Autoregressive Modeling for Tropical Cyclone Intensity Climatology

• Published in: Journal of climate 2016-11, Vol.29 (21), p.7815-7830
• Main Authors: Lee, Chia-Ying, Tippett, Michael K., Sobel, Adam H., Camargo, Suzana J.
• Format: Article
• Language: English
• Subjects: Amplification; Applied mathematics; Autoregressive models; Autoregressive processes; Basins; Climate change; Climate models; Climatology; Components; Computer simulation; Cyclones; Distribution; Humidity; Hurricanes; Loads (forces); Modelling; Observatories; Physics; Probability; Probability theory; Randomness; Regression analysis; Relative humidity; Spatial distribution; Storms; Tropical climate; Tropical cyclones; Vertical shear
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/JCLI-D-15-0909.1

An autoregressive model is developed to simulate the climatological distribution of global tropical cyclone (TC) intensity. The model consists of two components: a regression-based deterministic component that advances the TC intensity in time and depends on the storm state and surrounding large-scale environment and a stochastic forcing. Potential intensity, deep-layer mean vertical shear, and midlevel relative humidity are the environmental variables included in the deterministic component. Given a storm track and its environment, the model is initialized and then iterated along the track. Model performance is evaluated by its ability to represent the observed global and basin distributions of TC intensity as well as lifetime maximum intensity (LMI). The deterministic model alone captures the spatial features of the climatological TC intensity distribution but with intensities that remain below 100 kt (1 kt ≈ 0.51 m s−1). Addition of white (uncorrelated in time) stochastic forcingr reduces this bias by improving the simulated intensification rates and the frequency of major storms. The model simulates a realistic range of intensities, but the frequency of major storms remains too low in some basins.