Tom Beucler
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Enforcing analytic constraints in neural networks emulating physical systems
T Beucler, M Pritchard, S Rasp, J Ott, P Baldi, P Gentine
Physical Review Letters 126 (9), 098302, 2021
Achieving conservation of energy in neural network emulators for climate modeling
T Beucler, S Rasp, M Pritchard, P Gentine
2019 International Conference on Machine Learning (Workshop)., 2019
Interpreting and Stabilizing Machine-learning Parametrizations of Convection
N Brenowitz, T Beucler, M Pritchard, C Bretherton
Journal of the Atmospheric Sciences, 2020
Moisture‐radiative cooling instability
T Beucler, TW Cronin
Journal of Advances in Modeling Earth Systems 8 (4), 1620-1640, 2016
Towards physically-consistent, data-driven models of convection
T Beucler, M Pritchard, P Gentine, S Rasp
Igarss 2020-2020 ieee international geoscience and remote sensing symposium …, 2020
Assessing the potential of deep learning for emulating cloud superparameterization in climate models with real‐geography boundary conditions
G Mooers, M Pritchard, T Beucler, J Ott, G Yacalis, P Baldi, P Gentine
Journal of Advances in Modeling Earth Systems 13 (5), e2020MS002385, 2021
A budget for the size of convective self‐aggregation
T Beucler, TW Cronin
Quarterly Journal of the Royal Meteorological Society, 2018
Convective dynamics and the response of precipitation extremes to warming in radiative-convective equilibrium
T Abbott, T Cronin, T Beucler
Journal of the Atmospheric Sciences, 2019
A Linear Response Framework for Radiative‐Convective Instability
T Beucler, T Cronin, K Emanuel
Journal of Advances in Modeling Earth Systems, …, 2018
Machine learning for clouds and climate (invited chapter for the agu geophysical monograph series “clouds and climate”)
T Beucler, I Ebert-Uphoff, S Rasp, M Pritchard, P Gentine
Authorea Preprints, 2022
Climate-invariant machine learning
T Beucler, M Pritchard, J Yuval, A Gupta, L Peng, S Rasp, F Ahmed, ...
arXiv preprint arXiv:2112.08440, 2021
Comparing convective self‐aggregation in idealized models to observed moist static energy variability near the equator
T Beucler, TH Abbott, TW Cronin, MS Pritchard
Geophysical Research Letters 46 (17-18), 10589-10598, 2019
Generative modeling of atmospheric convection
G Mooers, J Tuyls, S Mandt, M Pritchard, TG Beucler
Proceedings of the 10th international conference on climate informatics, 98-105, 2020
Deep learning for the parametrization of subgrid processes in climate models
P Gentine, V Eyring, T Beucler
Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021
Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models
G Behrens, T Beucler, P Gentine, F Iglesias‐Suarez, M Pritchard, V Eyring
Journal of advances in modeling earth systems 14 (8), e2022MS003130, 2022
Quantifying convective aggregation using the tropical moist margin's length
T Beucler, D Leutwyler, JM Windmiller
Journal of Advances in Modeling Earth Systems 12 (10), e2020MS002092, 2020
Deep learning based cloud cover parameterization for ICON
A Grundner, T Beucler, P Gentine, F Iglesias‐Suarez, MA Giorgetta, ...
Journal of Advances in Modeling Earth Systems 14 (12), e2021MS002959, 2022
A correlated stochastic model for the large‐scale advection, condensation and diffusion of water vapour
T Beucler
Quarterly Journal of the Royal Meteorological Society 142 (697), 1721-1731, 2016
Data-Driven Equation Discovery of a Cloud Cover Parameterization
A Grundner, T Beucler, P Gentine, V Eyring
arXiv preprint arXiv:2304.08063, 2023
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
S Yu, WM Hannah, L Peng, MA Bhouri, R Gupta, J Lin, B Lütjens, JC Will, ...
arXiv preprint arXiv:2306.08754, 2023
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