xradar furuno backend#
In this example, we read scn/scnx (furuno) data files using the xradar furuno
xarray backend.
[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 xradar as xd
import datatree as xt
import xarray as xr
try:
get_ipython().run_line_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 = xd.io.open_furuno_datatree(f, reindex_angle=False)
Downloading file 'furuno/0080_20210730_160000_01_02.scn.gz' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/furuno/0080_20210730_160000_01_02.scn.gz' to '/home/runner/work/wradlib/wradlib/wradlib-data'.
Inspect RadarVolume#
[3]:
display(vol)
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' time_coverage_start <U20 '2021-07-30T16:00:00Z' time_coverage_end <U20 '2021-07-30T16:00:14Z' longitude float64 15.45 altitude float64 407.9 latitude float64 47.08 Attributes: Conventions: None version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar instrument_name: None
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.DatasetView> Dimensions: () Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' time_coverage_start <U20 '2021-07-30T16:00:00Z' time_coverage_end <U20 '2021-07-30T16:00:14Z' longitude float64 15.45 altitude float64 407.9 latitude float64 47.08 Attributes: Conventions: None version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar instrument_name: None
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["sweep_0"])
<xarray.DatasetView> Dimensions: (azimuth: 1376, range: 602) Coordinates: * azimuth (azimuth) float32 0.21 0.47 0.74 ... 359.4 359.7 359.9 elevation (azimuth) float32 ... * range (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04 time (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500... longitude float64 ... latitude float64 ... altitude float64 ... Data variables: (12/14) RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... ... ... QUAL (azimuth, range) uint16 ... sweep_mode <U20 ... sweep_number int64 ... prt_mode <U7 ... follow_mode <U7 ... sweep_fixed_angle float64 ...
Goereferencing#
[6]:
swp = vol["sweep_0"].ds.copy()
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.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.wrl.vis.plot(proj="cg", fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7f6b685cf950>

[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.wrl.vis.plot(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.wrl.vis.plot(ax=ax)
ax.gridlines(draw_labels=True)
[11]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6b6894b490>

[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).wrl.vis.plot(ax=ax)
plot_rivers(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6b68274a50>

[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.wrl.vis.plot(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[13]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6b682bf110>

[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.wrl.vis.plot(ax=ax)
ax.gridlines()
[14]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6b682e6d10>

[15]:
swp.DBZH.wrl.vis.plot()
[15]:
<matplotlib.collections.QuadMesh at 0x7f6b68321d50>

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: (12/14) * 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 (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20... sweep_mode <U20 'azimuth_surveillance' 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: units: dBZ long_name: Equivalent reflectivity factor H standard_name: radar_equivalent_reflectivity_factor_h coordinates: elevation azimuth range latitude longitude altitude time ...
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.
[17]:
swp.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)
[17]:
<matplotlib.collections.QuadMesh at 0x7f6b6835b950>

[18]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(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 0x7f6b68352e10>

Export to ODIM and CfRadial2#
[20]:
xd.io.to_odim(vol, "furuno_scn_as_odim.h5")
xd.io.to_cfradial2(vol, "furuno_scn_as_cfradial2.nc")
Import again#
[21]:
vola = xd.io.open_odim_datatree("furuno_scn_as_odim.h5", reindex_angle=False)
display(vola)
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' time_coverage_start <U20 '2021-07-30T16:00:00Z' time_coverage_end <U20 '2021-07-30T16:00:14Z' longitude float64 15.45 altitude float64 407.9 latitude float64 47.08 Attributes: Conventions: ODIM_H5/V2_2 version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar instrument_name: None
[22]:
volb = xt.open_datatree("furuno_scn_as_cfradial2.nc")
display(volb)
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 ... platform_type object ... instrument_type object ... time_coverage_start object ... time_coverage_end object ... longitude float64 ... altitude float64 ... latitude float64 ... Attributes: Conventions: Cf/Radial version: 2.0 title: None institution: None references: None source: None history: None: xradar v0.1.0 CfRadial2 export comment: im/exported using xradar instrument_name: None
Use xr.open_dataset
to retrieve explicit group#
Load furuno scnx Data#
Data provided by GFZ German Research Centre for Geosciences.
[23]:
fpath = "furuno/2006_20220324_000000_000.scnx.gz"
f = wrl.util.get_wradlib_data_file(fpath)
vol = xd.io.open_furuno_datatree(f, reindex_angle=False)
Downloading file 'furuno/2006_20220324_000000_000.scnx.gz' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/furuno/2006_20220324_000000_000.scnx.gz' to '/home/runner/work/wradlib/wradlib/wradlib-data'.
Inspect RadarVolume#
[24]:
display(vol)
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' time_coverage_start <U20 '2022-03-24T00:00:01Z' time_coverage_end <U20 '2022-03-24T00:00:28Z' longitude float64 13.24 altitude float64 38.0 latitude float64 53.55 Attributes: Conventions: None version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar instrument_name: None
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).
[25]:
vol.root
[25]:
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' time_coverage_start <U20 '2022-03-24T00:00:01Z' time_coverage_end <U20 '2022-03-24T00:00:28Z' longitude float64 13.24 altitude float64 38.0 latitude float64 53.55 Attributes: Conventions: None version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar instrument_name: None
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.).
[26]:
display(vol["sweep_0"])
<xarray.DatasetView> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float32 0.19 0.68 1.16 ... 358.7 359.2 359.7 elevation (azimuth) float32 ... * range (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04 time (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500... longitude float64 ... latitude float64 ... altitude float64 ... Data variables: (12/14) RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... ... ... QUAL (azimuth, range) uint16 ... sweep_mode <U20 ... sweep_number int64 ... prt_mode <U7 ... follow_mode <U7 ... sweep_fixed_angle float64 ...
Goereferencing#
[27]:
swp = vol["sweep_0"].ds.copy()
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.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.
[28]:
swp.DBZH.plot.pcolormesh(x="x", y="y")
pl.gca().set_aspect("equal")

