Vertical Profile of Reflectivity (VPR)

Precipitation is 3-dimensional in space. The vertical distribution of precipitation (and thus reflectivity) is typically non-uniform. As the height of the radar beam increases with the distance from the radar location (beam elevation, earth curvature), one sweep samples from different heights. The effects of the non-uniform VPR and the different sampling heights need to be accounted for if we are interested in the precipitation near the ground or in defined heights. This module is intended to provide a set of tools to account for these effects.

The first step will normally be to reference the polar volume data in a 3-dimensional Cartesian coordinate system. The three dimensional Cartesian coordinates of the original polar volume data can be computed using wradlib.vpr.volcoords_from_polar.

Then, we can create regular 3-D grids in order to analyse the vertical profile of reflectivity or rainfall intensity. For some applications you might want to create so-called Constant Altitude Plan Position Indicators (CAPPI) in order to make radar observations at different distances from the radar more comparable. Basically, a CAPPI is simply one slice out of a 3-D volume grid. Analoguous, we will refer to the elements in a three dimensional Cartesian grid as voxels. In wradlib, you can create CAPPIS (CAPPI) and Pseudo CAPPIs (PseudoCAPPI) for different altitudes at once.

Here’s an example how a set of CAPPIs can be created from synthetic polar volume data:

>>> import wradlib
>>> import numpy as np
>>> from osgeo import osr
>>> import matplotlib.pyplot as pl
>>> pl.interactive(True)
>>> # define elevation and azimuth angles, ranges, radar site coordinates,
>>> # projection
>>> elevs  = np.array([0.5,1.5,2.4,3.4,4.3,5.3,6.2,7.5,8.7,10,12,14,16.7,19.5])
>>> azims  = np.arange(0., 360., 1.)
>>> ranges = np.arange(0., 120000., 1000.)
>>> sitecoords = (120.255547,14.924218,500.)
>>> proj = osr.SpatialReference()
>>> _ = proj.ImportFromEPSG(32651)
>>> # create Cartesian coordinates corresponding the location of the
>>> # polar volume bins
>>> polxyz  = wradlib.vpr.volcoords_from_polar(sitecoords, elevs,
...                                            azims, ranges, proj)  # noqa
>>> poldata = wradlib.vpr.synthetic_polar_volume(polxyz)
>>> # this is the shape of our polar volume
>>> polshape = (len(elevs),len(azims),len(ranges))
>>> # now we define the coordinates for the 3-D grid (the CAPPI layers)
>>> x = np.linspace(polxyz[:,0].min(), polxyz[:,0].max(), 120)
>>> y = np.linspace(polxyz[:,1].min(), polxyz[:,1].max(), 120)
>>> z = np.arange(500.,10500.,500.)
>>> xyz = wradlib.util.gridaspoints(z, y, x)
>>> gridshape = (len(z), len(y), len(x))
>>> # create an instance of the CAPPI class and
>>> # use it to create a series of CAPPIs
>>> gridder = wradlib.vpr.CAPPI(polxyz, xyz, maxrange=ranges.max(),  # noqa
...                             minelev=elevs.min(), maxelev=elevs.max(),
...                             ipclass=wradlib.ipol.Idw)
>>> gridded = np.ma.masked_invalid( gridder(poldata) ).reshape(gridshape)
>>>
>>> # plot results
>>> levels = np.linspace(0,100,25)
>>> wradlib.vis.plot_max_plan_and_vert(x, y, z, gridded, levels=levels,
...                                    cmap=pl.cm.viridis)
>>> pl.show()

volcoords_from_polar

Create Cartesian coordinates for regular polar volumes

make_3d_grid

Generate Cartesian coordinates for a regular 3-D grid based on radar specs.

norm_vpr_stats

Returns the average normalised vertical profile of a volume or any other desired statistics

CartesianVolume

Create 3-D regular volume grid in Cartesian coordinates from polar data with multiple elevation angles

CAPPI

Create a Constant Altitude Plan Position Indicator (CAPPI)

PseudoCAPPI

Create a Pseudo-CAPPI Constant Altitude Plan Position Indicator (CAPPI)

out_of_range

Masks the region outside the radar range

blindspots

Masks blind regions of the radar, marked on a 3-D grid