DOI: 10.1007/s00382-012-1302-7
Scopus记录号: 2-s2.0-84867102445
论文题名: Flow-dependent empirical singular vector with an ensemble Kalman filter data assimilation for El Nino prediction
作者: Ham Y.-G. ; Rienecker M.M.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2012
卷: 39, 期: 2017-07-08 起始页码: 1727
结束页码: 1738
语种: 英语
英文关键词: El Nino
; Ensemble Kalman filter
; ENSO
; Seasonal prediction
; Singular vector
英文摘要: In this study, a new approach for extracting flow-dependent empirical singular vectors (FESVs) for seasonal prediction using ensemble perturbations obtained from an ensemble Kalman filter (EnKF) assimilation is presented. Due to the short interval between analyses, EnKF perturbations primarily contain instabilities related to fast weather variability. To isolate slower, coupled instabilities that would be more suitable for seasonal prediction, an empirical linear operator for seasonal time-scales (i. e. several months) is formulated using a causality hypothesis; then, the most unstable mode from the linear operator is extracted for seasonal time-scales. It is shown that the flow-dependent operator represents nonlinear integration results better than a conventional empirical linear operator static in time. Through 20 years of retrospective seasonal predictions, it is shown that the skill of forecasting equatorial SST anomalies using the FESV is systematically improved over that using Conventional ESV (CESV). For example, the correlation skill of the NINO3 SST index using FESV is higher, by about 0.1, than that of CESV at 8-month leads. In addition, the forecast skill improvement is significant over the locations where the correlation skill of conventional methods is relatively low, indicating that the FESV is effective where the initial uncertainty is large. © 2012 Springer-Verlag. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/55142
Appears in Collections: 过去全球变化的重建
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作者单位: Global Modeling and Assimilation Office, NASA/GSFC Code 610.1, Greenbelt, MD, United States; Goddard Earth Sciences Technology and Research Studies and Investigations, Universities Space Research Association, Baltimore, MD, United States
Recommended Citation:
Ham Y.-G.,Rienecker M.M.. Flow-dependent empirical singular vector with an ensemble Kalman filter data assimilation for El Nino prediction[J]. Climate Dynamics,2012-01-01,39(2017-07-08)