pyhail 3.3.2

Python hail retreivals

Python Hail Retrieval Toolkit (pyhail) ⛈️📡🧊

This toolkit provides a collection of hail retrieval techniques for weather radar data.

Library Dependencies

• numpy
• scipy
• numba

Supporter radar file readers

• PyART
• PyOdim

Notebook plotting Dependencies

• matplotlib

Hail Retrivals

• *Hail Size Discrimination Algorithm - HSDA (Ortega et al. 2016)
• Hail Differential Reflectivity - HDR (Depue et al. 2007)
• Maximum Expected Size of Hail - MESH witt1998 (Witt et al. 1998)
• Maximum Expected Size of Hail - MESH mh2019_75/mh2019_95 (Murillo and Homeyer 2019)
• Accumulated Hail - hAcc (Wallace et al. 2019)

*Note that the Q confidence vector from Park et al. 2009 has not been implemented and all pixels are assigned a value of q=1.

MESH is implemented for both pyart radar (PPI) and grid (Cartesian) data!

Install using pypi

pip install pyhail

Install from source

To install pyhail, you can either download and unpack the zip file of the source code or use git to checkout the repository:

git clone [e-mail]:joshua-wx/pyhail.git

To install in your home directory, use:

python setup.py install --user

Use

• Example Notebook

Test files

The test file for the pyart and pyodim test notebooks is located at notebooks/data For the c_band_mesh_correction script, the test files are located on gadi. Please contact if you need access.

This project is maintained by Joshua Soderholm (aura at bom.gov.au). Any problems? Please use the Github issue tracker.