Statistical Modeling of Tropical Cyclone Intensity Climatology

The present study describes a new statistical-dynamical downscaling system for studying the influence of large-scale forcing on the climatology of tropical cyclone (TC) intensity. This system consists of a stochastically forced multiple linear regression model (MLR) for the 12-hour evolution of TC intensity. The deterministic MLR includes the effect of large-scale environment and storm characteristics estimated from best-track and monthly-averaged reanalysis datasets from 1981-1999 and validated on the period 2000-2012 for individual ocean basins. The distribution of the maximum lifetime intensity produced by the deterministic MLR has a systematic low bias compared to observations; the deterministic MLR creates almost no storms with intensities greater than 100 kt. In addition, the observed bimodal distribution of maximum lifetime intensity is missing from the deterministic results. The systematic low bias can be significantly reduced with the addition of stochastic forcing taken from the residuals of the MLR model. This stochastic forcing represents the component of TC intensification that is not linearly related to the environmental variables. A key feature of the stochastic forcing is its auto-correlation from one 12-hour time step to the next. The bimodal distribution of maximum lifetime intensity, which is related to rapid intensification, however, remains unresolved. The model produces too few cases of rapid intensification. Examining to what degree the bimodal distribution can be explained from a large-scale perspective is an ongoing task.