气候变化领域集成服务门户(CCPortal): Assessment of High-Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application

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DOI	10.1029/2020JC016277
	Assessment of High-Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application
	Fritzner S.; Graversen R.; Christensen K.H.
发表日期	2020
ISSN	21699275
卷号	125 期号:11
英文摘要	In this study, the potential for sea ice concentration prediction using machine-learning methods is investigated. Three different sea ice prediction models are compared: one high-resolution dynamical assimilative model and two statistical machine-learning models. The properties of all three models are explored, and the quality of their forecasts is compared. The dynamical model is a state-of-the-art coupled ocean and sea ice ensemble-prediction system with assimilation. The observations assimilated are high-resolution sea ice concentration from synthetic aperture radar (SAR) and sea surface temperature from infrared instruments. The machine-learning prediction models are a fully convolutional network and a k-nearest neighbors method. These methods use several variables as input for the prediction: sea ice concentration, sea surface temperature, and 2-m air temperature. Earlier studies have applied machine-learning approaches primarily for seasonal ice forecast. Here we focus on short-term predictions with a length of 1–4 weeks, which are of high interest for marine operations. The goal is to predict the future state of the sea ice using the same categories as traditional ice charts. The machine-learning forecasts were compared to persistence, which is the assumption that the sea ice does not change over the forecasting period. The machine-learning forecasts were found to improve upon persistence in periods of substantial change. In addition, compared to the dynamical model, the k-nearest neighbor algorithm was found to improve upon the 7-day forecast during a period of small sea ice variations. The fully convolutional network provided similar quality as the dynamical forecast. The study shows that there is a potential for sea ice predictions using machine-learning methods. © 2020. American Geophysical Union. All Rights Reserved.
英文关键词	Data Assimilation; Dynamical model; Forecast; Machine learning; Sea ice
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/186595
作者单位	Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway; Norwegian Meteorological Institute, Tromsø, Oslo, Norway; Department of Geosciences, University of Oslo, Oslo, Norway
推荐引用方式 GB/T 7714	Fritzner S.,Graversen R.,Christensen K.H.. Assessment of High-Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application[J],2020,125(11).
APA	Fritzner S.,Graversen R.,&Christensen K.H..(2020).Assessment of High-Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application.Journal of Geophysical Research: Oceans,125(11).
MLA	Fritzner S.,et al."Assessment of High-Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application".Journal of Geophysical Research: Oceans 125.11(2020).