xarray IRIS backend#

In this example, we read IRIS (sigmet) data files using the xradar iris 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 IRIS Volume Data#

[2]:
fpath = "sigmet/SUR210819000227.RAWKPJV"
f = wrl.util.get_wradlib_data_file(fpath)
vol = xd.io.open_iris_datatree(f, reindex_angle=False)
Downloading file 'sigmet/SUR210819000227.RAWKPJV' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/sigmet/SUR210819000227.RAWKPJV' 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 '2021-08-19T00:02:28Z'
    time_coverage_end    <U20 '2021-08-19T00:02:49Z'
    longitude            float64 25.52
    altitude             float64 157.0
    latitude             float64 58.48
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 '2021-08-19T00:02:28Z'
    time_coverage_end    <U20 '2021-08-19T00:02:49Z'
    longitude            float64 25.52
    altitude             float64 157.0
    latitude             float64 58.48
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: 359, range: 833)
Coordinates:
  * azimuth            (azimuth) float64 0.03021 1.035 2.054 ... 358.0 359.0
    elevation          (azimuth) float32 ...
    time               (azimuth) datetime64[ns] 2021-08-19T00:02:31.104000 .....
  * range              (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05
    longitude          float64 ...
    latitude           float64 ...
    altitude           float64 ...
Data variables: (12/16)
    DBTH               (azimuth, range) float32 ...
    DBZH               (azimuth, range) float32 ...
    VRADH              (azimuth, range) float32 ...
    WRADH              (azimuth, range) float32 ...
    ZDR                (azimuth, range) float32 ...
    KDP                (azimuth, range) float32 ...
    ...                 ...
    SNRH               (azimuth, range) float32 ...
    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#

[7]:
swp.DBZH.plot.pcolormesh(x="x", y="y")
pl.gca().set_aspect("equal")
../../_images/notebooks_fileio_wradlib_iris_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 0x7f6055849410>
../../_images/notebooks_fileio_wradlib_iris_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=25.518660116940737 +lat_0=58.48231002688408 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >
../../_images/notebooks_fileio_wradlib_iris_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 0x7f6049ee6f10>
../../_images/notebooks_fileio_wradlib_iris_backend_18_1.png
[12]:
import cartopy.feature as cfeature


def plot_borders(ax):
    borders = cfeature.NaturalEarthFeature(
        category="physical", name="coastline", scale="10m", facecolor="none"
    )
    ax.add_feature(borders, edgecolor="black", 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_borders(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6049f3b6d0>
../../_images/notebooks_fileio_wradlib_iris_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 0x7f6049f7b6d0>
../../_images/notebooks_fileio_wradlib_iris_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 0x7f6049e3b810>
../../_images/notebooks_fileio_wradlib_iris_backend_21_1.png
[15]:
swp.DBZH.wrl.vis.plot()
[15]:
<matplotlib.collections.QuadMesh at 0x7f60554fdf90>
../../_images/notebooks_fileio_wradlib_iris_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: 359, range: 833)>
array([[-327.68, -327.68, -327.68, ..., -327.68, -327.68, -327.68],
       [-327.68,    3.39,    6.45, ..., -327.68, -327.68, -327.68],
       [  -8.86,    5.25,    8.58, ..., -327.68, -327.68, -327.68],
       ...,
       [-327.68, -327.68, -327.68, ..., -327.68, -327.68, -327.68],
       [-327.68,    4.75,   10.95, ..., -327.68, -327.68, -327.68],
       [-327.68, -327.68,    4.94, ..., -327.68, -327.68, -327.68]])
Coordinates: (12/14)
  * azimuth     (azimuth) float64 0.03021 1.035 2.054 ... 357.0 358.0 359.0
    elevation   (azimuth) float64 0.5054 0.5054 0.5054 ... 0.5054 0.5054 0.5054
    time        (azimuth) datetime64[ns] 2021-08-19T00:02:31.104000 ... 2021-...
  * range       (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05
    longitude   float64 25.52
    latitude    float64 58.48
    ...          ...
    x           (azimuth, range) float64 0.07909 0.2373 ... -4.211e+03
    y           (azimuth, range) float64 150.0 450.0 ... 2.493e+05 2.496e+05
    z           (azimuth, range) float64 158.3 161.0 ... 6.023e+03 6.034e+03
    gr          (azimuth, range) float64 150.0 450.0 ... 2.493e+05 2.496e+05
    rays        (azimuth, range) float64 0.03021 0.03021 0.03021 ... 359.0 359.0
    bins        (azimuth, range) float32 150.0 450.0 ... 2.494e+05 2.498e+05
Attributes:
    units:          dBZ
    standard_name:  radar_equivalent_reflectivity_factor_h
    long_name:      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 0x7f6049e4e710>
../../_images/notebooks_fileio_wradlib_iris_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_iris_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 0x7f605582d750>
../../_images/notebooks_fileio_wradlib_iris_backend_29_1.png

Export to ODIM and CfRadial2#

Need to remove DB_XHDR since it can’t be represented as ODIM/CfRadial2 moment.

[20]:
# vol[0] = vol[0].drop("DB_XHDR", errors="ignore")
# vol[0].DBZH.sortby("rtime").plot(y="rtime")
[21]:
xd.io.to_odim(vol, "iris_as_odim.h5")
xd.io.to_cfradial2(vol, "iris_as_cfradial2.nc")

Import again#

[22]:
vola = xd.io.open_odim_datatree("iris_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 '2021-08-19T00:02:28Z'
    time_coverage_end    <U20 '2021-08-19T00:02:49Z'
    longitude            float64 25.52
    altitude             float64 157.0
    latitude             float64 58.48
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
[23]:
volb = xt.open_datatree("iris_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 Iris loading mechanisms#

Use xr.open_dataset to retrieve explicit group#

[24]:
swp_b = xr.open_dataset(
    f, engine="iris", group="sweep_0", backend_kwargs=dict(reindex_angle=False)
)
display(swp_b)
<xarray.Dataset>
Dimensions:            (azimuth: 359, range: 833)
Coordinates:
  * azimuth            (azimuth) float64 0.03021 1.035 2.054 ... 358.0 359.0
    elevation          (azimuth) float32 ...
    time               (azimuth) datetime64[ns] ...
  * range              (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05
    longitude          float64 ...
    latitude           float64 ...
    altitude           float64 ...
Data variables: (12/16)
    DBTH               (azimuth, range) float32 ...
    DBZH               (azimuth, range) float32 ...
    VRADH              (azimuth, range) float32 ...
    WRADH              (azimuth, range) float32 ...
    ZDR                (azimuth, range) float32 ...
    KDP                (azimuth, range) float32 ...
    ...                 ...
    SNRH               (azimuth, range) float32 ...
    sweep_mode         <U20 ...
    sweep_number       int64 ...
    prt_mode           <U7 ...
    follow_mode        <U7 ...
    sweep_fixed_angle  float64 ...