5 papers · 1 filter
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…
CORDEX-ML-Bench: A Benchmark for Data-Driven Regional Climate Downscaling -Experiment Design and Overview
Neelesh Rampal, José González-Abad, Henry Addison +34
Machine learning (ML) has emerged as a cost-effective approach to complement dynamical downscaling for producing high-resolution regional climate projections. However, the absence…
Generative AI-Downscaling of Large Ensembles Project Unprecedented Future Droughts
Hamish Lewis, Neelesh Rampal, Peter B. Gibson +4
Understanding how droughts may change in the future is essential for anticipating and mitigating their adverse impacts. However, robust climate projections require large amounts of…
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.…