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()
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) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float32 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 latitude float64 47.08 altitude float64 407.9 sweep_mode <U20 'azimuth_surveillance' 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 0x7f63133f7f40>

[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 0x7f63132c17b0>

[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 0x7f631314b220>

[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 0x7f63132c2f50>

[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 0x7f6312fde590>

[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f63130abbe0>

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) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float32 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) float32 0.09078 0.2723 0.4539 ... -31.13 -31.18 y (azimuth, range) float32 24.77 74.3 ... 2.973e+04 2.978e+04 z (azimuth, range) float32 411.0 418.0 ... 4.535e+03 4.542e+03 gr (azimuth, range) float32 24.77 74.3 ... 2.973e+04 2.978e+04 rays (azimuth, range) float32 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: long_name: Equivalent reflectivity factor H standard_name: radar_equivalent_reflectivity_factor_h units: dBZ
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 0x7f6312ecf850>

[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 0x7f6310115330>

[20]:
vol[0]
[20]:
<xarray.Dataset> Dimensions: (azimuth: 1376, range: 602) Coordinates: * azimuth (azimuth) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float32 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 latitude float64 47.08 altitude float64 407.9 sweep_mode <U20 'azimuth_surveillance' 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 0x7f63129b4520>

[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) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float32 7.8 7.8 7.8 7.8 7.8 ... 7.8 7.8 7.8 7.8 7.8 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 'azimuth_surveillance' longitude float64 15.45 latitude float64 47.08 altitude float64 407.9 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 0x7f6312a6c670>

[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) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float32 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 rtime (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20... longitude float64 15.45 latitude float64 47.08 altitude float64 407.9 sweep_mode <U20 'azimuth_surveillance' 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 0x7f6312902650>

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¶
[26]:
swp = xr.open_dataset(
f, engine="furuno", group=1, backend_kwargs=dict(reindex_angle=False)
)
display(swp)
<xarray.Dataset> Dimensions: (azimuth: 1376, range: 602) Coordinates: * azimuth (azimuth) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float32 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 latitude float64 47.08 altitude float64 407.9 sweep_mode <U20 'azimuth_surveillance' 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) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float32 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 latitude float64 53.55 altitude float64 38.0 sweep_mode <U20 'azimuth_surveillance' 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 0x7f63127fb610>

[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 0x7f631297a380>

[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 0x7f631c7451b0>

[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 0x7f6312703fd0>

[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 0x7f631274afe0>

[40]:
swp.DBZH.wradlib.plot_ppi()
[40]:
<matplotlib.collections.QuadMesh at 0x7f631276fee0>

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) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float32 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) float32 0.1244 0.3731 0.6218 ... -403.6 -404.1 y (azimuth, range) float32 37.5 112.5 ... 7.008e+04 7.015e+04 z (azimuth, range) float32 38.0 39.0 39.0 ... 937.0 939.0 940.0 gr (azimuth, range) float32 37.53 112.5 ... 7.008e+04 7.015e+04 rays (azimuth, range) float32 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: long_name: Equivalent reflectivity factor H standard_name: radar_equivalent_reflectivity_factor_h units: dBZ
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 0x7f63125a9300>

[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 0x7f631256e020>

[45]:
vol[0]
[45]:
<xarray.Dataset> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float32 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 latitude float64 53.55 altitude float64 38.0 sweep_mode <U20 'azimuth_surveillance' 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 0x7f63123cdb10>

[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) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float32 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5 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 'azimuth_surveillance' longitude float64 13.24 latitude float64 53.55 altitude float64 38.0 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 0x7f6312a37190>

[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) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float32 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 rtime (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20... longitude float64 13.24 latitude float64 53.55 altitude float64 38.0 sweep_mode <U20 'azimuth_surveillance' 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 0x7f63123513f0>

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¶
[51]:
swp = xr.open_dataset(
f, engine="furuno", group=1, backend_kwargs=dict(reindex_angle=False)
)
display(swp)
<xarray.Dataset> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float32 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 latitude float64 53.55 altitude float64 38.0 sweep_mode <U20 'azimuth_surveillance' 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