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Low‐order stochastic model and “past‐noise forecasting” of the Madden‐Julian Oscillation
Author(s) -
Kondrashov D.,
Chekroun M. D.,
Robertson A. W.,
Ghil M.
Publication year - 2013
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/grl.50991
Subject(s) - madden–julian oscillation , predictability , noise (video) , white noise , oscillation (cell signaling) , computer science , mode (computer interface) , climatology , econometrics , meteorology , environmental science , mathematics , statistics , artificial intelligence , geology , physics , convection , biology , image (mathematics) , genetics , operating system
This paper presents a predictability study of the Madden‐Julian Oscillation (MJO) that relies on combining empirical model reduction (EMR) with the “past‐noise forecasting” (PNF) method. EMR is a data‐driven methodology for constructing stochastic low‐dimensional models that account for nonlinearity, seasonality and serial correlation in the estimated noise, while PNF constructs an ensemble of forecasts that accounts for interactions between (i) high‐frequency variability (noise), estimated here by EMR, and (ii) the low‐frequency mode of MJO, as captured by singular spectrum analysis (SSA). A key result is that—compared to an EMR ensemble driven by generic white noise—PNF is able to considerably improve prediction of MJO phase. When forecasts are initiated from weak MJO conditions, the useful skill is of up to 30 days. PNF also significantly improves MJO prediction skill for forecasts that start over the Indian Ocean.