A Logistic-growth-equation-based Intensity Prediction Scheme for Western North Pacific Tropical Cyclones

Abstract

Accurate prediction of tropical cyclone (TC) intensity remains challenging due to complex physical processes involved in TC intensity changes. A 7-day TC intensity prediction scheme based on the logistic growth equation (LGE) for the western North Pacific (WNP) has been developed using the observed and reanalysis data. 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 33 years of training samples, 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 a step-wise regression (SWR) method and a machine learning (ML) method for the period 19822014. Using the LGE-based scheme, a total of 80 TCs during 20152017 are used to make independent forecasts. Results show that the root mean square errors of the LGE-based scheme are much smaller than those of the official intensity forecasts from the China Meteorological Administration (CMA), especially for TCs in the coastal regions of East Asia. Moreover, the scheme based on the ML shows better forecast skills than that based on the SWR. The new prediction scheme also exhibits strong potential for rapid intensification and weakening forecasts and an extension of the CMA current 5-day forecasts to 7-day forecasts.