84 citations · 100 across the 8 of their papers we have counts for
8 papers
Towards accurate extreme event likelihoods from diffusion model climate emulators
Peter Manshausen, Noah Brenowitz, Julius Berner +2
ML climate model emulators are useful for scenario planning and adaptation, allowing for cost-efficient experimentation. Recently, the diffusion model Climate in a Bottle (cBottle)…
AIMIP Phase 1: systematic evaluations of AI weather and climate models
Brian Henn, Christopher S. Bretherton, Nikolay Koldunov +18
We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a…
Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere
Noah D. Brenowitz, Tao Ge, Akshay Subramaniam +7
Climate modeling is reaching unprecedented resolution, producing petabytes of data. AI climate model emulators offer a path to computationally cheap analysis, enabling new scientif…
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
Peter Manshausen, Yair Cohen, Peter Harrington +7
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation…
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
Andrew Jesson, Alyson Douglas, Peter Manshausen +5
Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses…
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…
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
Paula Harder, William Jones, Redouane Lguensat +8
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. The gap can be…
Generation and imaging of magnetoacoustic waves over millimetre distances
Blai Casals, Nahuel Statuto, Michael Foerster +7
Using hybrid piezoelectric/magnetic systems we have generated large amplitude magnetization waves mediated by magneto-elasticity with up to 25 degrees variation in the magnetizatio…