wradlib.dp.process_raw_phidp_vulpiani#
- wradlib.dp.process_raw_phidp_vulpiani(phidp, dr, ndespeckle=5, winlen=7, niter=2, copy=False, **kwargs)[source]#
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:
phidp (
numpy.ndarray
) – array of shape (n azimuth angles, n range gates)dr (
float
) – gate length in kmwinlen (
int
) –winlen
parameter ofkdp_from_phidp
niter (
int
) – Number of iterations in which \(Phi_{DP}\) is retrieved from \(K_{DP}\) and vice versacopy (
bool
) – if True, the original \(Phi_{DP}\) array will remain unchanged
- 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.