气候变化领域集成服务门户(CCPortal): Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

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DOI	10.5194/acp-20-2303-2020
	Technical note: Deep learning for creating surrogate models of precipitation in Earth system models
	Weber T.; Corotan A.; Hutchinson B.; Kravitz B.; Link R.
发表日期	2020
ISSN	1680-7316
起始页码	2303
结束页码	2317
卷号	20 期号:4
英文摘要	We investigate techniques for using deep neural networks to produce surrogate models for short-term climate forecasts. A convolutional neural network is trained on 97 years of monthly precipitation output from the 1pctCO2 run (the CO2 concentration increases by 1 % per year) simulated by the second-generation Canadian Earth System Model (CanESM2). The neural network clearly outperforms a persistence forecast and does not show substantially degraded performance even when the forecast length is extended to 120 months. The model is prone to underpredicting precipitation in areas characterized by intense precipitation events. Scheduled sampling (forcing the model to gradually use its own past predictions rather than ground truth) is essential for avoiding amplification of early forecasting errors. However, the use of scheduled sampling also necessitates preforecasting (generating forecasts prior to the first forecast date) to obtain adequate performance for the first few prediction time steps. We document the training procedures and hyperparameter optimization process for researchers who wish to extend the use of neural networks in developing surrogate models. © 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
语种	英语
scopus关键词	amplification; artificial neural network; climate prediction; machine learning; numerical model; optimization; precipitation assessment; sampling
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141529
作者单位	Computer Science Department, Western Washington University, Bellingham, WA, United States; Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, United States; Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN, United States; Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, United States; Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, United States
推荐引用方式 GB/T 7714	Weber T.,Corotan A.,Hutchinson B.,et al. Technical note: Deep learning for creating surrogate models of precipitation in Earth system models[J],2020,20(4).
APA	Weber T.,Corotan A.,Hutchinson B.,Kravitz B.,&Link R..(2020).Technical note: Deep learning for creating surrogate models of precipitation in Earth system models.Atmospheric Chemistry and Physics,20(4).
MLA	Weber T.,et al."Technical note: Deep learning for creating surrogate models of precipitation in Earth system models".Atmospheric Chemistry and Physics 20.4(2020).