Sensitivity of Tropical Cyclone Intensity Variability to Different Stochastic Parameterization Methods

Frontiers in Earth Science

Wai-Tong Fan¹  Chanh Kieu²  Mahashweta Patra² 

[1] Center of Mathematical Sciences and Applications, Harvard University, Cambridge, MA, United States;Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN, United States;Department of Mathematics, Indiana University, Bloomington, IN, United States;

关键词: tropical cyclone development;    stochastic parameterization;    intensity error growth;    intensity error saturation;    random representation;   

DOI  :  10.3389/feart.2022.893781

来源: DOAJ

【 摘 要 】

Proper representations of stochastic processes in tropical cyclone (TC) models are critical for capturing TC intensity variability in real-time applications. In this study, three different stochastic parameterization methods, namely, random initial conditions, random parameters, and random forcing, are used to examine TC intensity variation and uncertainties. It is shown that random forcing produces the largest variability of TC intensity at the maximum intensity equilibrium and the fastest intensity error growth during TC rapid intensification using a fidelity-reduced dynamical model and a cloud-resolving model (CM1). In contrast, the random initial condition tends to be more effective during the early stage of TC development but becomes less significant at the mature stage. For the random parameter method, it is found that this approach depends sensitively on how the model parameters are randomized. Specifically, randomizing model parameters at the initial time appears to produce much larger effects on TC intensity variability and error growth compared to randomizing model parameters every model time step, regardless of how large the random noise amplitude is. These results highlight the importance of choosing a random representation scheme to capture proper TC intensity variability in practical applications.

【 授权许可】

Unknown