[!WARNING]

New Xarray-based Package for IMD Radar Data "Radarx"

• Looking for a more modern, flexible, and efficient way to work with IMD radar data?
• Check out our new package: Radarx at https://radarx.readthedocs.io

  It’s an xarray-based toolkit built on top of xradar that supports reading, visualizing, and analyzing IMD radar files with ease.

💬 Join the discussion and stay connected with the radar community at openradar.discourse.group

PyScanCf

Creates Py-ART compatible cf-radial data from individual sweeps of Indian Meteorological Department (IMD) Radar data

Description

PyScanCf is a library for creating cfradial (polar) data from IMD radars that contain all 10 sweeps from single scans which are named as (Polar_ABC.nc) as well as gridded radar data from which are named as (grid_ABC.nc). Both formats are compatible for PyART. It uses Pyart to create grid data, so please remember to cite Py-ART as well.

Latest Documentation

https://syedha.com/PyScanCf/

Latest Examples

https://github.com/syedhamidali/pyscancf_examples

Installing from source

Installing PyScanCf from source is the only way to get the latest updates and enhancement to the software that have not yet made it into a release. The latest source code for PyScanCf can be obtained from the GitHub repository, https://github.com/syedhamidali/PyScanCf.git.

How to install::

conda create -n pcf arm_pyart nbclassic git -c conda-forge
conda activate pcf
pip install git+https://github.com/syedhamidali/PyScanCf.git

Or, to install in your home directory, use::

git clone https://github.com/syedhamidali/PyScanCf.git
python setup.py install --user

Or, Install via pip::

pip install pyscancf

Citation

Syed, H. A., Sayyed, I., Kalapureddy, M. C. R., & Grandhi, K. K. (2021). PyScanCf – The library for single sweep datasets of IMD weather radars. Zenodo. doi:10.5281/zenodo.5574160.

PyScanCf Tutorial on Youtube

https://youtu.be/OUrdhe5virA

Documentation

Import Library::

import pyscancf as pcf

Mention the data path::

inp = '/Users/rizvi/Downloads/goa16'

Convert data to cfradial format::

pcf.cfrad(inp,inp,True,'REF')

And you'll see the beautiful gridded data plot in your notebook, the figures will be saved in the directory from where you launched the notebook

Detailed and efficient way to use this toolkit

Detailed Notebook