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R. Chattopadhyay
,
A. K. Sahai
, and
B. N. Goswami

Abstract

The nonlinear convectively coupled character of the summer monsoon intraseasonal oscillation (ISO) that manifests in its event-to-event variations is a major hurdle for skillful extended-range prediction of the active/break episodes. The convectively coupled character of the monsoon ISO implies that a particular nonlinear phase of the precipitation ISO is linked to a unique pattern of the large-scale variables. A methodology has been presented to capture different nonlinear phases of the precipitation ISO using a combination of a sufficiently large number of dynamical variables. This is achieved through a nonlinear pattern recognition technique known as self-organizing map (SOM) involving six daily large-scale circulation indices. It is demonstrated that the nonlinearly classified states of the large-scale circulation isolated at the SOM nodes without involving any information on rainfall are strongly linked to different phases of evolution of the rainfall ISO, including the active and break phases. While a lower SOM classification involving 9 different states identify the composite phases of the rainfall ISO, a higher SOM classification involving 81 states can identify different shades of composite phase of the rainfall ISO. The concept of isolating the nonlinear states, as well as the technique of doing so, is robust as almost identical phases of precipitation ISO are identified by the large-scale circulation indices derived from two different reanalysis datasets, namely, the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR reanalysis.

The ability of the SOM technique to isolate spatial structure and evolutionary history of nonlinear convectively coupled states of the summer monsoon ISO opens up a new possibility of extended-range prediction of summer monsoon ISO. This knowledge is used to develop an analog technique for predicting different phases of monsoon ISO. Skillful four-pentad lead prediction of rainfall over central India is demonstrated with the model using only large-scale circulation fields. A major strength of the model is that it can easily be used for real-time extended-range prediction of monsoons.

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