气候变化领域集成服务门户(CCPortal): Reconstruction of the Basin-Wide Sea-Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks

CCPortal

DOI	10.1029/2020JC016402
	Reconstruction of the Basin-Wide Sea-Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks
	Zhang Z.; Stanev E.V.; Grayek S.
发表日期	2020
ISSN	21699275
卷号	125 期号:12
英文摘要	We present an application of generative adversarial networks (GANs) to reconstruct the sea level of the North Sea using a limited amount of data from tidal gauges (TGs). The application of this technique, which learns how to generate datasets with the same statistics as the training set, is explained in detail to ensure that interested scientists can implement it in similar or different oceanographic cases. Training is performed for all of 2016, and the model is validated on data from 3 months in 2017 and compared against reconstructions using the Kalman filter approach. Tests with datasets generated by an operational model (“true data”) demonstrated that using data from only 19 locations where TGs permanently operate is sufficient to generate an adequate reconstruction of the sea surface height (SSH) in the entire North Sea. The machine learning approach appeared successful when learning from different sources, which enabled us to feed the network with real observations from TGs and produce high-quality reconstructions of the basin-wide SSH. Individual reconstruction experiments using different combinations of training and target data during the training and validation process demonstrated similarities with data assimilation when errors in the data and model were not handled appropriately. The proposed method demonstrated good skill when analyzing both the full signal and the low-frequency variability only. It was demonstrated that GANs are also skillful at learning and replicating processes with multiple time scales. The different skills in different areas of the North Sea are explained by the different signal-to-noise ratios associated with differences in regional dynamics. © 2020. The Authors.
英文关键词	deep learning; numerical models; sea level; tidal gauges; tides
语种	英语
来源期刊	Journal of Geophysical Research: Oceans
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/186558
作者单位	Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
推荐引用方式 GB/T 7714	Zhang Z.,Stanev E.V.,Grayek S.. Reconstruction of the Basin-Wide Sea-Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks[J],2020,125(12).
APA	Zhang Z.,Stanev E.V.,&Grayek S..(2020).Reconstruction of the Basin-Wide Sea-Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks.Journal of Geophysical Research: Oceans,125(12).
MLA	Zhang Z.,et al."Reconstruction of the Basin-Wide Sea-Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks".Journal of Geophysical Research: Oceans 125.12(2020).