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Found 4 papers, 2 papers with code
Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model
Results show that the CGAN outputs behave similarly to a numerical ensemble; they preserved the inter-variable correlations and the contribution of influential predictors as in the original HRRR forecasts.
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
In order to encourage broader adoption of evidential deep learning in Earth System Science, we have developed a new Python package, MILES-GUESS (https://github. com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.
The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences
In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system.
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
Some of the GAN configurations perform better than a baseline bespoke parameterization at both timescales, and the networks closely reproduce the spatio-temporal correlations and regimes of the Lorenz '96 system.
