DOI: 10.1007/s00382-015-2607-0
Scopus记录号: 2-s2.0-84957441693
论文题名: Prediction of northern summer low-frequency circulation using a high-order vector auto-regressive model
作者: Wang L. ; Ting M. ; Chapman D. ; Lee D.E. ; Henderson N. ; Yuan X.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2016
卷: 46, 期: 2017-03-04 起始页码: 693
结束页码: 709
语种: 英语
英文关键词: Circumglobal wave train
; Intraseasonal predictability
; Low frequency variability
; Madden–Julian oscillation
英文摘要: A data-driven, high-order vector auto-regressive (VAR) model is evaluated for predicting the Northern Hemisphere summer time (May through September) low frequency (>10 days or so) variability. The VAR model is suitable for linear stationary time series, similar to the commonly used linear inverse model (LIM), with additional temporal information incorporated to improve forecast skill. The intraseasonal forecast skill of the 250/750 hPa streamfunction is investigated using observational data since 1979, which shows significant improvements in high-order VAR models than the first-order model LIM. Furthermore, the tropical diabatic heating is found to significantly improve the forecast skill of the atmospheric low frequency circulation when included in the VAR model. The forecast skill of 250 hPa streamfunction at Arabian Peninsula is particularly enhanced for up to 5 weeks lead-time through circumglobal wave propagation associated with the persistent tropical eastern Pacific and equatorial Atlantic heating anomalies and the intraseasonal evolution of the tropical Indian Ocean and western Pacific heating anomalies. © 2015, The Author(s). 资助项目: NASA, National Aeronautics and Space Administration
; ONR, National Aeronautics and Space Administration
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53819
Appears in Collections: 过去全球变化的重建
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作者单位: Lamont-Doherty Earth Observatory, Columbia University, 61 Rt. 9W, Palisades, NY, United States
Recommended Citation:
Wang L.,Ting M.,Chapman D.,et al. Prediction of northern summer low-frequency circulation using a high-order vector auto-regressive model[J]. Climate Dynamics,2016-01-01,46(2017-03-04)