Search Results for author: Tom Beucler

Found 17 papers, 10 papers with code

Identifying Three-Dimensional Radiative Patterns Associated with Early Tropical Cyclone Intensification

1 code implementation17 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.

Lessons Learned: Reproducibility, Replicability, and When to Stop

no code implementations8 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.

Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications

no code implementations22 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.

Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models

1 code implementation28 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.

Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery

1 code implementation11 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).

Causal Discovery Dimensionality Reduction +3

Deep Learning Based Cloud Cover Parameterization for ICON

1 code implementation21 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.

Feature Importance

Climate-Invariant Machine Learning

1 code implementation14 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.

BIG-bench Machine Learning

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

no code implementations1 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

Generative Modeling for Atmospheric Convection

no code implementations3 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

Quantifying Convective Aggregation using the Tropical Moist Margin's Length

no code implementations26 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

Towards Physically-consistent, Data-driven Models of Convection

4 code implementations20 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.

Machine Learning for Clouds and Climate

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.

BIG-bench Machine Learning Cloud Detection +1

Convective dynamics and the response of precipitation extremes to warming in radiative-convective equilibrium

1 code implementation4 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

Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems

4 code implementations3 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

Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling

no code implementations15 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.

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