Search Results for author: Duncan Watson-Parris

Found 12 papers, 5 papers with code

FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation

1 code implementation14 Jul 2023 Shahine Bouabid, Dino Sejdinovic, Duncan Watson-Parris

The result is an emulator that (i) enjoys the flexibility of statistical machine learning models and can learn from observations, and (ii) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system.

Exploring Randomly Wired Neural Networks for Climate Model Emulation

1 code implementation6 Dec 2022 William Yik, Sam J. Silva, Andrew Geiss, Duncan Watson-Parris

We also find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers.

Identifying the Causes of Pyrocumulonimbus (PyroCb)

no code implementations16 Nov 2022 Emiliano Díaz Salas-Porras, Kenza Tazi, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, Nis Meinert

A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented.

Causal Discovery

Physics-Informed Learning of Aerosol Microphysics

no code implementations24 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.

AODisaggregation: toward global aerosol vertical profiles

1 code implementation6 May 2022 Shahine Bouabid, Duncan Watson-Parris, Sofija Stefanović, Athanasios Nenes, Dino Sejdinovic

In this work, we develop a framework for the vertical disaggregation of AOD into extinction profiles, i. e. the measure of light extinction throughout an atmospheric column, using readily available vertically resolved meteorological predictors such as temperature, pressure or relative humidity.


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

no code implementations28 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 implementations22 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 implementations29 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.

Cumulo: A Dataset for Learning Cloud Classes

1 code implementation5 Nov 2019 Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris

One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system.

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