A python package which can read CINRAD radar data, perform calculations and visualize the data.
example
folder contains detailed examples!
pip install cinrad
Python 3.5 +
Cartopy
Metpy
Shapefile
Pykdtree (optional)
You can also download from github page and build from source
python setup.py install
This submodule contains data structure used in this program.
Radial type data: cinrad.datastruct.Radial
Cross-section type data: cinrad.datastruct.Slice_
Grid type data: cinrad.datastruct.Grid
Decode CINRAD radar data.
from cinrad.io import CinradReader, StandardData
f = CinradReader(your_radar_file) #Old version data
f = StandardData(your_radar_file) #New standard data
f.get_data(tilt, drange, dtype) #Get data
f.get_raw(tilt, drange, dtype)
The get_raw
method returns radar records without other geographic information.
Convert data structure defined in this module into pyart.core.Radar
is very simple. cinrad.io.export
has a function standard_data_to_pyart
, which can take cinrad.io.StandardData
as input and return pyart.core.Radar
as output.
example
folder contains a simple demo about this.
cinrad.io.PUP
provides functions to decode PUP data. The extracted data can be further used to create PPI. (Only radial data are supported.)
cinrad.io.SWAN
provides similar interface to decode SWAN data.
from cinrad.io import PUP
f = PUP(your_radar_file)
data = f.get_data()
This submodule provides some useful algorithms in radar meteorology. All functions only accept numpy.ndarray
as input data. This submodule extends the usage of this program, as these functions can accept customized data rather than only the data decoded by cinrad.io
.
For direct computation of decoded data, cinrad.easycalc
provides functions that simplify the process of calculation. For functions contained in this submodule, only a list of reflectivity data is required as the argument.
Code to generate the required list:
r_list = [f.get_data(i, 230, 'REF') for i in f.angleindex_r]
# or
r_list = list(f.iter_tilt(230, 'REF'))
cinrad.easycalc.VCS
provides calculation of vertical cross-section for all variables.
import cinrad
from cinrad.visualize import Section
f = cinrad.io.CinradReader(your_radar_file)
rl = [f.get_data(i, 230, 'REF') for i in f.angleindex_r]
vcs = cinrad.easycalc.VCS(rl)
sec = vcs.get_section(start_cart=(111, 25.5), end_cart=(112, 26.7)) # pass geographic coordinates (longitude, latitude)
sec = vcs.get_section(start_polar=(115, 350), end_polar=(130, 30)) # pass polar coordinates (distance, azimuth)
fig = Section(sec)
fig('D:\\')
cinrad.easycalc.GridMapper
can merge different radar scans into a cartesian grid.
This submodule provides algorithms to correct raw radar fields.
This function can unwrap the folded velocity using algorithm originated from pyart
. (needs C compiler)
import cinrad
#(some codes omitted)
v = f.get_data(1, 230, 'VEL')
v_corrected = cinrad.correct.dealias(v)
Visualize the data stored in acceptable format (cinrad.datastruct
). It also means that you can using customized data to construct a object belongs to one of the class in cinrad.datastruct
and then perform visualization. For further information about this method, please see the examples contained in example
folder.
from cinrad.visualize import PPI
fig = PPI(R) #Plot PPI
fig('D:\\') #Pass the path to save the fig
from cinrad.visualize import Section
fig = Section(Slice_) #Plot VCS
fig('D:\\')
from cinrad.visualize import RHI
fig = RHI(rhi) #Plot data from RHI scan mode
fig('D:\\')
The path passed into the class can either be the folder path or the file path. Also, if no path is passed, the figure will be saved at the folder named PyCINRAD
in the home folder (e.g. C:\Users\tom
).
The summary of args that can be passed into PPI
are listed as follows.
arg | function |
---|---|
cmap |
colormaps used for plotting |
norm |
norm used for plotting |
nlabel |
number of labels on the colorbar |
label |
labels on the colorbar |
highlight |
highlight area of input name |
dpi |
dpi of figure |
extent |
area to plot e.g. extent=[90, 91, 29, 30] |
section |
cross-section data to ppi plot |
style |
control the background color black or white |
add_city_names |
annotate name of city on the plot |
Beside args, class PPI
has some other auxiliary plotting functions.
Plot range rings on the PPI plot.
Plot VCS section under the PPI plot.
This function is very similar to vcs
argument of class PPI
, but the range of y-axis can be adjusted only by this function.
Plot PUP STI product on the current PPI map, including past positions, current position, and forecast positions.
- Recognition and Analysis of Biological Echo Using WSR-88D Dual-polarization Weather Radar in Nanhui of Shanghai doi: 10.16765/j.cnki.1673-7148.2019.03.015
If you are interested in this program, you can join the developers of this program. Any contribution is appreciated!
If you have questions or advise about this program, you can create a issue or email me at [email protected].