xarray IRIS backend

Contents

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 plt
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:
    plt.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'.

Georeferencing#

[6]:

swp = vol["sweep_0"].ds.copy()
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.wrl.georef.georeference()

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.

For more details on plotting radar data see under Visualization.

[8]:

swp.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)

[8]:

<matplotlib.collections.QuadMesh at 0x7fb0b81efd10>

../../../_images/notebooks_fileio_backends_iris_backend_16_1.png

[9]:

fig = plt.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(crs={"latmin": 3e3}, fig=fig)

../../../_images/notebooks_fileio_backends_iris_backend_17_0.png