Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process

TL;DR

This paper introduces a novel environment-dependent hidden Markov model (MeHiM) for simulating tropical cyclone intensity evolution, incorporating environmental factors and improving prediction accuracy over previous models.

Contribution

The paper presents a new Markov environment-dependent hurricane intensity model that accounts for environmental variables and outperforms prior statistical models in simulating storm intensity changes.

Findings

Model accurately reproduces 6-h and 24-h intensity change distributions.

Significant improvement over previous statistical models.

Effective in simulating intensity evolution over ocean and land.

Abstract

A hidden Markov model is developed to simulate tropical cyclone intensity evolution dependent on the surrounding large-scale environment. The model considers three unobserved (hidden) discrete states of intensification and associates each state with a probability distribution of intensity change. The storm's transit from one state to another is described as a Markov chain. Both the intensity change and state transit components of the model are dependent on environmental variables including potential intensity, vertical wind shear, relative humidity, and ocean feedback. This Markov environment-dependent hurricane intensity model (MeHiM) is used to simulate the evolution of storm intensity along the storm track over the ocean, and a simple decay model is added to estimate the intensity change when the storm moves over land. Data for the North Atlantic (NA) basin from 1979-2014 (555 storms) are used for model development and evaluation. Probability distributions of 6-h and 24-h intensity change, lifetime maximum intensity, and landfall intensity based on model simulations and observations compare well. Although the MeHiM is still limited in fully describing rapid intensification, it shows a significant improvement over previous statistical models (e.g., linear, nonlinear, and finite mixture models).