Articles | Volume 20, issue 4
Technical note
26 Feb 2020
Technical note |  | 26 Feb 2020

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

Theodore Weber, Austin Corotan, Brian Hutchinson, Ben Kravitz, and Robert Link

Model code and software

A Deep Neural Network approach for estimating precipitation fields in Earth System Models T. Weber, A. Corotan, B. Hutchinson, B. Kravitz, and R. P. Link

Short summary
Climate model emulators can save computer time but are less accurate than full climate models. We use neural networks to build emulators of precipitation, trained on existing climate model runs. By doing so, we can capture nonlinearities and how the past state of a model (to some degree) shapes the future state. Our emulator outperforms a persistence forecast of precipitation.
Final-revised paper