12 papers
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…
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…
Surface temperature extremes produced by huge machine learning hindcasts of summer 2023
Mark Risser, Ankur Mahesh, Joshua North +6
The summer of 2023 was the second hottest on record, with numerous extreme heatwaves across the globe. Using the Spherical Fourier Neural Operator machine learning (ML) weather mod…
Physics-Aware Neural Operators for Direct Inversion in 3D Photoacoustic Tomography
Jiayun Wang, Yousuf Aborahama, Arya Khokhar +10
Learning physics-constrained inverse operators-rather than post-processing physics-based reconstructions-is a broadly applicable strategy for problems with expensive forward models…
A Library for Learning Neural Operators
Jean Kossaifi, Nikola Kovachki, Zongyi Li +8
We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimens…
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…