Metadata-Version: 2.4
Name: pyhail
Version: 3.3.2
Summary: Python hail retreivals
Home-page: https://github.com/joshuass/pyhail
Author: Joshua Soderholm
Author-email: Joshua Soderholm <joshua.soderholm@bom.gov.au>
Project-URL: Homepage, https://github.com/joshua-wx/pyhail
Project-URL: Bug Tracker, https://github.com/joshua-wx/pyhail/issues
Keywords: radar,weather,meteorology,calibration
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: numba>=0.59.1
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

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# 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](https://github.com/ARM-DOE/pyart)
- [PyOdim](https://github.com/vlouf/pyodim)

### Notebook plotting Dependencies
- matplotlib

### Hail Retrivals
- *Hail Size Discrimination Algorithm - HSDA ([Ortega et al. 2016](https://journals.ametsoc.org/doi/10.1175/JAMC-D-15-0203.1))
- Hail Differential Reflectivity - HDR ([Depue et al. 2007](https://doi.org/10.1175/JAM2529.1))
- Maximum Expected Size of Hail - MESH witt1998 ([Witt et al. 1998](https://journals.ametsoc.org/doi/10.1175/1520-0434%281998%29013%3C0286%3AAEHDAF%3E2.0.CO%3B2))
- Maximum Expected Size of Hail - MESH mh2019_75/mh2019_95 ([Murillo and Homeyer 2019](https://journals.ametsoc.org/view/journals/apme/58/5/jamc-d-18-0247.1.xml))
- Accumulated Hail - hAcc ([Wallace et al. 2019](https://journals.ametsoc.org/view/journals/wefo/34/1/waf-d-18-0053_1.xml))

*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 git@github.com:joshua-wx/pyhail.git`

To install in your home directory, use:

`python setup.py install --user`

### Use
- [Example Notebook](https://github.com/joshua-wx/pyhail/blob/master/notebooks/example.ipynb)

### 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.
