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20172026
most citedSpherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

53 citations · 170 across the 22 of their papers we have counts for

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Showing 2024 · physics.ao-phShow all

5 papers · 2 filters

physics.ao-ph2024★ 1 cited

Data-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale

Alberto Carpentieri, Jussi Leinonen, Jeff Adie +3

Accurate surface solar irradiance (SSI) forecasting is essential for optimizing renewable energy systems, particularly in the context of long-term energy planning on a global scale…

physics.ao-ph2024★ 1 cited

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…

physics.ao-ph2024★ 4 cited

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…

physics.ao-ph2024★ 11 cited

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…

physics.ao-ph2024★ 24 cited

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…