气候变化领域集成服务门户(CCPortal): Implementation of an Artificial Neural Network for Storm Surge Forecasting

CCPortal

DOI	10.1029/2020JD033266
	Implementation of an Artificial Neural Network for Storm Surge Forecasting
	Ramos-Valle A.N.; Curchitser E.N.; Bruyère C.L.; McOwen S.
发表日期	2021
ISSN	2169-897X
卷号	126 期号:13
英文摘要	Accurate and timely storm surge forecasts are essential during tropical cyclone events in order to assess the magnitude and location of the impacts. Coupled ocean-atmosphere dynamical models provide accurate measures of storm surge but remain too computationally expensive to run for real-time forecasting purposes. Therefore, it is common to utilize a parametric vortex model, implemented within a hydrodynamic model, which decreases computational time at the expense of forecast accuracy. Recently, data-driven neural networks are being implemented as an alternative due to their combined efficiency and high accuracy. This work seeks to examine how an artificial neural network (ANN) can be used to make accurate storm surge predictions, and explores the added value of using a recurrent neural network (RNN). In particular, it is concerned with determining the parameters needed to successfully implement a neural network model for the Mid-Atlantic Bight region. The neural network models were trained with modeled data resulting from coupling of the Hybrid Weather Research and Forecasting cyclone model (HWCM) and the Advanced Circulation Model. An ensemble of synthetic, but physically plausible, cyclones were simulated using the HWCM and used as input for the hydrodynamic model. Tests of the ANN were conducted to investigate the optimal lead-time configuration of the input data and the neural network architecture needed to minimize storm surge forecast errors. Results highlight the accuracy of the ANN in forecasting moderate storm surge levels, while indicating a deficiency in capturing the magnitude of the peak values, which is improved in the implementation of the RNN. © 2021. American Geophysical Union. All Rights Reserved.
英文关键词	artificial neural network; machine learning; storm surge
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/237159
作者单位	Department of Environmental Sciences, Rutgers University, New Brunswick, NJ, United States; National Center for Atmospheric Research, Boulder, CO, United States; Environmental Sciences and Management, North-West University, Potchefstroom, South Africa; Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, United States
推荐引用方式 GB/T 7714	Ramos-Valle A.N.,Curchitser E.N.,Bruyère C.L.,et al. Implementation of an Artificial Neural Network for Storm Surge Forecasting[J],2021,126(13).
APA	Ramos-Valle A.N.,Curchitser E.N.,Bruyère C.L.,&McOwen S..(2021).Implementation of an Artificial Neural Network for Storm Surge Forecasting.Journal of Geophysical Research: Atmospheres,126(13).
MLA	Ramos-Valle A.N.,et al."Implementation of an Artificial Neural Network for Storm Surge Forecasting".Journal of Geophysical Research: Atmospheres 126.13(2021).