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.
Create Cartesian coordinates for regular polar volumes |
|
Generate Cartesian coordinates for a regular 3-D grid based on radar specs. |
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Returns the average normalised vertical profile of a volume or any other desired statistics |
|
Create 3-D regular volume grid in Cartesian coordinates from polar data with multiple elevation angles |
|
Create a Constant Altitude Plan Position Indicator (CAPPI) |
|
Create a Pseudo-CAPPI Constant Altitude Plan Position Indicator (CAPPI) |
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Masks the region outside the radar range |
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Masks blind regions of the radar, marked on a 3-D grid |
- wradlib.vpr.volcoords_from_polar(site, elevs, azimuths, ranges, *, crs=None)[source]#
Create Cartesian coordinates for regular polar volumes
- Parameters
site (
tuple
) – sequence of three floats indicating the radar position (longitude in decimal degrees, latitude in decimal degrees, height a.s.l. in meters)elevs (sequence) – sequence of elevation angles
azimuths (sequence) – sequence of azimuth angles
ranges (sequence) – sequence of ranges
crs (
osgeo.osr.SpatialReference
) – GDAL OSR Spatial Reference Object describing projection
- Returns
output (
numpy.ndarray
) – Array of shape (num volume bins, 3)
Examples
See Recipe #2: Reading and visualizing an ODIM_H5 polar volume.
- wradlib.vpr.make_3d_grid(site, crs, maxrange, maxalt, horiz_res, vert_res, *, minalt=0.0)[source]#
Generate Cartesian coordinates for a regular 3-D grid based on radar specs.
- Parameters
site (
tuple
) – Radar location coordinates in lon, latcrs (
osgeo.osr.SpatialReference
) – GDAL OSR SRS describing projectionmaxrange (
float
) – maximum radar range (same unit as CRS defined bycrs
, typically meters)maxalt (
float
) – maximum altitude to which the 3-d grid should extend (meters)horiz_res (
float
) – horizontal resolution of the 3-d grid (same unit as CRS defined bycrs
, typically meters)vert_res (
float
) – vertical resolution of the 3-d grid (meters)
- Keyword Arguments
minalt (
float
) – minimum altitude to which the 3-d grid should extend (meters)- Returns
output (
numpy.ndarray
,tuple
) – float array of shape (num grid points, 3), a tuple of 3 representing the grid shape
- wradlib.vpr.norm_vpr_stats(volume, reference_layer, *, stat=None, **kwargs)[source]#
Returns the average normalised vertical profile of a volume or any other desired statistics
Given a Cartesian 3-d
volume
and an arbitraryreference layer
index, the function normalises all vertical profiles represented by the volume and computes a static of all profiles (e.g. an average vertical profile using the defaultstat
).- Parameters
volume (
numpy.ndarray
or) –numpy.ma.MaskedArray
Cartesian 3-d grid with shape (num vertical layers, num x intervals, num y intervals)reference_layer (
int
) – This index defines the vertical layers ofvolume
that is used to normalise all vertical profilesstat (
callable
) – typically a numpy statistics function (defaults to numpy.mean)kwargs (
dict
) – further keyword arguments taken bystat
- Returns
output (
numpy.ndarray
ornumpy.ma.MaskedArray
) – Array of shape (num vertical layers,) which provides the statistic fromstat
applied over all normalised vertical profiles (e.g. the mean normalised vertical profile ifnumpy.mean
is used)
- class wradlib.vpr.CartesianVolume(polcoords, gridcoords, *, maxrange=None, minelev=None, maxelev=None, ipclass=<class 'wradlib.ipol.Idw'>, **ipargs)[source]#
Bases:
object
Create 3-D regular volume grid in Cartesian coordinates from polar data with multiple elevation angles
- Parameters
polcoords (
numpy.ndarray
) – of shape (num bins, 3)gridcoords (
numpy.ndarray
) – of shape (num voxels, 3)maxrange (
float
) – The maximum radar range (must be the same for each elevation angle)minelev (
float
) – The minimum elevation angle of the volume (degree)maxelev (
float
) – The maximum elevation angle of the volume (degree)ipclass (
wradlib.ipol.IpolBase
) – an interpolation class fromwradlib.ipol
ipargs (
dict
) – keyword arguments corresponding toipclass
- Returns
output (
numpy.ndarray
) – float 1-d ndarray of the same length asgridcoords
(num voxels,)
Examples
See Recipe #2: Reading and visualizing an ODIM_H5 polar volume.
