66 citations · 132 across the 6 of their papers we have counts for
6 papers
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…
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
Andrew Jesson, Peter Manshausen, Alyson Douglas +3
Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters a cloud and acts as cloud condensation nuclei (CCN). An increase in CCN results in a decre…
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…
Model calibration using ESEm v1.0.0 -- an open, scalable Earth System Emulator
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu +1
Large computer models are ubiquitous in the earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core-hours to run to completio…
Machine learning for weather and climate are worlds apart
Duncan Watson-Parris
Modern weather and climate models share a common heritage, and often even components, however they are used in different ways to answer fundamentally different questions. As such,…
Detecting anthropogenic cloud perturbations with deep learning
Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen +3
One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud drop…