4 papers
HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts
Aayush Gupta, Akshay Subramaniam, Michael S. Pritchard +6
AI weather models now rival leading numerical weather prediction (NWP) systems in medium-range skill. However, almost all still rely on NWP data assimilation (DA) to provide initia…
Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models
Kelsey Lieberman, Laura Slivinski, Matt Bender +6
Satellite observation operators play an essential role in atmospheric data assimilation by translating model state variables into observation space. Previous work has shown that de…
Training Over a Distribution of Hyperparameters for Enhanced Performance and Adaptability on Imbalanced Classification
Kelsey Lieberman, Swarna Kamlam Ravindran, Shuai Yuan +1
Although binary classification is a well-studied problem, training reliable classifiers under severe class imbalance remains a challenge. Recent techniques mitigate the ill effects…
Optimizing for ROC Curves on Class-Imbalanced Data by Training over a Family of Loss Functions
Kelsey Lieberman, Shuai Yuan, Swarna Kamlam Ravindran +1
Although binary classification is a well-studied problem in computer vision, training reliable classifiers under severe class imbalance remains a challenging problem. Recent work h…