List of useful resources
Python software
- Machine Learning in Python: https://scikit-learn.org/
- Scalable Machine Learning with Dask: https://ml.dask.org/
- Metadata-aware sklearn: https://github.com/phausamann/sklearn-xarray
- Batch generation from xarray datasets: https://github.com/pangeo-data/xbatcher
- Parallel Machine Learning: https://ensemble-learning-models.readthedocs.io
- Metrics for verifying forecasts: https://github.com/xarray-contrib/xskillscore
- Verification of weather and climate forecasts: https://github.com/pangeo-data/climpred
- https://keras.io/ https://www.tensorflow.org/ https://pytorch.org/tutorials/
Python ML examples
- this repository
- https://earthml.holoviz.org
Machine Learning NWP Predictions
- Deep learning models for global weather prediction on a cubed sphere: https://github.com/jweyn/DLWP-CS
- post-processing experiments with neural networks: https://github.com/slerch/ppnn
- A benchmark dataset for data-driven weather forecasting: https://github.com/pangeo-data/WeatherBench
Subseasonal Papers
- Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C., Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J., Kang, H.-S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I., … Zhang, L. (2017). The Subseasonal to Seasonal (S2S) Prediction Project Database. Bulletin of the American Meteorological Society, 98(1), 163–173. https://journals.ametsoc.org/view/journals/bams/98/1/bams-d-16-0017.1.xml
- Pegion, K., Kirtman, B. P., Becker, E., Collins, D. C., LaJoie, E., Burgman, R., Bell, R., DelSole, T., Min, D., Zhu, Y., Li, W., Sinsky, E., Guan, H., Gottschalck, J., Metzger, E. J., Barton, N. P., Achuthavarier, D., Marshak, J., Koster, R. D., … Kim, H. (2019). The Subseasonal Experiment (SubX): A Multimodel Subseasonal Prediction Experiment. Bulletin of the American Meteorological Society, 100(10), 2043–2060. http://journals.ametsoc.org/doi/full/10.1175/BAMS-D-18-0270.1
Previous competitions
- RODEO I+II:
List to be continued in comments. Feel free to add.