no code implementations • 4 Apr 2024 • Jason Stock, Jaideep Pathak, Yair Cohen, Mike Pritchard, Piyush Garg, Dale Durran, Morteza Mardani, Noah Brenowitz
This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics.
1 code implementation • 28 Sep 2023 • Jerry Lin, Sungduk Yu, Tom Beucler, Pierre Gentine, David Walling, Mike Pritchard
The implication is that hundreds of candidate ML models should be evaluated online to detect the effects of parameterization design choices.
no code implementations • 24 Sep 2023 • Morteza Mardani, Noah Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Karthik Kashinath, Jan Kautz, Mike Pritchard
Predictions of weather hazard require expensive km-scale simulations driven by coarser global inputs.
1 code implementation • 8 Feb 2023 • Sungduk Yu, Mike Pritchard, Po-Lun Ma, Balwinder Singh, Sam Silva
Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic -- primarily relying on manual or grid searches.
no code implementations • 1 Dec 2021 • Harshini Mangipudi, Griffin Mooers, Mike Pritchard, Tom Beucler, Stephan Mandt
Understanding the details of small-scale convection and storm formation is crucial to accurately represent the larger-scale planetary dynamics.
2 code implementations • 14 Apr 2020 • Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi
Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras.