Predicting Distribution and Inter-Annual Variability of Tropical Cyclone Intensity from a Stochastic, Multiple-Linear Regression Model

We are working towards the development of a new statistical-dynamical downscaling system to study the influence of climate on tropical cyclones (TCs). The first step is development of an appropriate model for TC intensity as a function of environmental variables. We approach this issue with a stochastic model consisting of a multiple linear regression model (MLR) for 12-hour intensity forecasts as a deterministic component, and a random error generator as a stochastic component. Similar to the operational Statistical Hurricane Intensity Prediction Scheme (SHIPS), MLR relates the surrounding environment to storm intensity, but with only essential predictors calculated from monthly-mean NCEP reanalysis fields (potential intensity, shear, etc.) and from persistence. The deterministic MLR is developed with data from 1981-1999 and tested with data from 2000-2012 for the Atlantic, Eastern North Pacific, Western North Pacific, Indian Ocean, and Southern Hemisphere basins.

While the global MLR's skill is comparable to that of the operational statistical models (e.g., SHIPS), the distribution of the predicted maximum intensity from deterministic results has a systematic low bias compared to observations; the deterministic MLR creates almost no storms with intensities greater than 100 kt. The deterministic MLR can be significantly improved by adding the stochastic component, based on the distribution of random forecasting errors from the deterministic model compared to the training data. This stochastic component may be thought of as representing the component of TC intensification that is not linearly related to the environmental variables. We find that in order for the stochastic model to accurately capture the observed distribution of maximum storm intensities, the stochastic component must be auto-correlated across 12-hour time steps. This presentation also includes a detailed discussion of the distributions of other TC-intensity related quantities, as well as the inter-annual variability of predicted storm intensity in the form of accumulated cyclone energy (ACE). Applying this stochastic model in conjunction with global climate model fields is an ongoing task.