wradlib.dp.phidp_kdp_vulpiani#
- wradlib.dp.phidp_kdp_vulpiani(obj, dr, *, ndespeckle=5, winlen=7, niter=2, copy=False, **kwargs)[source]#
- wradlib.dp.phidp_kdp_vulpiani(obj: DataArray, *, winlen=7, **kwargs)
Establish consistent \(Phi_{DP}\) profiles from raw data.
This approach is based on [Vulpiani et al., 2012] and involves a two-step procedure of \(Phi_{DP}\) reconstruction.
Processing of raw \(Phi_{DP}\) data contains the following steps:
Despeckle
Initial \(K_{DP}\) estimation
Removal of artifacts
Phase unfolding
\(Phi_{DP}\) reconstruction using iterative estimation of \(K_{DP}\)
- Parameters
obj (
numpy.ndarray
) – array of shape (n azimuth angles, n range gates)dr (
float
) – gate length in kmndespeckle (
int
, optional) –ndespeckle
parameter ofdespeckle
, defaults to 5winlen (
int
, optional) –winlen
parameter ofkdp_from_phidp
, defaults to 7niter (
int
, optional) – Number of iterations in which \(Phi_{DP}\) is retrieved from \(K_{DP}\) and vice versa, defaults to 2.copy (
bool
, optional) – if True, the original \(Phi_{DP}\) array will remain unchanged, defaults to False
- Keyword Arguments
- Returns
phidp (
numpy.ndarray
) – array of shape (…, n azimuth angles, n range gates) reconstructed \(Phi_{DP}\)kdp (
numpy.ndarray
) – array of shape (…, n azimuth angles, n range gates)kdp
estimate corresponding tophidp
output
Examples
See Routine verification measures for radar-based precipitation estimates.