- class wradlib.vpr.CAPPI(polcoords, gridcoords, *, maxrange=None, minelev=None, maxelev=None, ipclass=<class 'wradlib.ipol.Idw'>, **ipargs)[source]#
Bases:
CartesianVolume
Create a Constant Altitude Plan Position Indicator (CAPPI)
A CAPPI gives the value of a target variable (typically reflectivity in dBZ, but here also other variables such as e.g. rainfall intensity) in a defined altitude.
In order to create a CAPPI, you first have to create an instance of this class. Calling this instance with the actual polar volume data will return the CAPPI grid.
- Parameters
polcoords (
numpy.ndarray
) – coordinate array of shape (num bins, 3) Represents the 3-D coordinates of the original radar binsgridcoords (
numpy.ndarray
) – coordinate array of shape (num voxels, 3) Represents the 3-D coordinates of the Cartesian gridmaxrange (
float
) – The maximum radar range (must be the same for each elevation angle)ipclass (
wradlib.ipol.IpolBase
) – an interpolation class fromwradlib.ipol
ipargs (
dict
) – keyword arguments corresponding toipclass
- Returns
output (
numpy.ndarray
) – float 1-d ndarray of the same length asgridcoords
(num voxels,)
See also
Examples
See Recipe #2: Reading and visualizing an ODIM_H5 polar volume.
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.) >>> site = (120.255547,14.924218,500.) >>> crs = osr.SpatialReference() >>> _ = crs.ImportFromEPSG(32651) >>> # create Cartesian coordinates corresponding the location of the >>> # polar volume bins >>> polxyz = wradlib.vpr.volcoords_from_polar(site, elevs, ... azims, ranges, crs=crs) # 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()
- class wradlib.vpr.PseudoCAPPI(polcoords, gridcoords, *, maxrange=None, minelev=None, maxelev=None, ipclass=<class 'wradlib.ipol.Idw'>, **ipargs)[source]#
Bases:
CartesianVolume
Create a Pseudo-CAPPI Constant Altitude Plan Position Indicator (CAPPI)
The difference to a CAPPI (
wradlib.vpr.CAPPI
) is that the blind area below and above the radar are not masked, but filled by interpolation. Only the areas beyond the range of the radar are masked out. As a result, “blind” areas below the radar are particularly filled from the lowest available elevation angle.In order to create a Pseudo CAPPI, you first have to create an instance of this class. Calling this instance with the actual polar volume data will return the Pseudo CAPPI grid.
- Parameters
polcoords (
numpy.ndarray
) – coordinate array of shape (num bins, 3) Represents the 3-D coordinates of the original radar binsgridcoords (
numpy.ndarray
) – coordinate array of shape (num voxels, 3) Represents the 3-D coordinates of the Cartesian gridmaxrange (
float
) – The maximum radar range (must be the same for each elevation angle)minelev (
float
) – The minimum elevation angle of the volume (degree)maxelev (
float
) – The maximum elevation angle of the volume (degree)ipclass (
wradlib.ipol.IpolBase
) – an interpolation class fromwradlib.ipol
ipargs (
dict
) – keyword arguments corresponding toipclass
- Returns
output (
numpy.ndarray
) – float 1-d ndarray of the same length asgridcoords
(num voxels,)
See also
Examples
See Recipe #2: Reading and visualizing an ODIM_H5 polar volume.
- wradlib.vpr.out_of_range(center, gridcoords, maxrange)[source]#
Masks the region outside the radar range
- Parameters
center (
tuple
) – radar locationgridcoords (
numpy.ndarray
) – array of 3-D coordinates with shape (num voxels, 3)maxrange (
float
) – maximum range (same unit as gridcoords)
- Returns
output (
numpy.ndarray
) – 1-D Boolean array of length len(gridcoords)
- wradlib.vpr.blindspots(center, gridcoords, minelev, maxelev, maxrange)[source]#
Masks blind regions of the radar, marked on a 3-D grid
The radar is blind below the radar, above the radar and beyond the range. The function returns three boolean arrays which indicate whether (1) the grid node is below the radar, (2) the grid node is above the radar, (3) the grid node is beyond the maximum range.
- Parameters
center (
tuple
) – radar locationgridcoords (
numpy.ndarray
) – array of 3-D coordinates with shape (num voxels, 3)minelev (
float
) – The minimum elevation angle of the volume (degree)maxelev (
float
) – The maximum elevation angle of the volume (degree)maxrange (
float
) – maximum range (same unit as gridcoords)
- Returns
output (
tuple
) – tuple of three boolean arrays (below, above, out_of_range) each of length (num grid points)