Physics-Informed Learning of Aerosol Microphysics

no code implementations • 24 Jul 2022 • Paula Harder, Duncan Watson-Parris, Philip Stier, Dominik Strassel, Nicolas R. Gauger, Janis Keuper

The original M7 model is used to generate data of input-output pairs to train a neural network on it.

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions

2 code implementations • 21 Apr 2022 • Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit

Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics.

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific

no code implementations • 28 Oct 2021 • Andrew Jesson, Peter Manshausen, Alyson Douglas, Duncan Watson-Parris, Yarin Gal, Philip Stier

Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters a cloud and acts as cloud condensation nuclei (CCN).

Emulating Aerosol Microphysics with Machine Learning

no code implementations • 22 Sep 2021 • Paula Harder, Duncan Watson-Parris, Dominik Strassel, Nicolas Gauger, Philip Stier, Janis Keuper

This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time.

BIG-bench Machine Learning

Detecting anthropogenic cloud perturbations with deep learning

no code implementations • 29 Nov 2019 • Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen, Anthony Caterini, Dino Sejdinovic, Philip Stier

One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance.