Climate Change Data Portal
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 | 16807316 |
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. |
关键词 | amplificationartificial neural networkclimate predictionmachine learningnumerical modeloptimizationprecipitation assessmentsampling |
语种 | 英语 |
来源机构 | Atmospheric Chemistry and Physics |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/132219 |
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]. Atmospheric Chemistry and Physics,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.,20(4). |
MLA | Weber T.,et al."Technical note: Deep learning for creating surrogate models of precipitation in Earth system models".20.4(2020). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。