Tropical Cyclone Intensity Forecasting with Three Multiple Linear Regression Models and Random Forest Classification

The Statistical Hurricane Intensity Prediction Scheme (SHIPS) is a multiple linear regression model for predicting tropical cyclone (TC) intensity. It has been widely used in operational centers because of multiple advantages, including forecast stability, high accuracy, easy interpretation, and low computational cost. The Japan Meteorological Agency version of SHIPS is called the Typhoon Intensity Forecasting Scheme Based on SHIPS (TIFS) and predicts both maximum wind speed and central pressure. Although the addition of new predictors to SHIPS and TIFS has improved its accuracy, predicting TC intensity with a single regression model has limitations.

In this study, a new TIFS-based forecasting scheme (TIFS-RF) was developed, in which three TIFS models corresponding to the intensifying, steady-state, and weakening stages of TCs are introduced and in which the weighted mean of the three model forecasts based on random forest (RF) decision trees is computed as a final intensity forecast. Compared to conventional single TIFS models, TIFS-RF provided much better forecasts with an improvement rate of up to 13% at forecast times from 1 to 4 days. In particular, the improvement was significant for steady-state (SS) TCs, tropical depressions, and TCs undergoing extratropical transition within five days. The conventional single TIFS models tended to overforecast (i.e., show a positive bias for Vmax and a negative bias for Pmin) in SS cases. The TIFS-RF scheme could reduce the overforecast bias in SS cases by using the weighted mean of the three TIFS model forecasts based on RF decision trees. In contrast, TIFS-RF generally did not improve rapid intensification (RI) prediction, and its prediction accuracy for rapidly weakening cases was lower than that of single TIFS models. Even though nearly all decision trees of the RF model predicted intensification or weakening, the intensifying or weakening TIFS model failed to predict the actual large intensity changes. This result suggests a limitation of the linear regression model. However, the overall accuracy of TIFS-RF was much better than that of single TIFS models because the number of SS samples is much greater than the number of RI and rapidly weakening samples. One solution to compensate for the weaknesses of the TIFS-RF model is to use the consensus of the TIFS-RF and Hurricane Weather Research and Forecasting (HWRF) model forecasts. Here, the overall accuracy of the consensus was found to be better than that of either TIFS-RF or HWRF alone.