At La Réunion RSMC, operational forecast errors computed during the 2001-2016 period for tropical systems over the southwest Indian Ocean (SWIO) indicate a mean position error of about 120 km at 24 h with a downward time trend. Operational intensity forecast errors are significantly larger for RI events at short lead times (10.8 m/s versus 4.9 m/s for non-RI events at 24 h); however, RI events (intensity increase >= 30 kt in 24 h) are characterized by significantly lower track errors at 12-, 24- and 48-h lead time than non-RI events, possibly due to a better location of the storm center at high intensities.
To offer further guidance to the SWIO practical intensity forecasts and better anticipate rapid intensity changes, the dominant large-scale factors governing the intensity changes of tropical systems in the SWIO have been identified. The predictors have been identified using both Era-Interim reanalysis fields at 0.75° latitude-longitude resolution and the final analysis of best-track data produced by La Réunion RSMC for all overwater storms of tropical characteristics from 1999 to 2016. As a key prerequisite, a proper SWIO threshold for rapid intensification has been defined at the 95% level of 24-h intensity change following the methodology used for Atlantic systems which enables basin inter-comparisons. Also, the statistical relationship linking the observed maximum intensity of a tropical system to the sea surface temperature (SST) has been examined to derive the first empirical MPI formula over the SWIO basin; it is tested as a potential predictor for TC intensity changes.
Based on the examination of a total of 26 potential predictors, statistical-dynamical tools of the same ilk as those developed in other basins have thus been designed to predict TC intensity change and/or the probability of RI at short range. Two tools will be presented: (i) a multiple linear regression model for TC intensity change at 24 h based on a multivariate adaptive regression splines (MARS) technique that models nonlinearities and interactions between variables, and (ii) a decision tree to anticipate RI periods in the next 24 h.