26 citations · 29 across the 7 of their papers we have counts for
9 papers
GOES-East full-disk AI nowcasting of cloud evolution in observation space
Dhamma Kimpara, Omid Bagheri, Ivette Hernandez Banos +2
Clouds affect aviation, solar energy, remote sensing, and storm prediction, yet they remain among the hardest atmospheric features to forecast, particularly at convective scales. B…
Controllable Probabilistic Forecasting with Stochastic Decomposition Layers
John S. Schreck, William E. Chapman, Charlie Becker +6
AI weather prediction ensembles with latent noise injection and optimized with the continuous ranked probability score (CRPS) have produced both accurate and well-calibrated predic…
Winter Precipitation Type Diagnosis and Uncertainty Quantification with a Physically Consistent Machine Learning Method
Charlie Becker, David John Gagne, Julie Demuth +8
Accurately forecasting winter precipitation type and its transitions is critical for high-impact decision making. However, existing methods struggle in thermodynamically ambiguous…
Consistency Conditions for Differentiable Surrogate Losses
Drona Khurana, Anish Thilagar, Dhamma Kimpara +1
The statistical consistency of surrogate losses for discrete prediction tasks is often checked via the condition of calibration. However, directly verifying calibration can be ardu…
CAMulator: Fast Emulation of the Community Atmosphere Model
William E. Chapman, John S. Schreck, Yingkai Sha +5
We introduce CAMulator version 1, an auto-regressive machine-learned (ML) emulator of the Community Atmosphere Model version 6 (CAM6) that simulates the next atmospheric state give…
Community Research Earth Digital Intelligence Twin (CREDIT)
John Schreck, Yingkai Sha, William Chapman +7
Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional…
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode
Enrique Nueve, Bo Waggoner, Dhamma Kimpara +1
In multiclass classification over outcomes, the outcomes must be embedded into the reals with dimension at least in order to design a consistent surrogate loss that leads…
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…
Proper losses for discrete generative models
Rafael Frongillo, Dhamma Kimpara, Bo Waggoner
We initiate the study of proper losses for evaluating generative models in the discrete setting. Unlike traditional proper losses, we treat both the generative model and the target…