Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020GL089444 |
Evaluation of neural network emulations for radiation parameterization in cloud resolving model | |
Soonyoung Roh; Hwan‐; Jin Song | |
2020-10-17 | |
发表期刊 | Geophysical Research Letters
![]() |
出版年 | 2020 |
英文摘要 | This study evaluated the forecast performance of neural network (NN)‐based radiation emulators with 300 to 56 neurons developed under the cloud‐resolving simulation. These emulators are 20–100 times faster than the original parameterization and express evolutionary features well for 6 hrs. The results suggest that the frequent use of an NN emulator can improve not only computational speed but also forecasting accuracy in comparison to the infrequent use of original radiation parameterization, which is commonly used for speedup but can induce numerical instability as a result of imbalance with other processes. The forecast error of the emulator results was much improved in comparison with that for infrequent radiation runs with similar computational cost. The 56‐neuron emulator results were even more accurate than the infrequent runs, which had a computational cost five times higher. The speed and accuracy advantages of radiation emulators can be utilized for weather forecasting. |
领域 | 气候变化 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/299211 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Soonyoung Roh,Hwan‐,Jin Song. Evaluation of neural network emulations for radiation parameterization in cloud resolving model[J]. Geophysical Research Letters,2020. |
APA | Soonyoung Roh,Hwan‐,&Jin Song.(2020).Evaluation of neural network emulations for radiation parameterization in cloud resolving model.Geophysical Research Letters. |
MLA | Soonyoung Roh,et al."Evaluation of neural network emulations for radiation parameterization in cloud resolving model".Geophysical Research Letters (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。
修改评论