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/python3.10/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")
../../_images/notebooks_fileio_wradlib_furuno_backend_14_0.png
[8]:
fig = pl.figure(figsize=(10, 10))
swp.DBZH.wradlib.plot_ppi(proj="cg", fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7f9b6c9e9b10>
../../_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.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 >
../../_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.wradlib.plot_ppi(ax=ax)
ax.gridlines(draw_labels=True)
[11]:
<cartopy.mpl.gridliner.Gridliner at 0x7f9b6c7ffc10>
../../_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).wradlib.plot_ppi(ax=ax)
plot_rivers(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7f9b6c86fca0>
../../_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.wradlib.plot_ppi(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[13]:
<cartopy.mpl.gridliner.Gridliner at 0x7f9b6c6fdcf0>
../../_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.wradlib.plot_ppi(ax=ax)
ax.gridlines()
[14]:
<cartopy.mpl.gridliner.Gridliner at 0x7f9b6c74fac0>
../../_images/notebooks_fileio_wradlib_furuno_backend_21_1.png
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f9b6c4e9b70>
../../_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: (12/15)
  * 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:
    standard_name:  radar_equivalent_reflectivity_factor_h
    long_name:      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 0x7f9b6c5ab040>
../../_images/notebooks_fileio_wradlib_furuno_backend_26_1.png
[18]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wradlib.plot_ppi(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 0x7f9b6c242290>
../../_images/notebooks_fileio_wradlib_furuno_backend_29_1.png
[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 0x7f9b6c8573a0>
../../_images/notebooks_fileio_wradlib_furuno_backend_32_1.png
[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 0x7f9b65987b50>
../../_images/notebooks_fileio_wradlib_furuno_backend_35_3.png
[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 0x7f9b65823b20>
../../_images/notebooks_fileio_wradlib_furuno_backend_36_3.png

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")
../../_images/notebooks_fileio_wradlib_furuno_backend_53_0.png
[33]:
fig = pl.figure(figsize=(10, 10))
swp.DBZH.wradlib.plot_ppi(proj="cg", fig=fig)
[33]:
<matplotlib.collections.QuadMesh at 0x7f9b65481ae0>
../../_images/notebooks_fileio_wradlib_furuno_backend_54_1.png
[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 >
../../_images/notebooks_fileio_wradlib_furuno_backend_56_1.png
[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 0x7f9b65304340>
../../_images/notebooks_fileio_wradlib_furuno_backend_57_1.png
[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 0x7f9b7703d2a0>
../../_images/notebooks_fileio_wradlib_furuno_backend_58_1.png
[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 0x7f9b653bed70>
../../_images/notebooks_fileio_wradlib_furuno_backend_59_1.png
[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 0x7f9b6520eec0>
../../_images/notebooks_fileio_wradlib_furuno_backend_60_1.png
[40]:
swp.DBZH.wradlib.plot_ppi()
[40]:
<matplotlib.collections.QuadMesh at 0x7f9b6538ad40>
../../_images/notebooks_fileio_wradlib_furuno_backend_61_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.

[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:
    standard_name:  radar_equivalent_reflectivity_factor_h
    long_name:      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 0x7f9b65342bc0>
../../_images/notebooks_fileio_wradlib_furuno_backend_65_1.png
[43]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wradlib.plot_ppi(proj={"latmin": 3e3}, fig=fig)
../../_images/notebooks_fileio_wradlib_furuno_backend_66_0.png

Mask some values

[44]:
dbzh = swp["DBZH"].where(swp["DBZH"] >= 0)
dbzh.plot(x="x", y="y")
[44]:
<matplotlib.collections.QuadMesh at 0x7f9b65262bf0>
../../_images/notebooks_fileio_wradlib_furuno_backend_68_1.png
[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 0x7f9b652c89a0>
../../_images/notebooks_fileio_wradlib_furuno_backend_71_1.png
[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 0x7f9b6595cdf0>
../../_images/notebooks_fileio_wradlib_furuno_backend_74_3.png
[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 0x7f9b76e186a0>
../../_images/notebooks_fileio_wradlib_furuno_backend_75_3.png

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