CfRadial1 - Export#

Imports#

import cmweather  # noqa
import xarray as xr
from open_radar_data import DATASETS

import xradar as xd

Plot Azimuth vs. Range#

radar.sweep_0.DBZ.plot(cmap="ChaseSpectral", vmin=-10, vmax=70)

<matplotlib.collections.QuadMesh at 0x7d9a36afb0e0>

../_images/07a457472c1301af7892da045b19289c203440fa42e39789ec5697a8c9810ddc.png

Plot Time vs. Range#

radar.sweep_0.DBZ.swap_dims({"azimuth": "time"}).sortby("time").plot(
    cmap="ChaseSpectral", vmin=-10, vmax=70
)

<matplotlib.collections.QuadMesh at 0x7d9a2db860d0>

../_images/ebe759f5d013e1e83c6d927748822fe5289119ddde67eeada41ddb6420a17b6d.png

Plot PPI#

radar["sweep_0"]["DBZ"].plot(x="x", y="y", cmap="ChaseSpectral", vmin=-10, vmax=70)

<matplotlib.collections.QuadMesh at 0x7d9a36a74e10>

../_images/b8ad64cae40a3708e25d28992c2a7b0b0a8693132d26c9a49afaf2966285ce54.png

Filter#

Apply basic reflectivity filter. This is just a demonstration.

def ref_filter(dtree, sweep="sweep_0", field="DBZ"):
    ds = dtree[sweep].ds
    ds = ds.where((ds[field] >= -10) & (ds[field] <= 70))
    red_patch = ds.where(
        (
            (ds[field] >= ds[field].max().values - 0.5)
            & (ds[field] <= ds[field].max().values + 0.5)
        ),
        drop=True,
    )
    rmin, rmax = int(red_patch.range.min().values - 150), int(
        red_patch.range.max().values + 150
    )
    out_of_range_mask = (ds.range < rmin) | (ds.range > rmax)
    ds[field] = ds[field].where(out_of_range_mask)
    # Interpolate missing values using the slinear method along the 'range' dimension
    ds[field] = ds[field].interpolate_na(dim="range", method="slinear")
    dtree[sweep][f"corr_{field}"] = ds[field].copy()
    return dtree[sweep]

swp0 = ref_filter(radar, sweep="sweep_0", field="DBZ")

swp0.corr_DBZ.plot(x="x", y="y", cmap="ChaseSpectral", vmin=-10, vmax=70)

<matplotlib.collections.QuadMesh at 0x7d9a36995950>

../_images/20e792adbe2e44b9c6d2e9c70bd1934b3651146d36a0a307b10af388cdd4f7b3.png

Filter full volume#

# Initialize an empty DataTree
result_tree = xr.DataTree()

for sweep in radar.sweep_group_name.values:
    corrected_data = ref_filter(radar, sweep, field="DBZ")

    # Convert the xarray Dataset to a DataTree and add it to the result_tree
    data_tree = xr.DataTree.from_dict(corrected_data.to_dict())

    # Copy the contents of data_tree into result_tree
    for key, value in data_tree.items():
        result_tree[key] = value

radar.sweep_6.corr_DBZ.plot(x="x", y="y", cmap="ChaseSpectral", vmin=-10, vmax=70)

<matplotlib.collections.QuadMesh at 0x7d9a2abe5a90>

../_images/3dfc86a0327fa87b2fe09c793cfe3a3104744631816497029a59c6ed296f33a9.png

Export#

Export to CfRadial1

xd.io.to_cfradial1(dtree=radar, filename="cfradial1_qced.nc", calibs=True)

?xd.io.to_cfradial1

Note#

If filename is None in the xd.io.to_cfradial1 function, it will automatically generate a
filename using the instrument name and the first available timestamp from the data.