Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation

• URL: http://arxiv.org/abs/2210.04001v1
• Date: Sat, 8 Oct 2022 11:51:12 GMT
• Title: Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation
• Authors: Raghul Parthipan and Damon J. Wischik
• Abstract summary: We use a transfer learning approach to leverage all the high-resolution data. We show it stabilises training, gives improved generalisation performance and results in better forecasting skill.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 coarse-grained high-resolution data. But only keeping this coarse-grained data means the rest of the high-resolution data is thrown out. We use a transfer learning approach, which can be applied to a range of machine learning models, to leverage all the high-resolution data. We use three chaotic systems to show it stabilises training, gives improved generalisation performance and results in better forecasting skill. Our anonymised code is at https://www.dropbox.com/sh/0o1pks1i90mix3q/AAAMGfyD7EyOkdnA_Hp5ZpiWa?dl=0

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