root)
display(volb[0])
volb[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset> Dimensions: (sweep: 1) Coordinates: longitude float64 15.45 altitude float64 407.9 sweep_mode <U20 'azimuth_surveillance' time datetime64[ns] 2021-07-30T16:00:00 latitude float64 47.08 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 '2021-07-30T16:00:00Z' time_coverage_end <U20 '2021-07-30T16:00:14Z' sweep_group_name (sweep) <U7 'sweep_0' sweep_fixed_angle (sweep) float64 7.8 Attributes: version: None title: None institution: None references: None source: None history: None comment: im/exported using wradlib instrument_name: None fixed_angle: 7.8
<xarray.Dataset> Dimensions: (azimuth: 1376, range: 602) Coordinates: * azimuth (azimuth) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 ... * range (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04 rtime (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20... longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... time datetime64[ns] 2021-07-30T16:00:00 Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... QUAL (azimuth, range) uint16 ... Attributes: fixed_angle: 7.8
[24]:
<matplotlib.collections.QuadMesh at 0x7fd8f1ad34d0>

Check equality¶
We have to drop the time variable when checking equality since IRIS has millisecond resolution.
[25]:
xr.testing.assert_allclose(vol.root.drop("time"), vola.root.drop("time"))
xr.testing.assert_allclose(
vol[0].drop(["rtime", "time", "QUAL"]), vola[0].drop(["rtime", "time"])
)
xr.testing.assert_allclose(vol.root.drop("time"), volb.root.drop("time"))
# xr.testing.assert_allclose(vol[0].drop("time"), volb[0].drop("time"))
xr.testing.assert_allclose(vola.root, volb.root)
xr.testing.assert_allclose(vola[0].drop("rtime"), volb[0].drop(["rtime", "QUAL"]))
More Furuno 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. furuno
-> wradlib-furuno
). 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="furuno"
.
[26]:
swp = xr.open_dataset(
f, engine="wradlib-furuno", group=1, backend_kwargs=dict(reindex_angle=False)
)
display(swp)
<xarray.Dataset> Dimensions: (azimuth: 1376, range: 602) Coordinates: * azimuth (azimuth) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 ... * range (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04 time datetime64[ns] ... rtime (azimuth) datetime64[ns] ... longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... QUAL (azimuth, range) uint16 ... Attributes: fixed_angle: 7.8
Load furuno scnx Data¶
Data provided by GFZ German Research Centre for Geosciences.
[27]:
fpath = "furuno/2006_20220324_000000_000.scnx.gz"
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_furuno_dataset(f, reindex_angle=False)
Inspect RadarVolume¶
[28]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 1)
Elevation(s): (0.5)
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).
[29]:
vol.root
[29]:
<xarray.Dataset> Dimensions: (sweep: 1) Coordinates: time datetime64[ns] 2022-03-24T00:00:01 longitude float64 13.24 altitude float64 38.0 sweep_mode <U20 'azimuth_surveillance' latitude float64 53.55 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 '2022-03-24T00:00:01Z' time_coverage_end <U20 '2022-03-24T00:00:28Z' sweep_group_name (sweep) <U7 'sweep_0' sweep_fixed_angle (sweep) float64 0.5 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.).
[30]:
display(vol[0])
<xarray.Dataset> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 ... * range (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04 time datetime64[ns] 2022-03-24T00:00:01 rtime (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20... longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... QUAL (azimuth, range) uint16 ... Attributes: fixed_angle: 0.5
Goereferencing¶
[31]:
swp = vol[0].copy().pipe(wrl.georef.georeference_dataset)
Plotting¶
Currently the data dynamic range is left as read from the file. That way the difference between shortpulse and longpulse can be clearly seen.
[32]:
swp.DBZH.plot.pcolormesh(x="x", y="y")
pl.gca().set_aspect("equal")

[33]:
fig = pl.figure(figsize=(10, 10))
swp.DBZH.wradlib.plot_ppi(proj="cg", fig=fig)
[33]:
<matplotlib.collections.QuadMesh at 0x7fd8f19eaa90>

