11 papers in the archive.
AERIS is a 1.3 to 80B parameter Swin diffusion transformer reaching 10.2 ExaFLOPS on Aurora, stable to 90-day forecasts and outperforming IFS ENS.
Software, scaling & compressionAllen Institute for AI (Ai2, ACE)Argonne National LaboratoryUniversity of California, Los Angeles (UCLA)University of Chicago
LUCIE, a lightweight ERA5-trained emulator trained on 2 years of data, stays stable and climatologically consistent for 100-year, 100-member simulations on one GPU.
Applies a Fourier neural operator with a Clausius–Clapeyron constraint for zero-shot super-resolution of WRF near-surface heat fields over the Great Lakes, reaching 1-km resolution.
ResNet50 classifier on an edge device classifies ARM Doppler lidar cloud and clear-air images with 97.6% and 94.7% accuracy.
A graph neural network predicts regional heatwaves from weather station data, enabling real-time warnings at lower computational and data cost.
A rotation-invariant autoencoder with clustering yields AICCA, a 22-year atlas of 42 unsupervised cloud classes from 198 million MODIS image patches.
Observations & remote sensingArgonne National LaboratoryUniversity of Chicago
Replaces the forward model in variational data assimilation with a differentiable learned emulator, speeding geopotential height DA by four orders of magnitude.
CNNs trained with MSE and conditional GANs emulate 12 km precipitation from 50 km simulations, running 30 years in 14 minutes versus one month.
Proposes machine learning algorithms for uncertainty quantification in numerical weather prediction models; no abstract is available to confirm details.
Explainability, uncertainty & physicsArgonne National Laboratory
Domain-aware deep neural network emulates a WRF planetary boundary layer scheme over the full diurnal cycle and transfers to nearby locations.
Hybrid physics-ML & parameterizationArgonne National Laboratory
Proposes machine-learning-based adaptive covariance localization for ensemble Kalman filters, tuning the localization radius in time and space to counter sampling errors.
