Hybrid Neural Network Models for Postprocessing Medium-Range Forecasts of Tropical Cyclone Tracks over the Western North Pacific

Abstract

Abstract Tropical cyclone (TC) track forecasts derived from dynamical models inherit their errors. In this study, a neural network (NN) algorithm was proposed for postprocessing TC tracks predicted by the Global Ensemble Forecast System (GEFS) for lead times of 2, 4, 5, and 6 days over the western North Pacific. The hybrid NN is a combination of three NN classes: 1) convolutional NN that extracts spatial features from GEFS fields; 2) multilayer perceptron, which processes TC positions predicted by GEFS; and 3) recurrent NN that handles information from previous time steps. A dataset of 204 TCs (6744 samples), which were formed from 1985 to 2019 (June–October) and survived for at least six days, was separated into various track patterns. TCs in each track pattern were distributed uniformly to validation and test dataset, in which each contained 10% TCs of the entire dataset, and the remaining 80% were allocated to the training dataset. Two NN architectures were developed, with and without a shortcut connection. Feature selection and hyperparameter tuning were performed to improve model performance. The results present that mean track error and dispersion could be reduced, particularly with the shortcut connection, which also corrected the systematic speed and direction bias of GEFS. Although a reduction in mean track error was not achieved by the NNs for every forecast lead time, improvement can be foreseen upon calibration for reducing overfitting, and the performance encourages further development in the present application.