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09/19/2023
Multi-fidelity climate model parameterization for better generalization and extrapolation
Machine-learning-based parameterizations (i.e. representation of sub-gri...
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06/14/2023
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
Modern climate projections lack adequate spatial and temporal resolution...
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01/10/2023
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceiv...
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12/01/2022
Pre-averaging fractional processes contaminated by noise, with an application to turbulence
In this article, we consider the problem of estimating fractional proces...
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10/26/2022
History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96
Physical parameterizations are used as representations of unresolved sub...
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12/21/2021
Deep Learning Based Cloud Cover Parameterization for ICON
A promising approach to improve cloud parameterizations within climate m...
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12/14/2021
Climate-Invariant Machine Learning
Data-driven algorithms, in particular neural networks, can emulate the e...
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11/30/2021
On the Generalization of Agricultural Drought Classification from Climate Data
Climate change is expected to increase the likelihood of drought events,...
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02/20/2020
Towards Physically-consistent, Data-driven Models of Convection
Data-driven algorithms, in particular neural networks, can emulate the e...
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06/16/2019
Recovering the parameters underlying the Lorenz-96 chaotic dynamics
Climate projections suffer from uncertain equilibrium climate sensitivit...
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06/15/2019
Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling
Artificial neural-networks have the potential to emulate cloud processes...
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06/12/2018