activity
20172026
most citedFourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

455 citations · 930 across the 30 of their papers we have counts for

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Showing 2026Show all

7 papers · 1 filter

physics.ao-ph2026

reGRAF: a global MPAS reforecast data set with convection-allowing refinements over the US and Europe

Thomas M Hamill, Akshay Subramaniam, Montgomery Flora +8

NVIDIA and The Weather Company (TWC) have generated a data set of reforecasts from TWC's GRAF (Global high-Resolution Atmospheric Forecasting) model, a version of the National Cent…

physics.ao-ph2026

Anomalous Diffusion of Tropical Cyclones Observed in Huge Ensembles of Hindcasts

Abdoul R. Zeba, William D. Collins, Ankur Mahesh +5

We examine whether tropical cyclones (TCs) obey ordinary Brownian or anomalous diffusion using a huge ensemble (HENS) of hindcasts for summer 2023. Anomalous diffusion has been inf…

physics.ao-ph2026★ 2 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)…

stat.AP2026

Surface temperature extremes produced by huge machine learning hindcasts of summer 2023

Mark Risser, Ankur Mahesh, William D. Collins +6

The summer of 2023 brought record-breaking heat extremes across the globe. In this work, we investigate how much worse these extremes might have become, given identical large-scale…

physics.ao-ph2026

Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators

Ankur Mahesh, William D. Collins, Travis A. O'Brien +10

The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and slow feedbacks. Slow feedbacks are typic…

physics.ao-ph2026

HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts

Aayush Gupta, Akshay Subramaniam, Michael S. Pritchard +6

AI weather models now rival leading numerical weather prediction (NWP) systems in medium-range skill. However, almost all still rely on NWP data assimilation (DA) to provide initia…