A intensity prediction scheme of tropical cyclones (TCs) based on the logistic growth equation (LGE) for the western North Pacific (WNP) has been developed using the machine learning method called Long Short-Term Memory (LSTM). In the LGE, TC intensity change is determined by a growth term and a decaying term. These two terms comprise four free parameters, including the time-dependent growth rate and maximum potential intensity (MPI), and two constants. With training samples from the observation, optimal predictors are selected first, and then the two constants are determined based on the least square method, making the regressed growth rate from the optimal predictors as close to the observed as possible. The growth rate is further estimated based on LSTM for the period. Then, we transfer the learned features in the observations to perform further training for the Global Forecast System (GFS) forecast data in the past years. Finally, TC intensity forecasts are conducted based on the GFS environmental forecasts and CMA TC track forecasts.

LGEM-WNP Forecast Error for Typhoon Track in 2015-2017

Comparison of TC intensity Errors of LGEM-WNP and CMA Subjective Forecasts in 2015-2017