An analog method for real-time forecasting of summer monsoon subseasonal variability

Abstract

A physically based empirical real-time forecasting strategy to predict the subseasonal variations of the Indian summer monsoon up to four-five pentads (20-25 days) in advance has been developed. The method is based on the event-to-event similarity in the properties of monsoon intraseasonal oscillations (ISOs). This two-tier analog method is applied to NOAA outgoing longwave radiation (OLR) pentad averaged data that have sufficiently long records of observation and are available in nearly real time. High-frequency modes in the data are eliminated by reconstructing the data using the first 10 empirical orthogonal functions (EOFs), which together explain about 75% of the total variance. In the first level of the method, the spatial analogs of initial condition pattern are identified from the modeling data. The principal components (PCs) of these spatial analogs, whose evolution history of the latest five pentads matches that of the initial condition pattern, are considered the temporal PC analogs. Predictions are generated for each PC as the average evolution of PC analogs for the given lead time. Predicted OLR values are constructed using the EOFs and predicted PCs. OLR data for 1979-99 are used as the modeling data and independent hindcasts are generated for the period 2000-05. The skill of anomaly predictions is rather high over the central and northern Indian region for lead times of four-five pentads. The phases and amplitude of intraseasonal convective spells are predicted well, especially the long midseason break of 2002 that resulted in large-scale drought conditions. Skillful predictions can be made up to five pentads when started from an active initial state, whereas the limit of useful predictions is about two-three pentads when started from break initial conditions. An important feature of this method is that unlike some other empirical methods to forecast monsoon ISOs, it uses minimal time filtering to avoid any possible endpoint effects and hence may be readily used for real-time applications. Moreover, as the modeling data grow with time as a result of the increased number of observations, the number of analogs would also increase and eventually the quality of forecasts would improve.