Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation
Year: 2022
Type: preprint
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 code is at https://github.com/raghul-parthipan/dont_waste_data
Source: arXiv (Cornell University)
Authors: Parthipan, Raghul, Wischik, Damon J.
Institutions: British Antarctic Survey, University of Cambridge
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W4304700923