Articles | Volume 20, issue 4
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
26 Feb 2020
Technical note |
| 26 Feb 2020
| 26 Feb 2020
Technical note: Deep learning for creating surrogate models of precipitation in Earth system models
Theodore Weber
Computer Science Department, Western Washington University,
Bellingham, WA, USA
Austin Corotan
Computer Science Department, Western Washington University,
Bellingham, WA, USA
Brian Hutchinson
CORRESPONDING AUTHOR
Computer Science Department, Western Washington University,
Bellingham, WA, USA
Computing and Analytics Division, Pacific Northwest
National Laboratory, Seattle, WA, USA
Ben Kravitz
Department of Earth and Atmospheric Sciences, Indiana University,
Bloomington, IN, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USA
Robert Link
Joint Global Change Research
Institute, Pacific Northwest National Laboratory, College Park, MD, USA
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Cited
14 citations as recorded by crossref.
- Machine learning for weather and climate are worlds apart D. Watson-Parris 10.1098/rsta.2020.0098
- Emulating radiative transfer with artificial neural networks S. Sethuram et al. 10.1093/mnras/stad2524
- Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich 10.3390/cli12110189
- From rain to data: A review of the creation of monthly and daily station‐based gridded precipitation datasets R. Serrano‐Notivoli & E. Tejedor 10.1002/wat2.1555
- Transferring climate change physical knowledge F. Immorlano et al. 10.1073/pnas.2413503122
- Partial differential equations discovery with EPDE framework: Application for real and synthetic data M. Maslyaev et al. 10.1016/j.jocs.2021.101345
- Grouped convolution dual-attention network for time series forecasting of water temperature in offshore aquaculture net pen X. Sun et al. 10.1016/j.eswa.2025.127438
- A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
- Statistical mechanics in climate emulation: Challenges and perspectives I. Sudakow et al. 10.1017/eds.2022.15
- Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output B. Kravitz et al. 10.1371/journal.pclm.0000159
- Performance assessment for climate intervention (PACI): preliminary application to a stratospheric aerosol injection scenario L. Wheeler et al. 10.3389/fenvs.2023.1205515
- A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies A. Thelen et al. 10.1007/s00158-022-03425-4
- Machine learning for faster estimates of groundwater response to artificial aquifer recharge V. Fernandes et al. 10.1016/j.jhydrol.2024.131418
- Surrogate modelling of a detailed farm‐level model using deep learning L. Shang et al. 10.1111/1477-9552.12543
12 citations as recorded by crossref.
- Machine learning for weather and climate are worlds apart D. Watson-Parris 10.1098/rsta.2020.0098
- Emulating radiative transfer with artificial neural networks S. Sethuram et al. 10.1093/mnras/stad2524
- Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich 10.3390/cli12110189
- From rain to data: A review of the creation of monthly and daily station‐based gridded precipitation datasets R. Serrano‐Notivoli & E. Tejedor 10.1002/wat2.1555
- Transferring climate change physical knowledge F. Immorlano et al. 10.1073/pnas.2413503122
- Partial differential equations discovery with EPDE framework: Application for real and synthetic data M. Maslyaev et al. 10.1016/j.jocs.2021.101345
- Grouped convolution dual-attention network for time series forecasting of water temperature in offshore aquaculture net pen X. Sun et al. 10.1016/j.eswa.2025.127438
- A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
- Statistical mechanics in climate emulation: Challenges and perspectives I. Sudakow et al. 10.1017/eds.2022.15
- Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output B. Kravitz et al. 10.1371/journal.pclm.0000159
- Performance assessment for climate intervention (PACI): preliminary application to a stratospheric aerosol injection scenario L. Wheeler et al. 10.3389/fenvs.2023.1205515
- A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies A. Thelen et al. 10.1007/s00158-022-03425-4
Latest update: 20 May 2025
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.
Climate model emulators can save computer time but are less accurate than full climate models....
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