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DOI	10.1029/2019JD031551
	Detection of Non-Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model
	Goodliff M.; Fletcher S.; Kliewer A.; Forsythe J.; Jones A.
发表日期	2020
ISSN	2169897X
卷号	125 期号:2
英文摘要	An important assumption made in most variational, ensemble, and hybrid-based data assimilation systems is that all minimized errors are Gaussian random variables. A theory developed at the Cooperative Institute for Research in the Atmosphere enables for the Gaussian assumption for the different types of errors to be relaxed to a lognormally distributed random variable. While this is a first step toward using more consistent distributions to model the errors involved in numerical weather/ocean prediction, we still need to be able to identify when we need to assign a lognormal distribution in a mixed Gaussian-lognormal approach. In this paper, we present some machine learning techniques and experiments with the Lorenz 63 model. Using these machine learning techniques, we show detection of non-Gaussian distributions can be done using two methods: a support vector machine and a neural network. This is done by training past data to classify (1) differences with the distribution statistics (means and modes) and (2) the skewness of the probability density function. © 2019. American Geophysical Union. All Rights Reserved.
英文关键词	atmospheric flows; Lorenz 63 model; machine learning; neural network; non-Gaussianity; support vector machine
语种	英语
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/186224
作者单位	Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, United States
推荐引用方式 GB/T 7714	Goodliff M.,Fletcher S.,Kliewer A.,et al. Detection of Non-Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model[J],2020,125(2).
APA	Goodliff M.,Fletcher S.,Kliewer A.,Forsythe J.,&Jones A..(2020).Detection of Non-Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model.Journal of Geophysical Research: Atmospheres,125(2).
MLA	Goodliff M.,et al."Detection of Non-Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model".Journal of Geophysical Research: Atmospheres 125.2(2020).