[34]:
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
)
[35]:
map_proj = ccrs.AzimuthalEquidistant(
central_latitude=swp.latitude.values, central_longitude=swp.longitude.values
)
pm = swp.DBZH.wradlib.plot_ppi(proj=map_proj)
ax = pl.gca()
ax.gridlines(crs=map_proj)
print(ax)
< GeoAxes: +proj=aeqd +ellps=WGS84 +lon_0=13.243970000000001 +lat_0=53.55478 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >

[36]:
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.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines(draw_labels=True)
[36]:
<cartopy.mpl.gridliner.Gridliner at 0x7fd90510e210>

[37]:
import cartopy.feature as cfeature
def plot_rivers(ax):
rivers = cfeature.NaturalEarthFeature(
category="physical",
name="rivers_lake_centerlines",
scale="10m",
facecolor="none",
)
ax.add_feature(rivers, edgecolor="blue", 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)
DBZH = swp.DBZH
pm = DBZH.where(DBZH > 0).wradlib.plot_ppi(ax=ax)
plot_rivers(ax)
ax.gridlines(draw_labels=True)
[37]:
<cartopy.mpl.gridliner.Gridliner at 0x7fd8f1808090>

[38]:
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.DBZH.wradlib.plot_ppi(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[38]:
<cartopy.mpl.gridliner.Gridliner at 0x7fd8f1889250>

[39]:
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.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines()
[39]:
<cartopy.mpl.gridliner.Gridliner at 0x7fd8f1734f90>

[40]:
swp.DBZH.wradlib.plot_ppi()
[40]:
<matplotlib.collections.QuadMesh at 0x7fd90139a890>

Inspect radar moments¶
The DataArrays can be accessed by key or by attribute. Each DataArray has dimensions and coordinates of it’s parent dataset.
[41]:
display(swp.DBZH)
<xarray.DataArray 'DBZH' (azimuth: 722, range: 936)> array([[ nan, nan, nan, ..., -80.740005, -79.34 , -79.240005], [ nan, nan, nan, ..., -80.31 , -79.06 , -79.25 ], [ nan, nan, nan, ..., -80.2 , -79.149994, -79.31999 ], ..., [ nan, nan, nan, ..., -79.78999 , -79.45999 , -79. ], [ nan, nan, nan, ..., -80.09 , -79.31 , -79.020004], [ nan, nan, nan, ..., -80.369995, -79.33 , -79.149994]], dtype=float32) Coordinates: (12/15) * azimuth (azimuth) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5 * range (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04 time datetime64[ns] 2022-03-24T00:00:01 rtime (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20... longitude float64 13.24 ... ... x (azimuth, range) float64 0.1243 0.373 0.6217 ... -403.6 -404.0 y (azimuth, range) float64 37.5 112.5 ... 7.008e+04 7.015e+04 z (azimuth, range) float64 38.3 38.95 39.61 ... 937.5 939.2 940.3 gr (azimuth, range) float64 37.53 112.5 ... 7.008e+04 7.015e+04 rays (azimuth, range) float64 0.19 0.19 0.19 ... 359.7 359.7 359.7 bins (azimuth, range) float32 37.5 112.5 ... 7.009e+04 7.016e+04 Attributes: units: dBZ standard_name: radar_equivalent_reflectivity_factor_h long_name: Equivalent reflectivity factor H
Create simple plot¶
Using xarray features a simple plot can be created like this. Note the sortby('rtime')
method, which sorts the radials by time.
[42]:
swp.DBZH.sortby("rtime").plot(x="range", y="rtime", add_labels=False)
[42]:
<matplotlib.collections.QuadMesh at 0x7fd8f0b80950>

[43]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wradlib.plot_ppi(proj={"latmin": 3e3}, fig=fig)

Mask some values¶
[44]:
dbzh = swp["DBZH"].where(swp["DBZH"] >= 0)
dbzh.plot(x="x", y="y")
[44]:
<matplotlib.collections.QuadMesh at 0x7fd8f09d4d50>

