气候变化领域集成服务门户(CCPortal): Machine Learning-Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses

CCPortal

DOI	10.1029/2019MS001876
	Machine Learning-Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses
	Irrgang C.; Saynisch-Wagner J.; Thomas M.
发表日期	2020
ISSN	19422466
卷号	12 期号:5
英文摘要	The characterization of uncertainties in geophysical quantities is an important task with widespread applications for time series prediction, numerical modeling, and data assimilation. In this context, machine learning is a powerful tool for estimating complex patterns and their evolution through time. Here, we utilize a supervised machine learning approach to dynamically predict the spatiotemporal uncertainty of near-surface wind velocities over the ocean. A recurrent neural network (RNN) is trained with reanalyzed 10 m wind velocities and corresponding precalculated uncertainty estimates during the 2012–2016 time period. Afterward, the neural network's performance is examined by analyzing its prediction for the subsequent year 2017. Our experiments show that a recurrent neural network can capture the globally prevalent wind regimes without prior knowledge about underlying physics and learn to derive wind velocity uncertainty estimates that are only based on wind velocity trajectories. At single training locations, the RNN-based wind uncertainties closely match with the true reference values, and the corresponding intra-annual variations are reproduced with high accuracy. Moreover, the neural network can predict global lateral distribution of uncertainties with small mismatch values after being trained only at a few isolated locations in different dynamic regimes. The presented approach can be combined with numerical models for a cost-efficient generation of ensemble simulations or with ensemble-based data assimilation to sample and predict dynamically consistent error covariance information of atmospheric boundary forcings. ©2020. The Authors.
英文关键词	artificial neural network; atmospheric reanalysis; data assimilation; ensemble simulation; machine learning; wind velocity
语种	英语
scopus关键词	Forecasting; Learning systems; Numerical models; Reactor cores; Recurrent neural networks; Supervised learning; Velocity; Wind; Ensemble based data assimilation; Ensemble simulation; Error covariance information; Lateral distributions; Recurrent neural network (RNN); Supervised machine learning; Time series prediction; Uncertainty estimates; Uncertainty analysis; artificial neural network; atmospheric forcing; data assimilation; global change; machine learning; prediction; spatiotemporal analysis; uncertainty analysis; wind velocity
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/156714
作者单位	Section 1.3: Earth System Modelling, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany; Institute of Meteorology, Freie Universität Berlin, Berlin, Germany
推荐引用方式 GB/T 7714	Irrgang C.,Saynisch-Wagner J.,Thomas M.. Machine Learning-Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses[J],2020,12(5).
APA	Irrgang C.,Saynisch-Wagner J.,&Thomas M..(2020).Machine Learning-Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses.Journal of Advances in Modeling Earth Systems,12(5).
MLA	Irrgang C.,et al."Machine Learning-Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses".Journal of Advances in Modeling Earth Systems 12.5(2020).