气候变化领域集成服务门户(CCPortal): Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning

CCPortal

DOI	10.1029/2019MS001958
	Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning
	Chattopadhyay A.; Nabizadeh E.; Hassanzadeh P.
发表日期	2020
ISSN	19422466
卷号	12 期号:2
英文摘要	Numerical weather prediction models require ever-growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and an impact-based autolabeling strategy. Using data from a large-ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large-scale circulation patterns (Z500) labeled 0–4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69–45% (77–48%) or 62–41% (73–47%) 1–5 days ahead. Using both surface temperature and Z500, accuracies (recalls) with CapsNets increase to ∼80% (88%). In both cases, CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression, and their accuracy is least affected as the size of the training set is reduced. The results show the promises of multivariate data-driven frameworks for accurate and fast extreme weather predictions, which can potentially augment numerical weather prediction efforts in providing early warnings. ©2020. The Authors.
英文关键词	analog forecasting; data-driven modeling; deep learning; extreme weather events; weather prediction
语种	英语
scopus关键词	Atmospheric temperature; Convolutional neural networks; Extreme weather; Labeled data; Logistic regression; Pattern recognition; Surface properties; Weather forecasting; Data-driven model; Earth system model; Extreme weather events; Large-scale circulation patterns; Numerical weather prediction; Numerical weather prediction models; Surface temperatures; Weather prediction; Deep learning; accuracy assessment; artificial neural network; atmospheric circulation; early warning system; extreme event; geographical region; multivariate analysis; surface temperature; weather forecasting; North America
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/156759
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.,Nabizadeh E.,Hassanzadeh P.. Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning[J],2020,12(2).
APA	Chattopadhyay A.,Nabizadeh E.,&Hassanzadeh P..(2020).Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning.Journal of Advances in Modeling Earth Systems,12(2).
MLA	Chattopadhyay A.,et al."Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning".Journal of Advances in Modeling Earth Systems 12.2(2020).