Understanding the dynamical mechanism of year-to-year incremental prediction by nonlinear time series prediction theory-Reference-Cited by-同舟云学术

Understanding the dynamical mechanism of year-to-year incremental prediction by nonlinear time series prediction theory

Published:2017-06-29 Issue:1 Volume:11 Page:71-77
ISSN:1674-2834
Container-title:Atmospheric and Oceanic Science Letters
language:en
Short-container-title:Atmospheric and Oceanic Science Letters

Author:

Bi Shu-Ting¹²,Wang Peng-Fei¹³,Pan Xin-Nong²⁴,Li Chao-Fan¹

Affiliation:

1. Center for Monsoon System Research (CMSR), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

2. University of Chinese Academy of Sciences, Beijing, China

3. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

4. Key Laboratory for Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Public Science and Technology Research Funds Projects of Ocean

Publisher

Elsevier BV

Subject

Atmospheric Science,Oceanography

Link

https://tandfonline.com/doi/pdf/10.1080/16742834.2017.1345278

Reference18 articles.

1. Nonlinear prediction of chaotic time series

2. An approach to improving the dynamical extended-range (monthly) prediction

3. Predicting Winter Surface Air Temperature in Northeast China

4. Forecasting the summer rainfall in North China using the year-to-year increment approach

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