PyBlock is a Python 2 or 3 module that enables the end user to estimate partial beam blockage using polarimetric radar data. The methodologies it uses depend on the self-consistency of polarimetric radar variables - reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp) - in pure rain. There are two methodologies currently available to the end user, both described in Lang et al. (2009): The KDP method, and the Fully Self-Consistent (FSC) method. Briefly, the KDP method will check the behavior of Zh and Zdr for a given range of Kdp both inside and outside of blocked azimuths, and use that to suggest corrections to these measurands. This is effectively a relative calibration of Z and Zdr. The FSC method uses a derived or specified self-consistency relationship to do an absolute calibration of Zh within the blocked regions. PyBlock implements these methodologies within an object-oriented Python framework.
The following dependencies need to be installed first:
- A robust version of Python 2.7 or 3.4-3.6 (other versions untested) w/ most standard scientific packages (e.g., numpy, matplotlib, pandas, etc.) - Get one for free here.
- The
h5pymodule (available via most package managers; e.g., conda) - [The Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART)] (https://github.com/ARM-DOE/pyart)
- CSU_RadarTools
- SkewT - Python 3 version can be found here.
- [DualPol] (https://github.com/nasa/DualPol)
Specific import calls in the PyBlock source code:
from __future__ import division
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from warnings import warn
import statsmodels.api as sm
import os
import h5py
import pyart
import dualpol
from csu_radartools import csu_misc
import six
To install PyBlock, in the main directory for the package:
python setup.py install
To access everything:
import pyblock
To see PyBlock in action, check out the IPython notebook provided in this distribution.