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")
../../_images/notebooks_fileio_wradlib_furuno_backend_14_0.png 
[8]:

fig = pl.figure(figsize=(10, 10))
swp.DBZH.wrl.vis.plot(proj="cg", fig=fig)

[8]:

<matplotlib.collections.QuadMesh at 0x7f6b685cf950>
../../_images/notebooks_fileio_wradlib_furuno_backend_15_1.png 
[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 >
../../_images/notebooks_fileio_wradlib_furuno_backend_17_1.png 
[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>
../../_images/notebooks_fileio_wradlib_furuno_backend_18_1.png 
[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>
../../_images/notebooks_fileio_wradlib_furuno_backend_19_1.png 
[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>
../../_images/notebooks_fileio_wradlib_furuno_backend_20_1.png 
[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>
../../_images/notebooks_fileio_wradlib_furuno_backend_21_1.png 
[15]:

swp.DBZH.wrl.vis.plot()

[15]:

<matplotlib.collections.QuadMesh at 0x7f6b68321d50>
../../_images/notebooks_fileio_wradlib_furuno_backend_22_1.png

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>
../../_images/notebooks_fileio_wradlib_furuno_backend_26_1.png 
[18]:

fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(proj={"latmin": 3e3}, fig=fig)
../../_images/notebooks_fileio_wradlib_furuno_backend_27_0.png

Mask some values#

[19]:

dbzh = swp["DBZH"].where(swp["DBZH"] >= 0)
dbzh.plot(x="x", y="y")

[19]:

<matplotlib.collections.QuadMesh at 0x7f6b68352e10>
../../_images/notebooks_fileio_wradlib_furuno_backend_29_1.png

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")
../../_images/notebooks_fileio_wradlib_furuno_backend_47_0.png 
[29]:

fig = pl.figure(figsize=(10, 10))
swp.DBZH.wrl.vis.plot(proj="cg", fig=fig)

[29]:

<matplotlib.collections.QuadMesh at 0x7f6b67d69150>
../../_images/notebooks_fileio_wradlib_furuno_backend_48_1.png 
[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 >
../../_images/notebooks_fileio_wradlib_furuno_backend_50_1.png 
[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>
../../_images/notebooks_fileio_wradlib_furuno_backend_51_1.png 
[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>
../../_images/notebooks_fileio_wradlib_furuno_backend_52_1.png 
[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>
../../_images/notebooks_fileio_wradlib_furuno_backend_53_1.png 
[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>
../../_images/notebooks_fileio_wradlib_furuno_backend_54_1.png 
[36]:

swp.DBZH.wrl.vis.plot()

[36]:

<matplotlib.collections.QuadMesh at 0x7f6b67c79c10>
../../_images/notebooks_fileio_wradlib_furuno_backend_55_1.png

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>
../../_images/notebooks_fileio_wradlib_furuno_backend_59_1.png 
[39]:

fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(proj={"latmin": 3e3}, fig=fig)
../../_images/notebooks_fileio_wradlib_furuno_backend_60_0.png

Mask some values#

[40]:

dbzh = swp["DBZH"].where(swp["DBZH"] >= 0)
dbzh.plot(x="x", y="y")

[40]:

<matplotlib.collections.QuadMesh at 0x7f6b683d8a90>
../../_images/notebooks_fileio_wradlib_furuno_backend_62_1.png

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 ...