Iris/Sigmet - Reader#

import xarray as xr
from open_radar_data import DATASETS

import xradar as xd

Download#

Fetching Iris radar data file from open-radar-data repository.

filename_single = DATASETS.fetch("SUR210819000227.RAWKPJV")
filename_volume = DATASETS.fetch("cor-main131125105503.RAW2049")

Downloading file 'SUR210819000227.RAWKPJV' from 'https://github.com/openradar/open-radar-data/raw/main/data/SUR210819000227.RAWKPJV' to '/home/docs/.cache/open-radar-data'.

Downloading file 'cor-main131125105503.RAW2049' from 'https://github.com/openradar/open-radar-data/raw/main/data/cor-main131125105503.RAW2049' to '/home/docs/.cache/open-radar-data'.

open_iris_datatree#

The same works analoguous with the datatree loader. But additionally we can provide a sweep string, number or list. The underlying xarray.Dataset can be accessed with property .ds.

help(xd.io.open_iris_datatree)

Help on function open_iris_datatree in module xradar.io.backends.iris:

open_iris_datatree(filename_or_obj, **kwargs)
    Open Iris/Sigmet dataset as :py:class:`xarray.DataTree`.

    Parameters
    ----------
    filename_or_obj : str, Path, file-like or DataStore
        Strings and Path objects are interpreted as a path to a local or remote
        radar file

    Keyword Arguments
    -----------------
    sweep : int, list of int, optional
        Sweep number(s) to extract, default to first sweep. If None, all sweeps are
        extracted into a list.
    first_dim : str
        Can be ``time`` or ``auto`` first dimension. If set to ``auto``,
        first dimension will be either ``azimuth`` or ``elevation`` depending on
        type of sweep. Defaults to ``auto``.
    reindex_angle : bool or dict
        Defaults to False, no reindexing. Given dict should contain the kwargs to
        reindex_angle. Only invoked if `decode_coord=True`.
    fix_second_angle : bool
        If True, fixes erroneous second angle data. Defaults to ``False``.
    site_as_coords : bool
        Attach radar site-coordinates to Dataset, defaults to ``True``.
    kwargs : dict
        Additional kwargs are fed to :py:func:`xarray.open_dataset`.

    Returns
    -------
    dtree: xarray.DataTree
        DataTree

Plot Time vs. Azimuth#

dtree["sweep_0"].ds.azimuth.plot(y="time")

[<matplotlib.lines.Line2D at 0x78d410b0e350>]

../_images/ef682526eed555122250e1b8d5fb61e64aa2a06d22467d7cea633cd0e8569761.png

Plot Sweep Range vs. Time#

We need to sort by time and specify the y-coordinate. Please also observe the different resolutions of this plot, compared to the Azimuth vs. Range plot below. This is due to second-resolution of the time coordinate.

dtree["sweep_0"].ds.DBZH.sortby("time").plot(y="time")

<matplotlib.collections.QuadMesh at 0x78d410b5bb10>

../_images/217ae8870f7cffb545f60928cd0f5e1ee472df430fa0111f9b321115b2f33b77.png

Plot Sweep Range vs. Azimuth#

dtree["sweep_0"].ds.DBZH.plot()

<matplotlib.collections.QuadMesh at 0x78d410735f90>

../_images/630b0220635039d76e27679f6244392f6ff8c6f832d1a99aac9134357bc5f818.png

import matplotlib.pyplot as plt

sweep = dtree["sweep_0"].to_dataset(inherit="all_coords")
sweep = sweep.sel(range=slice(0, 60000))
sweep = xd.georeference.get_x_y_z(sweep)
for var in xd.util.get_sweep_dataset_vars(sweep):
    plt.figure()
    sweep[var].plot(x="x", y="y")
    plt.title(var)

/home/docs/checkouts/readthedocs.org/user_builds/xradar/conda/latest/lib/python3.13/site-packages/xradar/io/backends/iris.py:254: RuntimeWarning: invalid value encountered in sqrt
  return np.sqrt(decode_array(data, **kwargs))