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https://github.com/djgagne/hagelslag
Hagelslag supports segmentation and tracking of weather fields and scalable verification, including performance diagrams and reliability diagrams.
https://github.com/djgagne/hagelslag
geojson hail hrrr machine-learning mrms netcdf performance performance-diagram python reliability segmentation storms tracking verification weather zarr
Last synced: 3 months ago
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Hagelslag supports segmentation and tracking of weather fields and scalable verification, including performance diagrams and reliability diagrams.
- Host: GitHub
- URL: https://github.com/djgagne/hagelslag
- Owner: djgagne
- License: mit
- Created: 2015-06-16T20:48:43.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2024-02-10T00:50:36.000Z (9 months ago)
- Last Synced: 2024-05-20T10:14:22.164Z (6 months ago)
- Topics: geojson, hail, hrrr, machine-learning, mrms, netcdf, performance, performance-diagram, python, reliability, segmentation, storms, tracking, verification, weather, zarr
- Language: Jupyter Notebook
- Homepage:
- Size: 91.3 MB
- Stars: 67
- Watchers: 16
- Forks: 27
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- open-sustainable-technology - Hagelslag - An object-based severe storm forecasting system that utilizing image processing and machine learning tools to derive calibrated probabilities of severe hazards from convection-allowing numerical weather prediction model output. (Climate Change / Natural Hazard and Storm)
README
# Hagelslag
## Storm tracking, machine learning, and probabilistic evaluation
[](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1261776&HistoricalAwards=false)
Hagelslag is an object-based severe storm forecasting system that utilizing image processing and machine learning tools
to derive calibrated probabilities of severe hazards from convection-allowing numerical weather prediction model output.
The package contains modules for storm identification and tracking, spatio-temporal data extraction, and
machine learning model training to predict hazard intensity as well as space and time translations.
### Citation
If you employ hagelslag in your research, please acknowledge its use with the following citations:
Gagne, D. J., A. McGovern, S. E. Haupt, R. A. Sobash, J. K. Williams, M. Xue, 2017: Storm-Based Probabilistic Hail
Forecasting with Machine Learning Applied to Convection-Allowing Ensembles, Wea. Forecasting, 32, 1819-1840.
https://doi.org/10.1175/WAF-D-17-0010.1.
Gagne II, D. J., A. McGovern, N. Snook, R. Sobash, J. Labriola, J. K. Williams, S. E. Haupt, and M. Xue, 2016:
Hagelslag: Scalable object-based severe weather analysis and forecasting. Proceedings of the Sixth Symposium on
Advances in Modeling and Analysis Using Python, New Orleans, LA, Amer. Meteor. Soc., 447.
If you discover any issues, please post them to the Github issue tracker page. Questions and comments should be sent to
djgagne at ou dot edu.
### Requirements
Hagelslag is compatible with Python 3.6 or newer. Hagelslag is easiest to install with the help of the [Miniconda
Python Distribution](https://docs.conda.io/en/latest/miniconda.html), but it should work with other
Python setups as well. Hagelslag requires the following packages and recommends the following versions:
* numpy >= 1.10
* scipy >= 0.15
* matplotlib >= 1.4
* scikit-learn >= 0.16
* pandas >= 0.15
* arrow >= 0.8.0
* pyproj
* netCDF4-python
* xarray
* jupyter
* ncepgrib2
* pygrib
* cython
* pip
* sphinx
* mock
Install dependencies with the following commands:
```
git clone https://github.com/djgagne/hagelslag.git
cd ~/hagelslag
conda env create -f environment.yml
conda activate hagelslag
```
### Installation
Install the latest version of hagelslag with the following command from the top-level hagelslag directory (where setup.py
is):
`pip install .`
Hagelslag will install the libraries in site-packages and will also install 3 applications into the `bin` directory
of your Python installation.
### Use
A Jupyter notebook is located in the demos directory that showcases the functionality of the package. For larger scale
use, 3 scripts are provided in the bin directory.
* `hsdata` performs object tracking and matching as well as data processing.
* `hsfore` trains and applies machine learning models.
* `hseval` performs forecast verification.
All scripts take input from a config file. The config file should be valid Python code and contain a dictionary called
config. Custom machine learning models and parameters should be contained within the config files. Examples of them can
be found in the config directory.
### Documentation
API Documentation is available [here](http://hagelslag.readthedocs.io/en/latest/).