works on

From the 1 of 19 linked papers with an AI index.

activity
20242026
most citedScaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet

2 citations · 3 across the 5 of their papers we have counts for

collaborators
Showing physics.ao-phShow all

10 papers · 1 filter

physics.ao-ph20261 cited

AIMIP Phase 1: systematic evaluations of AI weather and climate models

Brian Henn, Christopher S. Bretherton, Nikolay Koldunov +18

The paper introduces AIMIP Phase 1, an intercomparison framework for AI‑based weather and climate models that evaluates their ability to simulate historical atmospheric conditions…

physics.ao-ph20262 cited

Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet

Zeyuan Hu, Akshay Subramaniam, Noel Keen +9

Kilometer-scale convection shapes precipitation extremes, tropical organization, and cloud feedbacks, but most global atmospheric models approximate these processes at 25-100 km re…

physics.ao-ph2026

Towards accurate extreme event likelihoods from diffusion model climate emulators

Peter Manshausen, Noah Brenowitz, Julius Berner +2

ML climate model emulators are useful for scenario planning and adaptation, allowing for cost-efficient experimentation. Recently, the diffusion model Climate in a Bottle (cBottle)…

physics.ao-ph2026

Learning Accurate Storm-Scale Evolution from Observations

Jaideep Pathak, Mohammad Shoaib Abbas, Peter Harrington +10

Accurate short-term prediction of clouds and precipitation is critical for severe weather warnings, aviation safety, and renewable energy operations. Forecasts at this timescale ar…

physics.ao-ph2025

Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere

Noah D. Brenowitz, Tao Ge, Akshay Subramaniam +7

Climate modeling is reaching unprecedented resolution, producing petabytes of data. AI climate model emulators offer a path to computationally cheap analysis, enabling new scientif…

physics.ao-ph2025

Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators

Ankur Mahesh, William Collins, Boris Bonev +13

Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems pr…