Generative Modeling for Atmospheric Convection

no code implementations • 3 Jul 2020 • Griffin Mooers, Jens Tuyls, Stephan Mandt, Michael Pritchard, Tom Beucler

While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of computational resources.

Clustering Dimensionality Reduction +1

Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs

no code implementations • 1 Dec 2021 • Harshini Mangipudi, Griffin Mooers, Mike Pritchard, Tom Beucler, Stephan Mandt

Understanding the details of small-scale convection and storm formation is crucial to accurately represent the larger-scale planetary dynamics.

Vocal Bursts Intensity Prediction

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

1 code implementation • NeurIPS 2023 • Sungduk Yu, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus Christopher Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles I Stern, Tom Beucler, Bryce Harrop, Benjamin R Hillman, Andrea Jenney, Savannah Ferretti, Nana Liu, Anima Anandkumar, Noah D Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C Bader, Pierre Baldi, Elizabeth Barnes, Christopher Bretherton, Peter Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David Randall, Sara Shamekh, Mark A Taylor, Nathan Urban, Janni Yuval, Guang Zhang, Michael Pritchard

The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.

Neural General Circulation Models for Weather and Climate

1 code implementation • 13 Nov 2023 • Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, Sam Hatfield, Peter Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P. Brenner, Stephan Hoyer

Here we present the first GCM that combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods.

Physical Simulations Weather Forecasting