气候变化领域集成服务门户(CCPortal): Data-Driven Super-Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning

CCPortal

DOI	10.1029/2020MS002084
	Data-Driven Super-Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning
	Chattopadhyay A.; Subel A.; Hassanzadeh P.
发表日期	2020
ISSN	19422466
卷号	12 期号:11
英文摘要	To make weather and climate models computationally affordable, small-scale processes are usually represented in terms of the large-scale, explicitly resolved processes using physics-based/semi-empirical parameterization schemes. Another approach, computationally more demanding but often more accurate, is super-parameterization (SP). SP involves integrating the equations of small-scale processes on high-resolution grids embedded within the low-resolution grid of large-scale processes. Recently, studies have used machine learning (ML) to develop data-driven parameterization (DD-P) schemes. Here, we propose a new approach, data-driven SP (DD-SP), in which the equations of the small-scale processes are integrated data-drivenly (thus inexpensively) using ML methods such as recurrent neural networks. Employing multiscale Lorenz 96 systems as the testbed, we compare the cost and accuracy (in terms of both short-term prediction and long-term statistics) of parameterized low-resolution (PLR) SP, DD-P, and DD-SP models. We show that with the same computational cost, DD-SP substantially outperforms PLR and is more accurate than DD-P, particularly when scale separation is lacking. DD-SP is much cheaper than SP, yet its accuracy is the same in reproducing long-term statistics (climate prediction) and often comparable in short-term forecasting (weather prediction). We also investigate generalization: when models trained on data from one system are applied to a more chaotic system, we find that models often do not generalize, particularly when short-term prediction accuracies are examined. However, we show that transfer learning, which involves re-training the data-driven model with a small amount of data from the new system, significantly improves generalization. Potential applications of DD-SP and transfer learning in climate/weather modeling are discussed. ©2020. The Authors.
英文关键词	climate modeling; deep learning; parameterization; subgrid modeling; super-parameterization; transfer learning
语种	英语
scopus关键词	Chaotic systems; Climate models; Learning systems; Parameterization; Recurrent neural networks; Transfer learning; Weather forecasting; Computational costs; High-resolution grids; Long term statistics; Parameterization schemes; Short term prediction; Short-term forecasting; Small-scale process; Weather and climate models; Data communication systems; climate modeling; climate prediction; computer system; data assimilation; equation; machine learning; parameterization; prediction
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156586
作者单位	Department of Mechanical Engineering, Rice University, Houston, TX, United States; Department of Earth, Environmental and Planetary Sciences, Rice University, Houston, TX, United States
推荐引用方式 GB/T 7714	Chattopadhyay A.,Subel A.,Hassanzadeh P.. Data-Driven Super-Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning[J],2020,12(11).
APA	Chattopadhyay A.,Subel A.,&Hassanzadeh P..(2020).Data-Driven Super-Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning.Journal of Advances in Modeling Earth Systems,12(11).
MLA	Chattopadhyay A.,et al."Data-Driven Super-Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning".Journal of Advances in Modeling Earth Systems 12.11(2020).