Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales

1 code implementation • 19 Jun 2024 • Peter Manshausen, Yair Cohen, Peter Harrington, Jaideep Pathak, Mike Pritchard, Piyush Garg, Morteza Mardani, Karthik Kashinath, Simon Byrne, Noah Brenowitz

Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization.

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).

NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations

1 code implementation • 13 Nov 2020 • Paula Harder, William Jones, Redouane Lguensat, Shahine Bouabid, James Fulton, Dánell Quesada-Chacón, Aris Marcolongo, Sofija Stefanović, Yuhan Rao, Peter Manshausen, Duncan Watson-Parris

The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night.

BIG-bench Machine Learning SSIM