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")
../../_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 0x7f63133f7f40>
../../_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 0x7f63132c17b0>
../../_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 0x7f631314b220>
../../_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 0x7f63132c2f50>
../../_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 0x7f6312fde590>
../../_images/notebooks_fileio_wradlib_furuno_backend_21_1.png
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f63130abbe0>
../../_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) 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>
../../_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 0x7f6310115330>
../../_images/notebooks_fileio_wradlib_furuno_backend_29_1.png
[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>
../../_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) 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>
../../_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) 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>
../../_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

[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")
../../_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 0x7f63127fb610>
../../_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 0x7f631297a380>
../../_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 0x7f631c7451b0>
../../_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 0x7f6312703fd0>
../../_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 0x7f631274afe0>
../../_images/notebooks_fileio_wradlib_furuno_backend_60_1.png
[40]:
swp.DBZH.wradlib.plot_ppi()
[40]:
<matplotlib.collections.QuadMesh at 0x7f631276fee0>
../../_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) 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>
../../_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 0x7f631256e020>
../../_images/notebooks_fileio_wradlib_furuno_backend_68_1.png
[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>
../../_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) 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>
../../_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) 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>
../../_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

[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