24 citations · 37 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
Watch an AI Weather Model Learn (and Unlearn) Tropical Cyclones
Rebecca Baiman, Ankur Mahesh, Elizabeth A. Barnes
In a changing climate, artificial intelligence (AI) weather models have the potential to provide cheaper, faster, and more accurate forecasts of high-impact weather events. To real…
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…
Constraining Atmospheric River Uncertainty Using Instantaneous Poleward Latent Heat Transport
Ankur Mahesh, William D. Collins, William R. Boos +2
Atmospheric rivers (ARs) are extreme weather events that play a crucial role in the global hydrological cycle. As a key mechanism of latent heat transport (LHT), they help maintain…
How does an AI Weather Model Learn to Forecast Extreme Weather?
Rebecca Baiman, Elizabeth A. Barnes, Ankur Mahesh
In a warming climate with more frequent severe weather, artificial intelligence (AI) weather models have the potential to provide cheaper, faster, and more accurate forecasts of hi…
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…