12 citations · 14 across the 4 of their papers we have counts for
4 papers
Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation
Jurij Schönfeld, Tom Beucler, Julien Savre +2
Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, th…
An intercomparison of generative machine learning methods for downscaling precipitation at fine spatial scales
Neelesh Rampal, Bryn Ward-Leikis, Yun Sing Koh +7
Machine learning (ML) offers a computationally efficient approach for generating large ensembles of high-resolution climate projections, but deterministic ML methods often smooth f…
Downscaling with AI reveals the large role of internal variability in fine-scale projections of climate extremes
Neelesh Rampal, Peter B. Gibson, Steven C. Sherwood +3
The computational cost of dynamical downscaling limits ensemble sizes in regional downscaling efforts. We present a newly developed generative-AI approach to greatly expand the sco…
On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates
Neelesh Rampal, Peter B. Gibson, Steven Sherwood +1
While deep-learning downscaling algorithms can generate fine-scale climate projections cost-effectively, it is still unclear how well they will extrapolate to unobserved climates.…