A feed forward neural network based on model output statistics for short-term hurricane intensity prediction

This deep learning model incorporates a real-time operational estimate of the current intensity and predictors derived from Hurricane Weather Research and Forecasting (HWRF; 2017 version) Model forecasts. The FFNN model is developed with the operational constraint of being restricted to 6-h-old HWRF data.

Best track intensity data are used for observational verification. The forecast training data are from 2014 to 2016 HWRF reforecast data and cover a wide variety of TCs from both the Atlantic and eastern Pacific Ocean basins. Cross validation shows that the FFNN increasingly outperforms the operational observation-adjusted HWRF (HWFI) in terms of mean absolute error (MAE) at forecast lead times from 3 to 57 h.