7 citations · 45 across the 13 of their papers we have counts for
14 papers
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…
Aboveground carbon biomass estimate with Physics-informed deep network
Juan Nathaniel, Levente J. Klein, Campbell D. Watson +2
The global carbon cycle is a key process to understand how our climate is changing. However, monitoring the dynamics is difficult because a high-resolution robust measurement of ke…
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…
Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers
Björn Lütjens, Catherine H. Crawford, Campbell D Watson +2
Numerical simulations in climate, chemistry, or astrophysics are computationally too expensive for uncertainty quantification or parameter-exploration at high-resolution. Reduced-o…
Wildfire risk forecast: An optimizable fire danger index
Eduardo Rodrigues, Bianca Zadrozny, Campbell Watson
Wildfire events have caused severe losses in many places around the world and are expected to increase with climate change. Throughout the years many technologies have been develop…
Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Ken C. L. Wong, Hongzhi Wang, Etienne E. Vos +3
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows…