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.
If you employ hagelslag in your research, please acknowledge its use with the following citation:
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.
Hagelslag is compatible with Python 2.7 and 3.5. Hagelslag is easiest to install with the help of the Anaconda Python Distribution, 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
To install hagelslag, enter the top-level directory of the package and run the standard python setup command:
python setup.py install
Hagelslag will install the libraries in site-packages and will also install 3 applications into the
of your Python installation.
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.
hsdataperforms object tracking and matching as well as data processing.
hsforetrains and applies machine learning models.
hsevalperforms 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.
API Documentation is available here.