xarray Rainbow5 backend

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xarray Rainbow5 backend#

In this example, we read Rainbow5 data files using the xradar rainbow xarray backend.

import glob
import gzip
import io
import wradlib as wrl
import wradlib_data
import warnings
from IPython.display import display

import matplotlib.pyplot as plt
import numpy as np
import xradar as xd
import xarray as xr

warnings.filterwarnings("ignore")

Load Rainbow5 Volume Data#

fpath = "rainbow/2013051000000600dBZ.vol"
f = wradlib_data.DATASETS.fetch(fpath)
vol = xd.io.open_rainbow_datatree(f, reindex_angle=False)

Downloading file 'rainbow/2013051000000600dBZ.vol' from 'https://github.com/wradlib/wradlib-data/raw/main/data/rainbow/2013051000000600dBZ.vol' to '/home/docs/.cache/wradlib-data'.

Georeferencing#

swp = vol["sweep_0"].to_dataset(inherit="all_coords")
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.

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

<matplotlib.collections.QuadMesh at 0x7b237b661be0>

../../../_images/cdc3cffd88e03ce1ab3c6cb82e34dfd7f5f83b97e921bc9fee2487b4215738aa.png

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

../../../_images/c26f014622b2f2ad01b806a7830f26084c520b83d5f2bf45d6bec45c13008428.png