Tracking and predicting the northward propagating monsoon intraseasonal oscillations based on extended EOF analysis

The wet/dry spells of the Indian summer monsoon (ISM) rainfall are largely governed by the northward propagating boreal summer monsoon intraseasonal oscillations (MISO). Unlike for the Madden Julian Oscillation , a low dimensional real-time monitoring metric for the MISO is not currently available. Here, we present a real time monitoring index developed for identifying the amplitude and phase of the MISO over the ISM domain. The index is constructed by applying extended empirical orthogonal function (EEOF) analysis on daily unfiltered rainfall anomalies averaged over the longitudinal domain 60.5°E-95.5°E. The gravest two modes of the EEOFs together explain about 23 % of the total variance, similar to the variance explained by MISO in observation. The pair of first two principal components (PCs) of the EEOFs is named as MISO1 and MISO2 indices which together represent the evolution of the MISOs in a low dimensional phase space. Power spectral analysis reveals that the MISO indices neatly isolate the MISO signal from the higher frequency noise. It is found that the current amplitude and phase of the MISO can be estimated by preserving a memory of at least 15 days. Composite pictures of the spatio-temporal evolution of the MISOs over the ISM domain are brought out using the MISO indices. Since the MISO index does not rely on any sort of time filtering, it has great potential for real time monitoring of the MISO. A new statistical prediction scheme for the northward propagation of the MISO is developed based on the MISO indices and its skill is verified. The evolution of the MISO represented in the phase space constructed by the first two PCs of the EEOF analysis (MISO1 and MISO2) for four different JJAS seasons. The approximate locations of active phase of rainfall are denoted. A clockwise motion in the MISO phase space indicates northward propagation. The MISO indices are normalized with their corresponding standard deviation