[45]:
vol[0]
[45]:
<xarray.Dataset> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 ... * range (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04 time datetime64[ns] 2022-03-24T00:00:01 rtime (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20... longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... QUAL (azimuth, range) uint16 ... Attributes: fixed_angle: 0.5
Export to ODIM and CfRadial2¶
[46]:
vol[0].DBZH.sortby("rtime").plot(y="rtime")
[46]:
<matplotlib.collections.QuadMesh at 0x7fd90272b310>

[47]:
vol.to_odim("furuno_scnx_as_odim.h5")
vol.to_cfradial2("furuno_scnx_as_cfradial2.nc")
Import again¶
[48]:
vola = wrl.io.open_odim_dataset(
"furuno_scnx_as_odim.h5", reindex_angle=False, keep_elevation=True
)
display(vola.root)
display(vola[0])
vola[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset> Dimensions: (sweep: 1) Coordinates: time datetime64[ns] 2022-03-24T00:00:01 sweep_mode <U20 'azimuth_surveillance' longitude float64 13.24 altitude float64 38.0 latitude float64 53.55 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 '2022-03-24T00:00:01Z' time_coverage_end <U20 '2022-03-24T00:00:28Z' sweep_group_name (sweep) <U7 'sweep_0' sweep_fixed_angle (sweep) float64 0.5 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
<xarray.Dataset> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 ... rtime (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439552 ... 20... * range (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04 time datetime64[ns] 2022-03-24T00:00:01 sweep_mode <U20 ... longitude float64 ... latitude float64 ... altitude float64 ... Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... Attributes: fixed_angle: 0.5
[48]:
<matplotlib.collections.QuadMesh at 0x7fd8f81cbfd0>

[49]:
volb = wrl.io.open_cfradial2_dataset("furuno_scnx_as_cfradial2.nc")
display(volb.root)
display(volb[0])
volb[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset> Dimensions: (sweep: 1) Coordinates: longitude float64 13.24 altitude float64 38.0 sweep_mode <U20 'azimuth_surveillance' time datetime64[ns] 2022-03-24T00:00:01 latitude float64 53.55 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 '2022-03-24T00:00:01Z' time_coverage_end <U20 '2022-03-24T00:00:28Z' sweep_group_name (sweep) <U7 'sweep_0' sweep_fixed_angle (sweep) float64 0.5 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
<xarray.Dataset> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 ... * range (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04 rtime (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20... longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... time datetime64[ns] 2022-03-24T00:00:01 Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... QUAL (azimuth, range) uint16 ... Attributes: fixed_angle: 0.5
[49]:
<matplotlib.collections.QuadMesh at 0x7fd8f19dc7d0>

Check equality¶
We have to drop the time variable when checking equality since IRIS has millisecond resolution.
[50]:
xr.testing.assert_allclose(vol.root.drop("time"), vola.root.drop("time"))
xr.testing.assert_allclose(
vol[0].drop(["rtime", "time", "QUAL"]), vola[0].drop(["rtime", "time"])
)
xr.testing.assert_allclose(vol.root.drop("time"), volb.root.drop("time"))
# xr.testing.assert_allclose(vol[0].drop("time"), volb[0].drop("time"))
xr.testing.assert_allclose(vola.root, volb.root)
xr.testing.assert_allclose(vola[0].drop("rtime"), volb[0].drop(["rtime", "QUAL"]))
More Furuno 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. furuno
-> wradlib-furuno
). 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="furuno"
.
[51]:
swp = xr.open_dataset(
f, engine="wradlib-furuno", group=1, backend_kwargs=dict(reindex_angle=False)
)
display(swp)
<xarray.Dataset> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 ... * range (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04 time datetime64[ns] ... rtime (azimuth) datetime64[ns] ... longitude float64 ... latitude float64 ... altitude float64 ... sweep_mode <U20 ... Data variables: RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... QUAL (azimuth, range) uint16 ... Attributes: fixed_angle: 0.5