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.
