1 code implementation • 17 Jan 2024 • Frederick Iat-Hin Tam, Tom Beucler, James H. Ruppert Jr
Cloud radiative feedback impacts early tropical cyclone (TC) intensification, but limitations in existing diagnostic frameworks make them unsuitable for studying asymmetric or transient radiative heating.
no code implementations • 8 Jan 2024 • Milton S. Gomez, Tom Beucler
While extensive guidance exists for ensuring the reproducibility of one's own study, there is little discussion regarding the reproduction and replication of external studies within one's own research.
no code implementations • 22 Nov 2023 • Tom Beucler, Erwan Koch, Sven Kotlarski, David Leutwyler, Adrien Michel, Jonathan Koh
We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future.
1 code implementation • 28 Sep 2023 • Jerry Lin, Sungduk Yu, Tom Beucler, Pierre Gentine, David Walling, Mike Pritchard
The implication is that hundreds of candidate ML models should be evaluated online to detect the effects of parameterization design choices.
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.
1 code implementation • 11 Apr 2023 • Saranya Ganesh S., Tom Beucler, Frederick Iat-Hin Tam, Milton S. Gomez, Jakob Runge, Andreas Gerhardus
We apply our framework to the statistical intensity prediction of Western Pacific Tropical Cyclones (TC), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging).
1 code implementation • 7 Dec 2022 • Francesco Zanetta, Daniele Nerini, Tom Beucler, Mark A. Liniger
Weather forecasting centers currently rely on statistical postprocessing methods to minimize forecast error.
1 code implementation • 21 Dec 2021 • Arthur Grundner, Tom Beucler, Pierre Gentine, Fernando Iglesias-Suarez, Marco A. Giorgetta, Veronika Eyring
A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations.
1 code implementation • 14 Dec 2021 • Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng, Jerry Lin, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O'Gorman, J. David Neelin, Nicholas J. Lutsko, Michael Pritchard
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates.
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.
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.
no code implementations • 26 Feb 2020 • Tom Beucler, David Leutwyler, Julia Windmiller
As the spatial organization of moisture is closely related to the organization of tropical convection, we hereby introduce a new organization index (BLW) measuring the ratio of the margin's length to the circumference of a well-defined equilibrium shape.
Atmospheric and Oceanic Physics
4 code implementations • 20 Feb 2020 • Tom Beucler, Michael Pritchard, Pierre Gentine, Stephan Rasp
Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations.
no code implementations • Open Access 2020 • Tom Beucler, I. Ebert‐Uphoff, S. Rasp, M. Pritchard, P. Gentine
Machine learning (ML) algorithms are powerful tools to build models of clouds and climate that are more faithful to the rapidly-increasing volumes of Earth system data than commonly-used semiempirical models.
1 code implementation • 4 Sep 2019 • Tristan H. Abbott, Timothy W. Cronin, Tom Beucler
Tropical precipitation extremes are expected to strengthen with warming, but quantitative estimates remain uncertain because of a poor understanding of changes in convective dynamics.
Atmospheric and Oceanic Physics
4 code implementations • 3 Sep 2019 • Tom Beucler, Michael Pritchard, Stephan Rasp, Jordan Ott, Pierre Baldi, Pierre Gentine
Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints.
Computational Physics Atmospheric and Oceanic Physics
no code implementations • 15 Jun 2019 • Tom Beucler, Stephan Rasp, Michael Pritchard, Pierre Gentine
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models.