Sensitivity of Tropical Cyclone Intensity Variability to Different Stochastic Parameterization Methods - Patra, Mahashweta; Fan, Wai-Tong; Kieu, Chanh (Frontiers in Earth Science, 2022)
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 including random initial conditions, random parameters, and random forcing are used to study TC intensity variation and uncertainties. Using a fidelity-reduced dynamical model and a cloud-resolving model (CM1), 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. 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 as compared to randomizing model parameters every model time step, regardless of how large the random noise amplitude is. These results suggest the importance of choosing a random representation scheme to capture proper TC intensity variability in practical applications.