Abstract
A statistical–dynamical model has been used for operational guidance for tropical cyclone (TC) intensity prediction. In this study, several multiple linear regression models and neural network (NN) models are developed for the intensity prediction of western North Pacific TCs at 24-, 48-, and 72-h intervals. The multiple linear regression models include a model of climatology and persistence (CLIPER), a model based on the Statistical Typhoon Intensity Prediction System (STIPS), which serves as the base regression model (BASE), and a model of STIPS with additional satellite estimates of surface evaporation (SLHF) and inner-core rain rate (IRR, STIPER model). A revised equation for the TC maximum potential intensity is derived using Tropical Rainfall Measuring Mission Microwave Imager optimally interpolated sea surface temperature data, which have higher temporal and spatial resolutions. Analyses of the resulting models show the marginal improvement of STIPER over BASE. However, IRR and SLHF are found to be significant predictors in the predictor pool. Neural network models using the same predictors as STIPER show reductions of the mean absolute errors of 7%, 11%, and 16% relative to STIPER for 24-, 48-, and 72-h forecasts, respectively. The largest improvement is found for the intensity forecasts of the rapidly intensifying and rapidly decaying TCs.
Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties
