Multivariate Regression based Forecast Model for Intraseasonal Oscillation

Abstract

The prediction of ISO using two multivariate linear regression (MVR) models were analyzed using various indices, which captures the ISO signal, as predictors and convective and circulations fields as predicted depending on the index under consideration. The MVR model using OLR MJO Index(OMI) is showing relatively high prediction skill during boreal winter as well as boreal summer compared to other considered models and could be preferred for operational predict ion of ISO during both the summer and winter seasons. Comparison of prediction skill of ISO between j-model and t-model shows that t-model is showing better prediction skill as compared to j-model in most of the cases. Here, j-model is created based on the cross validation approach (Jiang et. al, 2008) which uses same dataset for training and validation whereas t-model keeps separate some data set to train the model and separate data set to validate the model. Prediction Skill of MVR model created using OMI and RMM index is relatively higher compared to that created using MISO index. The skill of ISO prediction remains sufficiently good for a lead time which is greater in boreal winter as compared to boreal summ er in case of all season ISO indices (such as OMI and RMM) and this could be because of the multiple propagation directions in case of boreal summer as compared to its winter counterpart which complicates the prediction of the same. A time-related correlation between the predicted and observed u200 anomaly data is found to be relatively high and coherent as compared to OLR and u850 field data in case of MVR model created using RMM.