[29]:
fig = pl.figure(figsize=(10, 10))
swp.DBZH.wrl.vis.plot(proj="cg", fig=fig)
[29]:
<matplotlib.collections.QuadMesh at 0x7f6b67d69150>

[30]:
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
)
[31]:
map_proj = ccrs.AzimuthalEquidistant(
central_latitude=swp.latitude.values, central_longitude=swp.longitude.values
)
pm = swp.DBZH.wrl.vis.plot(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 >

[32]:
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.wrl.vis.plot(ax=ax)
ax.gridlines(draw_labels=True)
[32]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6b67db6410>

[33]:
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).wrl.vis.plot(ax=ax)
plot_rivers(ax)
ax.gridlines(draw_labels=True)
[33]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6b67d68fd0>

[34]:
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.wrl.vis.plot(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[34]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6b67e27450>

[35]:
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.wrl.vis.plot(ax=ax)
ax.gridlines()
[35]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6b681c0e50>

[36]:
swp.DBZH.wrl.vis.plot()
[36]:
<matplotlib.collections.QuadMesh at 0x7f6b67c79c10>

Inspect radar moments#
The DataArrays can be accessed by key or by attribute. Each DataArray has dimensions and coordinates of it’s parent dataset.
[37]:
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: (12/14) * 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 (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20... sweep_mode <U20 'azimuth_surveillance' 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: units: dBZ long_name: Equivalent reflectivity factor H standard_name: radar_equivalent_reflectivity_factor_h coordinates: elevation azimuth range latitude longitude altitude time ...
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.
[38]:
swp.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)
[38]:
<matplotlib.collections.QuadMesh at 0x7f6b67cc1690>

[39]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(proj={"latmin": 3e3}, fig=fig)

Mask some values#
[40]:
dbzh = swp["DBZH"].where(swp["DBZH"] >= 0)
dbzh.plot(x="x", y="y")
[40]:
<matplotlib.collections.QuadMesh at 0x7f6b683d8a90>

Export to ODIM and CfRadial2#
[41]:
xd.io.to_odim(vol, "furuno_scnx_as_odim.h5")
xd.io.to_cfradial2(vol, "furuno_scnx_as_cfradial2.nc")
Import again#
[42]:
vola = xd.io.open_odim_datatree("furuno_scnx_as_odim.h5", reindex_angle=False)
display(vola)
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' time_coverage_start <U20 '2022-03-24T00:00:01Z' time_coverage_end <U20 '2022-03-24T00:00:28Z' longitude float64 13.24 altitude float64 38.0 latitude float64 53.55 Attributes: Conventions: ODIM_H5/V2_2 version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar instrument_name: None
[43]:
volb = xt.open_datatree("furuno_scnx_as_cfradial2.nc")
display(volb)
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 ... platform_type object ... instrument_type object ... time_coverage_start object ... time_coverage_end object ... longitude float64 ... altitude float64 ... latitude float64 ... Attributes: Conventions: Cf/Radial version: 2.0 title: None institution: None references: None source: None history: None: xradar v0.1.0 CfRadial2 export comment: im/exported using xradar instrument_name: None
More Furuno loading mechanisms#
Use xr.open_dataset
to retrieve explicit group#
[44]:
swp_b = xr.open_dataset(f, engine="furuno", backend_kwargs=dict(reindex_angle=False))
display(swp_b)
<xarray.Dataset> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float32 0.19 0.68 1.16 ... 358.7 359.2 359.7 elevation (azimuth) float32 ... * range (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04 time (azimuth) datetime64[ns] ... longitude float64 ... latitude float64 ... altitude float64 ... Data variables: (12/14) RATE (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... ... ... QUAL (azimuth, range) uint16 ... sweep_mode <U20 ... sweep_number int64 ... prt_mode <U7 ... follow_mode <U7 ... sweep_fixed_angle float64 ...