8 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…
On Neural Scaling Laws for Weather Emulation through Continual Training
Shashank Subramanian, Alexander Kiefer, Arnur Nigmetov +3
Neural scaling laws, which in some domains can predict the performance of large neural networks as a function of model, data, and compute scale, are the cornerstone of building fou…
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
Wuyang Chen, Jialin Song, Pu Ren +3
Recent years have witnessed the promise of coupling machine learning methods and physical domain-specific insights for solving scientific problems based on partial differential equ…
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
Pu Ren, N. Benjamin Erichson, Junyi Guo +4
Super-resolution (SR) techniques aim to enhance data resolution, enabling the retrieval of finer details, and improving the overall quality and fidelity of the data representation.…