Search Results for author:
Found 6 papers, 3 papers with code
DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
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.
Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models
The implication is that hundreds of candidate ML models should be evaluated online to detect the effects of parameterization design choices.
Residual Diffusion Modeling for Km-scale Atmospheric Downscaling
Predictions of weather hazard require expensive km-scale simulations driven by coarser global inputs.
Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset
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.
Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs
Understanding the details of small-scale convection and storm formation is crucial to accurately represent the larger-scale planetary dynamics.
A Fortran-Keras Deep Learning Bridge for Scientific Computing
Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras.
