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20212024
most citedHard-Constrained Deep Learning for Climate Downscaling

7 citations · 22 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024★ 3 cited

Tree semantic segmentation from aerial image time series

Venkatesh Ramesh, Arthur Ouaknine, David Rolnick

Earth's forests play an important role in the fight against climate change, and are in turn negatively affected by it. Effective monitoring of different tree species is essential t…

cs.LG2023★ 5 cited

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning

Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh +6

Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) community has taken an increased interest…

cs.LG2023★ 6 cited

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Qidong Yang, Alex Hernandez-Garcia, Paula Harder +5

Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computationally expensive to resolve complex climate…

physics.ao-ph2022★ 7 cited

Hard-Constrained Deep Learning for Climate Downscaling

Paula Harder, Alex Hernandez-Garcia, Venkatesh Ramesh +5

The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses…

cs.LG2021★ 1 cited

ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models

Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole +2

Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in replacing subroutines that explicitly compute…