DOI: 10.1175/JCLI-D-17-0411.1
Scopus记录号: 2-s2.0-85047061594
论文题名: Predicting monsoon intraseasonal precipitation using a low-order nonlinear stochastic model
作者: Chen N. ; Majda A.J. ; Sabeerali C.T. ; Ajayamohan R.S.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2018
卷: 31, 期: 11 起始页码: 4403
结束页码: 4427
语种: 英语
英文关键词: Monsoons
; Spectral analysis/models/distribution
; Statistical forecasting
; Stochastic models
; Time series
Scopus关键词: Atmospheric thermodynamics
; Forecasting
; Nonlinear analysis
; Spectrum analysis
; Stochastic systems
; Time series
; Time series analysis
; Intermittent time series
; Intraseasonal oscillations
; Monsoons
; Nonlinear stochastic model
; Outgoing longwave radiation
; Spatio-temporal reconstruction
; Spectral analysis/models/distribution
; Statistical forecasting
; Stochastic models
英文摘要: The authors assess the predictability of large-scale monsoon intraseasonal oscillations (MISOs) as measured by precipitation. An advanced nonlinear data analysis technique, nonlinear Laplacian spectral analysis (NLSA), is applied to the daily precipitation data, resulting in two spatial modes associated with the MISO. The large-scale MISO patterns are predicted in two steps. First, a physics-constrained low-order nonlinear stochastic model is developed to predict the highly intermittent time series of these two MISO modes. The model involves two observed MISO variables and two hidden variables that characterize the strong intermittency and random oscillations in the MISO time series. It is shown that the precipitation MISO indices can be skillfully predicted from 20 to 50 days in advance. Second, an effective and practical spatiotemporal reconstruction algorithm is designed, which overcomes the fundamental difficulty in most data decomposition techniques with lagged embedding that requires extra information in the future beyond the predicted range of the time series. The predicted spatiotemporal patterns often have comparable skill to the MISO indices. One of the main advantages of the proposed model is that a short (3 year) training period is sufficient to describe the essential characteristics of the MISO and retain skillful predictions. In addition, both model statistics and prediction skill indicate that outgoing longwave radiation is an accurate proxy for precipitation in describing the MISO. Notably, the length of the lagged embedding window used in NLSA is crucial in capturing the main features and assessing the predictability of MISOs. © 2018 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/111519
Appears in Collections: 气候减缓与适应
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作者单位: Department of Mathematics and Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United States; Center for Prototype Climate Modeling, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
Recommended Citation:
Chen N.,Majda A.J.,Sabeerali C.T.,et al. Predicting monsoon intraseasonal precipitation using a low-order nonlinear stochastic model[J]. Journal of Climate,2018-01-01,31(11)