DOI: 10.1175/JCLI-D-15-0016.1
Scopus记录号: 2-s2.0-84960915665
论文题名: Long-lead seasonal prediction of China summer rainfall using an EOF-PLS regression-based methodology
作者: Xing W. ; Wang B. ; Yim S.-Y.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2016
卷: 29, 期: 5 起始页码: 1783
结束页码: 1796
语种: 英语
Scopus关键词: Atmospheric temperature
; Climatology
; Financial data processing
; Least squares approximations
; Microcomputers
; Oceanography
; Orthogonal functions
; Principal component analysis
; Rain
; Regression analysis
; Snow
; Climate prediction
; Empirical Orthogonal Function
; Partial least-squares regression
; Pattern correlation
; Principal component
; Seasonal forecasting
; Statistical forecasting
; Surface temperatures
; Forecasting
; climate prediction
; rainfall
; regression analysis
; seasonal variation
; statistical analysis
; surface temperature
; China
英文摘要: Considerable year-to-year variability of summer rainfall exposes China to threats of frequent droughts and floods. Objective prediction of the summer rainfall anomaly pattern turns out to be very challenging. As shown in the present study, the contemporary state-of-the-art dynamical models' 1-month-lead prediction of China summer rainfall (CSR) anomalies has insignificant skills. Thus, there is an urgent need to explore other ways to improve CSR prediction. The present study proposes a combined empirical orthogonal function (EOF)-partial least squares (PLS) regression method to offer a potential long-lead objective prediction of spatial distribution of CSR anomalies. The essence of the methodology is to use PLS regression to predict the principal component (PC) of the first five leading EOF modes of CSR. The preceding December-January mean surface temperature field [ST; i.e., SST over ocean and 2-m air temperature (T2m) over land] is selected as the predictor field for all five PCs because SST and snow cover, which is reflected by 2-m air temperature, are the most important factors that affect CSR and because the correlation between each mode and ST during winter is higher than in spring. The 4-month-lead forecast models are established by using the data from 1979 to 2004. A 9-yr independent forward-rolling prediction is made for the latest 9 yr (2005-13) as a strict forecast validation. The pattern correlation coefficient skill (0.32) between the observed and the 4-month-lead predicted patterns during the independent forecast period of 2005-13 is significantly higher than the dynamic models' 1-month-lead hindcast skill (0.04), which indicates that the EOF-PLS regression is a useful tool for improving the current seasonal rainfall prediction. Issues related to the EOF-PLS method are also discussed. © 2016 American Meteorological Society. 资助项目: CSC, China Sponsorship Council
; NRF, National Research Foundation
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50283
Appears in Collections: 气候变化事实与影响
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作者单位: College of Oceanic and Atmospheric Sciences, Physical Oceanography Laboratory, Ocean University of China, Qingdao Collaborative Innovation Center of Marine Science and Technology, Qingdao, China; Department of Atmospheric Sciences, International Pacific Research Center, University of Hawai'i at Manoa, Honolulu, HI, United States; Earth System Modeling Center, Nanjing University of Information Science and Technology, Nanjing, China; Korea Meteorological Administration, Seoul, South Korea
Recommended Citation:
Xing W.,Wang B.,Yim S.-Y.. Long-lead seasonal prediction of China summer rainfall using an EOF-PLS regression-based methodology[J]. Journal of Climate,2016-01-01,29(5)