Accepted papers
SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale
Jason Stock, Kyle Hilburn, Imme Ebert-Uphoff, Charles Anderson
Using Neural Networks for Data Cleaning in Weather Datasets
Jack R. P. Hanslope, Laurence Aitchison
Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network
Zesheng Liu, Maryam Rahnemoonfar
Younghyun Koo, Maryam Rahnemoonfar
Valid Error Bars for Neural Weather Models using Conformal Prediction
Vignesh Gopakumar, Joel Oskarrson, Ander Gray, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt Kusner, Marc Deisenroth
Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay, Romit Maulik
Transfer Learning for Emulating Ocean Climate Variability across CO2 forcing (oral)
Surya Dheeshjith, Adam Subel, Shubham Gupta, Alistair Adcroft, Carlos Fernandez-Granda, Julius Busecke, Laure Zanna
ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution
Guillaume Couairon, Christian Lessig, Anastase Charantonis, Claire Monteleoni
Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer, Christopher S. Bretherton, Rose Yu
A Generative Machine Learning Approach for Improving Precipitation from Earth System Models
Philipp Hess, Niklas Boers
Dynamic Basis Function Interpolation for Adaptive In Situ Data Integration in Ocean Modeling
Derek DeSantis, Ayan Biswas Earl Lawrence, Phillip Wolfram
Towards diffusion models for large-scale sea-ice modelling
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Julien Brajard
Learning Optimal Filters Using Variational Inference (oral)
Enoch Luk, Eviatar Bach, Ricardo Baptista, Andrew Stuart
Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation (oral)
Aman Gupta, Aditi Sheshadri, Sujit Roy, Vishal Gaur, Manil Maskey, Rahul Ramachandran
VarteX: Enhancing Weather Forecast through Distributed Variable Representation
Ayumu Ueyama, Kazuhiko Kawamoto, Hiroshi Kera
Latent Diffusion Model for Generating Ensembles of Climate Simulations
Johannes Meuer, Maximilian Witte, Tobias Sebastian Finn, Claudia Timmreck, Thomas Ludwig, Christopher Kadow
A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling
Jose González-Abad
Jun Sasaki, Maki Okada, Kenji Utsunomiya, Koji Yamaguchi
Moritz Feik, Sebastian Lerch, Jan Stühmer
Evaluating the transferability potential of deep learning models for climate downscaling
Ayush Prasad, Paula Harder, Qidong Yang, Prasanna Sattegeri, Daniela Szwarcman, Campbell Watson, David Rolnick