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'.

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

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")

xarray IRIS backend

Contents

xarray IRIS backend#

Load IRIS Volume Data#

Inspect RadarVolume#

Inspect root group#

Inspect sweep group(s)#

Goereferencing#

Plotting#

Inspect radar moments#

Create simple plot#

Mask some values#

Export to ODIM and CfRadial2#

Import again#

More Iris loading mechanisms#

Use `xr.open_dataset` to retrieve explicit group#

xarray IRIS backend

Contents

xarray IRIS backend#

Load IRIS Volume Data#

Inspect RadarVolume#

Inspect root group#

Inspect sweep group(s)#

Goereferencing#

Plotting#

Inspect radar moments#

Create simple plot#

Mask some values#

Export to ODIM and CfRadial2#

Import again#

More Iris loading mechanisms#

Use xr.open_dataset to retrieve explicit group#

Use `xr.open_dataset` to retrieve explicit group#