radarx.grid.grid.grid_radar#
- radarx.grid.grid.grid_radar(dtree, data_vars=None, pseudo_cappi=True, x_lim=(-100000.0, 100000.0), y_lim=(-100000.0, 100000.0), z_lim=(0, 10000.0), x_step=1000, y_step=1000, z_step=250, x_smth=0.2, y_smth=0.2, z_smth=1)[source]#
Interpolate radar data to a 3D grid and optionally create a pseudo-CAPPI.
- Parameters:
dtree (
xradar.DataTree
) – Input radar DataTree containing radar sweeps.data_vars (
list
ofstr
, optional) – List of variables to interpolate. If None, all variables in the dataset are used. Defaults to None.pseudo_cappi (
bool
, optional) – If True, extrapolates data to lower altitudes to create a pseudo-CAPPI. Defaults to True.x_lim (
tuple
offloat
, optional) – Range of x-coordinates (meters) for the Cartesian grid. Defaults to (-100e3, 100e3).y_lim (
tuple
offloat
, optional) – Range of y-coordinates (meters) for the Cartesian grid. Defaults to (-100e3, 100e3).z_lim (
tuple
offloat
, optional) – Range of z-coordinates (meters) for the Cartesian grid. Defaults to (0, 16e3).x_step (
int
, optional) – Grid resolution in the x-direction (meters). Defaults to 500.y_step (
int
, optional) – Grid resolution in the y-direction (meters). Defaults to 500.z_step (
int
, optional) – Grid resolution in the z-direction (meters). Defaults to 250.x_smth (
float
, optional) – Smoothing factor for the x-dimension. Defaults to 0.2.y_smth (
float
, optional) – Smoothing factor for the y-dimension. Defaults to 0.2.z_smth (
float
, optional) – Smoothing factor for the z-dimension. Defaults to 1.
- Returns:
xarray.Dataset
– Interpolated dataset with the specified variables and 3D grid. Includes longitude and latitude coordinates for the grid.
Notes
The pseudo-CAPPI is created by extrapolating data from higher altitudes to fill missing values at lower altitudes.
Interpolation is performed using Barnes interpolation.