Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process
| Autor: | Renzhi Jing, Ning Lin |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Atmospheric Science 010504 meteorology & atmospheric sciences Meteorology Stochastic modelling FOS: Physical sciences Statistics - Applications 01 natural sciences Atmosphere Physics - Atmospheric and Oceanic Physics 010104 statistics & probability Hidden markov process Climatology Atmospheric and Oceanic Physics (physics.ao-ph) Environmental science Applications (stat.AP) 0101 mathematics Tropical cyclone Hidden Markov model Intensity (heat transfer) Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences |
| DOI: | 10.48550/arxiv.1808.05276 |
| Popis: | 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 storm intensity change 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 to 2014 (555 storms) are used for model development and evaluation. Probability distributions of 6- 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). |
| Databáze: | OpenAIRE |
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