455 citations · 511 across the 14 of their papers we have counts for
18 papers
A library for differentiable signal processing and machine learning on the sphere
Thorsten Kurth, Max Rietmann, Mauro Bisson +6
The two-dimensional sphere embedded in three-dimensional Euclidean space S2, plays a central role in a variety of scientific and engineering domains, including geophysics, planetar…
4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
Deifilia Kieckhefen, Juan Pedro Gutiérrez Hermosillo Muriedas, Lars Helge Heyen +12
We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25 global resolution able to accurately quantify both aleatoric and epistemic un…
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…
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…
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…
Data-driven solar forecasting enables near-optimal economic decisions
Zhixiang Dai, Minghao Yin, Xuanhong Chen +27
Solar energy adoption is critical to achieving net-zero emissions. However, it remains difficult for many industrial and commercial actors to decide on whether they should adopt di…
Surya: Foundation Model for Heliophysics
Sujit Roy, Johannes Schmude, Rohit Lal +30
Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observator…
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh +7
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed…
Attention on the Sphere
Boris Bonev, Max Rietmann, Andrea Paris +2
We introduce a generalized attention mechanism for spherical domains, enabling Transformer architectures to natively process data defined on the two-dimensional sphere - a critical…
Modulated Adaptive Fourier Neural Operators for Temporal Interpolation of Weather Forecasts
Jussi Leinonen, Boris Bonev, Thorsten Kurth +1
Weather and climate data are often available at limited temporal resolution, either due to storage limitations, or in the case of weather forecast models based on deep learning, th…
Toward Capturing Genetic Epistasis From Multivariate Genome-Wide Association Studies Using Mixed-Precision Kernel Ridge Regression
Hatem Ltaief, Rabab Alomairy, Qinglei Cao +7
We exploit the widening margin in tensor-core performance between [FP64/FP32/FP16/INT8,FP64/FP32/FP16/FP8/INT8] on NVIDIA [Ampere,Hopper] GPUs to boost the performance of output ac…
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…
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators
Ankur Mahesh, William Collins, Boris Bonev +12
In Part I, we created an ensemble based on Spherical Fourier Neural Operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multipl…
Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model
Chenggong Wang, Michael S. Pritchard, Noah Brenowitz +5
Seasonal climate forecasts are socioeconomically important for managing the impacts of extreme weather events and for planning in sectors like agriculture and energy. Climate predi…
Neural Operators with Localized Integral and Differential Kernels
Miguel Liu-Schiaffini, Julius Berner, Boris Bonev +3
Neural operators learn mappings between function spaces, which is practical for learning solution operators of PDEs and other scientific modeling applications. Among them, the Four…
A Practical Probabilistic Benchmark for AI Weather Models
Noah D. Brenowitz, Yair Cohen, Jaideep Pathak +6
Since the weather is chaotic, forecasts aim to predict the distribution of future states rather than make a single prediction. Recently, multiple data driven weather models have em…
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
Matthias Karlbauer, Nathaniel Cresswell-Clay, Dale R. Durran +5
We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3-h time resolution for up to one-year lead times on a 110-km global m…
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
Jaideep Pathak, Shashank Subramanian, Peter Harrington +10
FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.2…