8 citations · 39 across the 15 of their papers we have counts for
5 papers · 1 filter
On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling
Paula Harder, Christian Lessig, Matthew Chantry +2
Deep learning offers promising capabilities for the statistical downscaling of climate and weather forecasts, with generative approaches showing particular success in capturing fin…
Evaluating the transferability potential of deep learning models for climate downscaling
Ayush Prasad, Paula Harder, Qidong Yang +4
Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at region…
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-Informed Learning of Aerosol Microphysics
Paula Harder, Duncan Watson-Parris, Philip Stier +3
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of u…
Emulating Aerosol Microphysics with Machine Learning
Paula Harder, Duncan Watson-Parris, Dominik Strassel +3
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of u…