26 citations · 33 across the 3 of their papers we have counts for
5 papers
Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach
Da Fan, David John Gagne, Gregory S Elsaesser +5
Perturbed parameter ensembles (PPEs) reveal how physics parameters affect climate simulations, but interpreting parameter sensitivities across multivariate, spatially structured ou…
A Self-Diagnosing Structural Error-Aware Parameter Estimation Method for Earth System Models
Qingyuan Yang, Addisu G Semie, Brian Medeiros +3
We propose a fully automated, structural error-aware, interpretable climate model parameter estimation method that leverages Perturbed Parameter Ensembles (PPEs). It is based on hi…
Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation
Da Fan, David John Gagne, Steven J. Greybush +3
This study evaluated the probability and uncertainty forecasts of five recently proposed Bayesian deep learning methods relative to a deterministic residual neural network (ResNet)…
Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations
Da Fan, Steven J. Greybush, David John Gagne +1
Convection initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, object-based pro…
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
John S. Schreck, David John Gagne, Charlie Becker +13
Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates an…