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

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

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

Machine learning emulation of precipitation from km-scale UK regional climate simulations using a diffusion model

Henry Addison, Elizabeth Kendon, Suman Ravuri +2

High-resolution climate simulations are valuable for understanding climate change impacts. This has motivated use of regional convection-permitting climate models (CPMs), but these…

cs.LG2026

Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting

Jean Kossaifi, Nikola Kovachki, Morteza Mardani +15

The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fun…

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…

cs.LG2024

Neural Compression of Atmospheric States

Piotr Mirowski, David Warde-Farley, Mihaela Rosca +7

Atmospheric states derived from reanalysis comprise a substantial portion of weather and climate simulation outputs. Many stakeholders -- such as researchers, policy makers, and in…