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Found 6 papers, 5 papers with code
Towards Physically Consistent Deep Learning For Climate Model Parameterizations
Climate models play a critical role in understanding and projecting climate change.
Transferring climate change physical knowledge
Precise and reliable climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties.
Bootstrap aggregation and confidence measures to improve time series causal discovery
It can be combined with a range of time series causal discovery methods and provides a measure of confidence for the links of the time series graphs.
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
As an extension of the ClimSim dataset (Yu et al., 2024), we present ClimSim-Online, which also includes an end-to-end workflow for developing hybrid ML-physics simulators.
Deep Learning Based Cloud Cover Parameterization for ICON
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.
On the Generalization of Agricultural Drought Classification from Climate Data
Climate change is expected to increase the likelihood of drought events, with severe implications for food security.
