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cs.LG2022★ 2 cited
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation
Raghul Parthipan, Damon J. Wischik
How can we learn from all available data when training machine-learnt climate models, without incurring any extra cost at simulation time? Typically, the training data comprises co…
cs.LG2022
Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking +1
The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through paramet…